Code.ipynb

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          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from sklearn.preprocessing import LabelEncoder, RobustScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "from google.colab import drive\n",
        "import torch.optim as optim\n",
        "from copy import deepcopy\n",
        "import torch.nn as nn\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import torch\n",
        "\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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        "id": "TBMG2pDZ8Tut",
        "outputId": "5bf1095f-c12b-4227-a304-d95d4ce95e9f",
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          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
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      "metadata": {
        "id": "AEqiA06e_Zrt"
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      "cell_type": "code",
      "source": [
        "df_food_trade = pd.read_csv('/content/drive/MyDrive/fao_data/food_trade.csv')\n",
        "\n",
        "df_food_trade"
      ],
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              "       Domain Code                        Domain  Area Code (M49)  \\\n",
              "0              TCL  Crops and livestock products                4   \n",
              "1              TCL  Crops and livestock products                4   \n",
              "2              TCL  Crops and livestock products                4   \n",
              "3              TCL  Crops and livestock products                4   \n",
              "4              TCL  Crops and livestock products                4   \n",
              "...            ...                           ...              ...   \n",
              "141733         TCL  Crops and livestock products              716   \n",
              "141734         TCL  Crops and livestock products              716   \n",
              "141735         TCL  Crops and livestock products              716   \n",
              "141736         TCL  Crops and livestock products              716   \n",
              "141737         TCL  Crops and livestock products              716   \n",
              "\n",
              "               Area  Element Code       Element Item Code (CPC)  \\\n",
              "0       Afghanistan          5622  Import Value           F1888   \n",
              "1       Afghanistan          5622  Import Value           F1888   \n",
              "2       Afghanistan          5622  Import Value           F1888   \n",
              "3       Afghanistan          5622  Import Value           F1888   \n",
              "4       Afghanistan          5622  Import Value           F1888   \n",
              "...             ...           ...           ...             ...   \n",
              "141733     Zimbabwe          5922  Export Value           F1896   \n",
              "141734     Zimbabwe          5622  Import Value           F1896   \n",
              "141735     Zimbabwe          5922  Export Value           F1896   \n",
              "141736     Zimbabwe          5622  Import Value           F1896   \n",
              "141737     Zimbabwe          5922  Export Value           F1896   \n",
              "\n",
              "                            Item  Year Code  Year      Unit      Value Flag  \\\n",
              "0       Cereals and Preparations       1991  1991  1000 USD   41600.00    A   \n",
              "1       Cereals and Preparations       1992  1992  1000 USD   25600.00    E   \n",
              "2       Cereals and Preparations       1993  1993  1000 USD   40000.00    E   \n",
              "3       Cereals and Preparations       1994  1994  1000 USD   25700.00    E   \n",
              "4       Cereals and Preparations       1995  1995  1000 USD   37720.00    E   \n",
              "...                          ...        ...   ...       ...        ...  ...   \n",
              "141733                   Tobacco       2020  2020  1000 USD  794956.99    A   \n",
              "141734                   Tobacco       2021  2021  1000 USD   18265.04    A   \n",
              "141735                   Tobacco       2021  2021  1000 USD  836533.69    A   \n",
              "141736                   Tobacco       2022  2022  1000 USD   27138.09    A   \n",
              "141737                   Tobacco       2022  2022  1000 USD  998057.60    A   \n",
              "\n",
              "       Flag Description  Note  \n",
              "0       Official figure   NaN  \n",
              "1       Estimated value   NaN  \n",
              "2       Estimated value   NaN  \n",
              "3       Estimated value   NaN  \n",
              "4       Estimated value   NaN  \n",
              "...                 ...   ...  \n",
              "141733  Official figure   NaN  \n",
              "141734  Official figure   NaN  \n",
              "141735  Official figure   NaN  \n",
              "141736  Official figure   NaN  \n",
              "141737  Official figure   NaN  \n",
              "\n",
              "[141738 rows x 15 columns]"
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              "      <td>TCL</td>\n",
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              "      <td>4</td>\n",
              "      <td>Afghanistan</td>\n",
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              "      <td>1992</td>\n",
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              "      <td>Cereals and Preparations</td>\n",
              "      <td>1994</td>\n",
              "      <td>1994</td>\n",
              "      <td>1000 USD</td>\n",
              "      <td>25700.00</td>\n",
              "      <td>E</td>\n",
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              "      <td>1995</td>\n",
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              "      <td>716</td>\n",
              "      <td>Zimbabwe</td>\n",
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              "      <td>A</td>\n",
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              "\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_food_trade"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_land_temperature = pd.read_csv('/content/drive/MyDrive/fao_data/land_temperature.csv')\n",
        "\n",
        "df_land_temperature"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 806
        },
        "id": "yV7Q2uv_oCgl",
        "outputId": "27826aa5-e393-4c20-ce59-8103dd63b413"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Domain Code                      Domain  Area Code (M49)         Area  \\\n",
              "0              ET  Temperature change on land                4  Afghanistan   \n",
              "1              ET  Temperature change on land                4  Afghanistan   \n",
              "2              ET  Temperature change on land                4  Afghanistan   \n",
              "3              ET  Temperature change on land                4  Afghanistan   \n",
              "4              ET  Temperature change on land                4  Afghanistan   \n",
              "...           ...                         ...              ...          ...   \n",
              "54805          ET  Temperature change on land              716     Zimbabwe   \n",
              "54806          ET  Temperature change on land              716     Zimbabwe   \n",
              "54807          ET  Temperature change on land              716     Zimbabwe   \n",
              "54808          ET  Temperature change on land              716     Zimbabwe   \n",
              "54809          ET  Temperature change on land              716     Zimbabwe   \n",
              "\n",
              "       Element Code             Element  Months Code               Months  \\\n",
              "0              7271  Temperature change         7016          Dec–Jan–Feb   \n",
              "1              7271  Temperature change         7016          Dec–Jan–Feb   \n",
              "2              7271  Temperature change         7016          Dec–Jan–Feb   \n",
              "3              7271  Temperature change         7016          Dec–Jan–Feb   \n",
              "4              7271  Temperature change         7016          Dec–Jan–Feb   \n",
              "...             ...                 ...          ...                  ...   \n",
              "54805          6078  Standard Deviation         7020  Meteorological year   \n",
              "54806          6078  Standard Deviation         7020  Meteorological year   \n",
              "54807          6078  Standard Deviation         7020  Meteorological year   \n",
              "54808          6078  Standard Deviation         7020  Meteorological year   \n",
              "54809          6078  Standard Deviation         7020  Meteorological year   \n",
              "\n",
              "       Year Code  Year Unit  Value Flag Flag Description  \n",
              "0           2000  2000   °c  0.618    E  Estimated value  \n",
              "1           2001  2001   °c  0.365    E  Estimated value  \n",
              "2           2002  2002   °c  1.655    E  Estimated value  \n",
              "3           2003  2003   °c  0.997    E  Estimated value  \n",
              "4           2004  2004   °c  1.883    E  Estimated value  \n",
              "...          ...   ...  ...    ...  ...              ...  \n",
              "54805       2018  2018   °c  0.311    E  Estimated value  \n",
              "54806       2019  2019   °c  0.311    E  Estimated value  \n",
              "54807       2020  2020   °c  0.311    E  Estimated value  \n",
              "54808       2021  2021   °c  0.311    E  Estimated value  \n",
              "54809       2022  2022   °c  0.311    E  Estimated value  \n",
              "\n",
              "[54810 rows x 14 columns]"
            ],
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              "      <th>4</th>\n",
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              "      <td>4</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>7271</td>\n",
              "      <td>Temperature change</td>\n",
              "      <td>7016</td>\n",
              "      <td>Dec–Jan–Feb</td>\n",
              "      <td>2004</td>\n",
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              "      <td>ET</td>\n",
              "      <td>Temperature change on land</td>\n",
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              "      <td>6078</td>\n",
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              "      <td>7020</td>\n",
              "      <td>Meteorological year</td>\n",
              "      <td>2018</td>\n",
              "      <td>2018</td>\n",
              "      <td>°c</td>\n",
              "      <td>0.311</td>\n",
              "      <td>E</td>\n",
              "      <td>Estimated value</td>\n",
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              "    <tr>\n",
              "      <th>54806</th>\n",
              "      <td>ET</td>\n",
              "      <td>Temperature change on land</td>\n",
              "      <td>716</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>6078</td>\n",
              "      <td>Standard Deviation</td>\n",
              "      <td>7020</td>\n",
              "      <td>Meteorological year</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>°c</td>\n",
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              "    <tr>\n",
              "      <th>54807</th>\n",
              "      <td>ET</td>\n",
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              "      <td>6078</td>\n",
              "      <td>Standard Deviation</td>\n",
              "      <td>7020</td>\n",
              "      <td>Meteorological year</td>\n",
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              "      <td>2020</td>\n",
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              "      <td>ET</td>\n",
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              "      <td>716</td>\n",
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              "      <td>7020</td>\n",
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              "      <td>E</td>\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "</div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_land_temperature",
              "summary": "{\n  \"name\": \"df_land_temperature\",\n  \"rows\": 54810,\n  \"fields\": [\n    {\n      \"column\": \"Domain Code\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"ET\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Domain\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Temperature change on land\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Area Code (M49)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 253,\n        \"min\": 4,\n        \"max\": 894,\n        \"num_unique_values\": 241,\n        \"samples\": [\n          68\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Area\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 241,\n        \"samples\": [\n          \"Bolivia (Plurinational State of)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Element Code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 596,\n        \"min\": 6078,\n        \"max\": 7271,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          6078\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Element\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Standard Deviation\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Months Code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 7016,\n        \"max\": 7020,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          7017\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Months\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Mar\\u2013Apr\\u2013May\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year Code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 2000,\n        \"max\": 2022,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 2000,\n        \"max\": 2022,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unit\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"\\u00b0c\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6696480733485192,\n        \"min\": -4.176,\n        \"max\": 8.2,\n        \"num_unique_values\": 3990,\n        \"samples\": [\n          0.974\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Flag\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"E\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Flag Description\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Estimated value\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_exchange_rate = pd.read_csv('/content/drive/MyDrive/fao_data/exchange_rate.csv')\n",
        "\n",
        "df_exchange_rate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "pzNrjVUBoNC4",
        "outputId": "60cd8769-0d7a-46b0-e719-0c25dbc6b12c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Domain Code          Domain  Area Code (M49)         Area  \\\n",
              "0               PE  Exchange rates                4  Afghanistan   \n",
              "1               PE  Exchange rates                4  Afghanistan   \n",
              "2               PE  Exchange rates                4  Afghanistan   \n",
              "3               PE  Exchange rates                4  Afghanistan   \n",
              "4               PE  Exchange rates                4  Afghanistan   \n",
              "...            ...             ...              ...          ...   \n",
              "103271          PE  Exchange rates              716     Zimbabwe   \n",
              "103272          PE  Exchange rates              716     Zimbabwe   \n",
              "103273          PE  Exchange rates              716     Zimbabwe   \n",
              "103274          PE  Exchange rates              716     Zimbabwe   \n",
              "103275          PE  Exchange rates              716     Zimbabwe   \n",
              "\n",
              "       ISO Currency Code (FAO)               Currency Element Code  \\\n",
              "0                          AFA                Afghani          LCU   \n",
              "1                          AFA                Afghani          LCU   \n",
              "2                          AFA                Afghani          LCU   \n",
              "3                          AFA                Afghani          LCU   \n",
              "4                          AFA                Afghani          LCU   \n",
              "...                        ...                    ...          ...   \n",
              "103271                     ZWD  Zimbabwe Dollar (old)          LCU   \n",
              "103272                     ZWD  Zimbabwe Dollar (old)          LCU   \n",
              "103273                     ZWD  Zimbabwe Dollar (old)          LCU   \n",
              "103274                     ZWD  Zimbabwe Dollar (old)          LCU   \n",
              "103275                     ZWD  Zimbabwe Dollar (old)          LCU   \n",
              "\n",
              "                             Element  Year Code  Year  Months Code     Months  \\\n",
              "0       Local currency units per USD       1980  1980         7001    January   \n",
              "1       Local currency units per USD       1980  1980         7002   February   \n",
              "2       Local currency units per USD       1980  1980         7003      March   \n",
              "3       Local currency units per USD       1980  1980         7004      April   \n",
              "4       Local currency units per USD       1980  1980         7005        May   \n",
              "...                              ...        ...   ...          ...        ...   \n",
              "103271  Local currency units per USD       2022  2022         7009  September   \n",
              "103272  Local currency units per USD       2022  2022         7010    October   \n",
              "103273  Local currency units per USD       2023  2023         7004      April   \n",
              "103274  Local currency units per USD       2023  2023         7005        May   \n",
              "103275  Local currency units per USD       2023  2023         7006       June   \n",
              "\n",
              "        Unit        Value Flag                         Flag Description  \n",
              "0        NaN    44.129167    X  Figure from international organizations  \n",
              "1        NaN    44.129167    X  Figure from international organizations  \n",
              "2        NaN    44.129167    X  Figure from international organizations  \n",
              "3        NaN    44.129167    X  Figure from international organizations  \n",
              "4        NaN    44.129167    X  Figure from international organizations  \n",
              "...      ...          ...  ...                                      ...  \n",
              "103271   NaN   597.970000    X  Figure from international organizations  \n",
              "103272   NaN   628.716452    X  Figure from international organizations  \n",
              "103273   NaN   981.370229    X  Figure from international organizations  \n",
              "103274   NaN  1439.613438    X  Figure from international organizations  \n",
              "103275   NaN  5482.310800    X  Figure from international organizations  \n",
              "\n",
              "[103276 rows x 16 columns]"
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              "      <th></th>\n",
              "      <th>Domain Code</th>\n",
              "      <th>Domain</th>\n",
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              "      <td>PE</td>\n",
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              "      <td>Afghanistan</td>\n",
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              "      <td>PE</td>\n",
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              "      <td>Afghanistan</td>\n",
              "      <td>AFA</td>\n",
              "      <td>Afghani</td>\n",
              "      <td>LCU</td>\n",
              "      <td>Local currency units per USD</td>\n",
              "      <td>1980</td>\n",
              "      <td>1980</td>\n",
              "      <td>7003</td>\n",
              "      <td>March</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>PE</td>\n",
              "      <td>Exchange rates</td>\n",
              "      <td>4</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFA</td>\n",
              "      <td>Afghani</td>\n",
              "      <td>LCU</td>\n",
              "      <td>Local currency units per USD</td>\n",
              "      <td>1980</td>\n",
              "      <td>1980</td>\n",
              "      <td>7004</td>\n",
              "      <td>April</td>\n",
              "      <td>NaN</td>\n",
              "      <td>44.129167</td>\n",
              "      <td>X</td>\n",
              "      <td>Figure from international organizations</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>PE</td>\n",
              "      <td>Exchange rates</td>\n",
              "      <td>4</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFA</td>\n",
              "      <td>Afghani</td>\n",
              "      <td>LCU</td>\n",
              "      <td>Local currency units per USD</td>\n",
              "      <td>1980</td>\n",
              "      <td>1980</td>\n",
              "      <td>7005</td>\n",
              "      <td>May</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>Figure from international organizations</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>103271</th>\n",
              "      <td>PE</td>\n",
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              "      <td>716</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWD</td>\n",
              "      <td>Zimbabwe Dollar (old)</td>\n",
              "      <td>LCU</td>\n",
              "      <td>Local currency units per USD</td>\n",
              "      <td>2022</td>\n",
              "      <td>2022</td>\n",
              "      <td>7009</td>\n",
              "      <td>September</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>Figure from international organizations</td>\n",
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              "      <th>103272</th>\n",
              "      <td>PE</td>\n",
              "      <td>Exchange rates</td>\n",
              "      <td>716</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWD</td>\n",
              "      <td>Zimbabwe Dollar (old)</td>\n",
              "      <td>LCU</td>\n",
              "      <td>Local currency units per USD</td>\n",
              "      <td>2022</td>\n",
              "      <td>2022</td>\n",
              "      <td>7010</td>\n",
              "      <td>October</td>\n",
              "      <td>NaN</td>\n",
              "      <td>628.716452</td>\n",
              "      <td>X</td>\n",
              "      <td>Figure from international organizations</td>\n",
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              "    <tr>\n",
              "      <th>103273</th>\n",
              "      <td>PE</td>\n",
              "      <td>Exchange rates</td>\n",
              "      <td>716</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWD</td>\n",
              "      <td>Zimbabwe Dollar (old)</td>\n",
              "      <td>LCU</td>\n",
              "      <td>Local currency units per USD</td>\n",
              "      <td>2023</td>\n",
              "      <td>2023</td>\n",
              "      <td>7004</td>\n",
              "      <td>April</td>\n",
              "      <td>NaN</td>\n",
              "      <td>981.370229</td>\n",
              "      <td>X</td>\n",
              "      <td>Figure from international organizations</td>\n",
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              "    <tr>\n",
              "      <th>103274</th>\n",
              "      <td>PE</td>\n",
              "      <td>Exchange rates</td>\n",
              "      <td>716</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWD</td>\n",
              "      <td>Zimbabwe Dollar (old)</td>\n",
              "      <td>LCU</td>\n",
              "      <td>Local currency units per USD</td>\n",
              "      <td>2023</td>\n",
              "      <td>2023</td>\n",
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              "      <td>May</td>\n",
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              "      <td>1439.613438</td>\n",
              "      <td>X</td>\n",
              "      <td>Figure from international organizations</td>\n",
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              "    <tr>\n",
              "      <th>103275</th>\n",
              "      <td>PE</td>\n",
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              "      <td>ZWD</td>\n",
              "      <td>Zimbabwe Dollar (old)</td>\n",
              "      <td>LCU</td>\n",
              "      <td>Local currency units per USD</td>\n",
              "      <td>2023</td>\n",
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              "      <td>X</td>\n",
              "      <td>Figure from international organizations</td>\n",
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              "<p>103276 rows × 16 columns</p>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c10b5c35-2253-4445-8fbc-a8725ed04a33\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c10b5c35-2253-4445-8fbc-a8725ed04a33')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c10b5c35-2253-4445-8fbc-a8725ed04a33 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_exchange_rate"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data preprocessing Section"
      ],
      "metadata": {
        "id": "4noeNXzKog10"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature Engineering"
      ],
      "metadata": {
        "id": "tojeOzOno0CA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculate average of avg exchange rate per year"
      ],
      "metadata": {
        "id": "92s_cui0opgF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "avg_exchange_rate_per_year = df_exchange_rate.groupby(['Year', 'Area'])['Value'].mean().reset_index()\n",
        "avg_exchange_rate_per_year = pd.DataFrame()._append(avg_exchange_rate_per_year, ignore_index=True)\n",
        "\n",
        "avg_exchange_rate_per_year"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "2m-zl_SsoaP1",
        "outputId": "f3391526-ecbc-4974-972c-dc9b17cbd559"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Year                 Area         Value\n",
              "0     1980          Afghanistan     44.129167\n",
              "1     1980              Algeria      3.837450\n",
              "2     1980               Angola     29.900000\n",
              "3     1980             Anguilla      2.700000\n",
              "4     1980  Antigua and Barbuda      2.700000\n",
              "...    ...                  ...           ...\n",
              "8634  2023             Viet Nam  23775.571818\n",
              "8635  2023                Yemen   1270.909046\n",
              "8636  2023               Zambia     19.799163\n",
              "8637  2023             Zimbabwe   2951.575984\n",
              "8638  2023        Åland Islands      0.924710\n",
              "\n",
              "[8639 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0bfb555e-6853-4425-ad41-994751a1f9bd\" class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Area</th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1980</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>44.129167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1980</td>\n",
              "      <td>Algeria</td>\n",
              "      <td>3.837450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1980</td>\n",
              "      <td>Angola</td>\n",
              "      <td>29.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1980</td>\n",
              "      <td>Anguilla</td>\n",
              "      <td>2.700000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1980</td>\n",
              "      <td>Antigua and Barbuda</td>\n",
              "      <td>2.700000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>8634</th>\n",
              "      <td>2023</td>\n",
              "      <td>Viet Nam</td>\n",
              "      <td>23775.571818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8635</th>\n",
              "      <td>2023</td>\n",
              "      <td>Yemen</td>\n",
              "      <td>1270.909046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8636</th>\n",
              "      <td>2023</td>\n",
              "      <td>Zambia</td>\n",
              "      <td>19.799163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8637</th>\n",
              "      <td>2023</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2951.575984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8638</th>\n",
              "      <td>2023</td>\n",
              "      <td>Åland Islands</td>\n",
              "      <td>0.924710</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8639 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0bfb555e-6853-4425-ad41-994751a1f9bd')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "      display:flex;\n",
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0bfb555e-6853-4425-ad41-994751a1f9bd button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0bfb555e-6853-4425-ad41-994751a1f9bd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5c52c51d-0f72-4cfa-8d2e-971b73f818a8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "avg_exchange_rate_per_year",
              "summary": "{\n  \"name\": \"avg_exchange_rate_per_year\",\n  \"rows\": 8639,\n  \"fields\": [\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 1980,\n        \"max\": 2023,\n        \"num_unique_values\": 44,\n        \"samples\": [\n          2017,\n          2004,\n          2005\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Area\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 219,\n        \"samples\": [\n          \"United Arab Emirates\",\n          \"Mali\",\n          \"Cura\\u00e7ao\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 72332706.23104945,\n        \"min\": 0.0006452906666666666,\n        \"max\": 6723052073.276047,\n        \"num_unique_values\": 5380,\n        \"samples\": [\n          5.06952,\n          8069.606236583334,\n          2.528783333333333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculate average of avg land temperature per year"
      ],
      "metadata": {
        "id": "ZVY0fqUzpWZP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "avg_temp_per_year = df_land_temperature.groupby(['Year', 'Area'])['Value'].mean().reset_index()\n",
        "avg_temp_per_year = pd.DataFrame()._append(avg_temp_per_year, ignore_index=True)\n",
        "\n",
        "avg_temp_per_year"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "CeIzJXwFpOOv",
        "outputId": "e17e3d7e-cbfd-43af-9264-2e3459d0f610"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Year                       Area   Value\n",
              "0     2000                Afghanistan  0.9128\n",
              "1     2000                    Albania  0.9002\n",
              "2     2000                    Algeria  0.6867\n",
              "3     2000             American Samoa  0.4704\n",
              "4     2000                    Andorra  0.8999\n",
              "...    ...                        ...     ...\n",
              "5476  2022  Wallis and Futuna Islands  0.6122\n",
              "5477  2022             Western Sahara  1.3425\n",
              "5478  2022                      Yemen     NaN\n",
              "5479  2022                     Zambia  0.5250\n",
              "5480  2022                   Zimbabwe -0.0091\n",
              "\n",
              "[5481 rows x 3 columns]"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "avg_temp_per_year",
              "summary": "{\n  \"name\": \"avg_temp_per_year\",\n  \"rows\": 5481,\n  \"fields\": [\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 2000,\n        \"max\": 2022,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2015,\n          2009,\n          2000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Area\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 241,\n        \"samples\": [\n          \"Bolivia (Plurinational State of)\",\n          \"Anguilla\",\n          \"United Kingdom of Great Britain and Northern Ireland\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4463323163876681,\n        \"min\": -0.3715,\n        \"max\": 3.6908000000000003,\n        \"num_unique_values\": 4378,\n        \"samples\": [\n          1.4268,\n          0.346,\n          0.5709000000000001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Purify df_food_trade"
      ],
      "metadata": {
        "id": "Rgg2YoEHpfrK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_food_trade = df_food_trade[df_food_trade['Element Code'] == 5622]\n",
        "\n",
        "df_food_trade.drop(labels=['Domain Code', 'Domain', 'Item Code (CPC)', 'Area Code (M49)', 'Element Code',\n",
        "                           'Element', 'Year Code', 'Unit', 'Flag', 'Flag Description', 'Note'],\n",
        "                   axis=1, inplace=True)\n",
        "\n",
        "df_food_trade"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "id": "iN_1ieKapk3I",
        "outputId": "882fadc0-d504-42e6-f903-9add3f54e2c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-8c384f51fed0>:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_food_trade.drop(labels=['Domain Code', 'Domain', 'Item Code (CPC)', 'Area Code (M49)', 'Element Code',\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Area                      Item  Year     Value\n",
              "0       Afghanistan  Cereals and Preparations  1991  41600.00\n",
              "1       Afghanistan  Cereals and Preparations  1992  25600.00\n",
              "2       Afghanistan  Cereals and Preparations  1993  40000.00\n",
              "3       Afghanistan  Cereals and Preparations  1994  25700.00\n",
              "4       Afghanistan  Cereals and Preparations  1995  37720.00\n",
              "...             ...                       ...   ...       ...\n",
              "141728     Zimbabwe                   Tobacco  2018  24977.84\n",
              "141730     Zimbabwe                   Tobacco  2019   7215.50\n",
              "141732     Zimbabwe                   Tobacco  2020  18370.85\n",
              "141734     Zimbabwe                   Tobacco  2021  18265.04\n",
              "141736     Zimbabwe                   Tobacco  2022  27138.09\n",
              "\n",
              "[73914 rows x 4 columns]"
            ],
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              "      <td>Afghanistan</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Afghanistan</td>\n",
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              "      <th>141728</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>141730</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>Tobacco</td>\n",
              "      <td>2019</td>\n",
              "      <td>7215.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141732</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>Tobacco</td>\n",
              "      <td>2020</td>\n",
              "      <td>18370.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141734</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>Tobacco</td>\n",
              "      <td>2021</td>\n",
              "      <td>18265.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141736</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>Tobacco</td>\n",
              "      <td>2022</td>\n",
              "      <td>27138.09</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_food_trade",
              "summary": "{\n  \"name\": \"df_food_trade\",\n  \"rows\": 73914,\n  \"fields\": [\n    {\n      \"column\": \"Area\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 203,\n        \"samples\": [\n          \"Belgium\",\n          \"Azerbaijan\",\n          \"Mauritania\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Item\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"Non-edible Fats and Oils\",\n          \"Non-food\",\n          \"Cereals and Preparations\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9,\n        \"min\": 1991,\n        \"max\": 2022,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          2020,\n          2006,\n          2015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1888520.6881238678,\n        \"min\": 0.0,\n        \"max\": 83558058.24,\n        \"num_unique_values\": 60162,\n        \"samples\": [\n          36155.3,\n          272037.0,\n          7999.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Concatenate dataframes based on Year and Area\n",
        "## Rename columns with same names"
      ],
      "metadata": {
        "id": "Alz_jSXGpzOd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_food_trade.rename(columns={\"Item\": \"Products\", \"Value\": \"Export Value\"}, inplace=True)\n",
        "\n",
        "avg_exchange_rate_per_year.rename(columns={\"Value\": \"Exchange Rate\"}, inplace=True)\n",
        "\n",
        "avg_temp_per_year.rename(columns={\"Value\": \"Land Temperature\"}, inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EhQo_q9ap4bw",
        "outputId": "03462428-99ce-45ae-b084-45a1438d27d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-10-69e67b26d6db>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_food_trade.rename(columns={\"Item\": \"Products\", \"Value\": \"Export Value\"}, inplace=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Food Trade == ', df_food_trade.columns)\n",
        "print('Exchange == ', avg_exchange_rate_per_year.columns)\n",
        "print('Temp == ', avg_temp_per_year.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h1hCuOSPqFpH",
        "outputId": "16e78574-1b89-4a96-9ed0-2f6f4b93c503"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Food Trade ==  Index(['Area', 'Products', 'Year', 'Export Value'], dtype='object')\n",
            "Exchange ==  Index(['Year', 'Area', 'Exchange Rate'], dtype='object')\n",
            "Temp ==  Index(['Year', 'Area', 'Land Temperature'], dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Concatenate dataframes\n"
      ],
      "metadata": {
        "id": "Gy5zmLOSqTpe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.merge(df_food_trade, avg_exchange_rate_per_year, on=['Year', 'Area'], how='left')\n",
        "df = pd.merge(df, avg_temp_per_year, on=['Year', 'Area'], how='left')\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "kQxCnwE0qZd6",
        "outputId": "f3893c38-4d13-44cd-96be-84534afd6767"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Area                  Products  Year  Export Value  \\\n",
              "0      Afghanistan  Cereals and Preparations  1991      41600.00   \n",
              "1      Afghanistan  Cereals and Preparations  1992      25600.00   \n",
              "2      Afghanistan  Cereals and Preparations  1993      40000.00   \n",
              "3      Afghanistan  Cereals and Preparations  1994      25700.00   \n",
              "4      Afghanistan  Cereals and Preparations  1995      37720.00   \n",
              "...            ...                       ...   ...           ...   \n",
              "73909     Zimbabwe                   Tobacco  2018      24977.84   \n",
              "73910     Zimbabwe                   Tobacco  2019       7215.50   \n",
              "73911     Zimbabwe                   Tobacco  2020      18370.85   \n",
              "73912     Zimbabwe                   Tobacco  2021      18265.04   \n",
              "73913     Zimbabwe                   Tobacco  2022      27138.09   \n",
              "\n",
              "       Exchange Rate  Land Temperature  \n",
              "0          50.599605               NaN  \n",
              "1          50.599605               NaN  \n",
              "2          50.599605               NaN  \n",
              "3         425.099934               NaN  \n",
              "4         833.333333               NaN  \n",
              "...              ...               ...  \n",
              "73909     322.206265            0.4625  \n",
              "73910      16.923764            0.6983  \n",
              "73911      51.329013            0.4306  \n",
              "73912      88.552447            0.1736  \n",
              "73913     349.518396           -0.0091  \n",
              "\n",
              "[73914 rows x 6 columns]"
            ],
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              "\n",
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              "    <div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Area</th>\n",
              "      <th>Products</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Afghanistan</td>\n",
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              "      <th>2</th>\n",
              "      <td>Afghanistan</td>\n",
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              "      <td>40000.00</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>Cereals and Preparations</td>\n",
              "      <td>1994</td>\n",
              "      <td>25700.00</td>\n",
              "      <td>425.099934</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Afghanistan</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>73909</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>Tobacco</td>\n",
              "      <td>2018</td>\n",
              "      <td>24977.84</td>\n",
              "      <td>322.206265</td>\n",
              "      <td>0.4625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73910</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>Tobacco</td>\n",
              "      <td>2019</td>\n",
              "      <td>7215.50</td>\n",
              "      <td>16.923764</td>\n",
              "      <td>0.6983</td>\n",
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              "    <tr>\n",
              "      <th>73911</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>Tobacco</td>\n",
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              "      <td>18370.85</td>\n",
              "      <td>51.329013</td>\n",
              "      <td>0.4306</td>\n",
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              "      <th>73912</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>Tobacco</td>\n",
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              "      <td>18265.04</td>\n",
              "      <td>88.552447</td>\n",
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              "      <td>349.518396</td>\n",
              "      <td>-0.0091</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>73914 rows × 6 columns</p>\n",
              "</div>\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c2f8f327-a7d9-4ee7-8ca7-174ea2eb7b73 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c2f8f327-a7d9-4ee7-8ca7-174ea2eb7b73');\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
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              "            title=\"Suggest charts\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0b3a87bd-5a51-40e1-80e2-a430a333ad0a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 73914,\n  \"fields\": [\n    {\n      \"column\": \"Area\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 203,\n        \"samples\": [\n          \"Belgium\",\n          \"Azerbaijan\",\n          \"Mauritania\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Products\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"Non-edible Fats and Oils\",\n          \"Non-food\",\n          \"Cereals and Preparations\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9,\n        \"min\": 1991,\n        \"max\": 2022,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          2020,\n          2006,\n          2015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Export Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1888520.6881238678,\n        \"min\": 0.0,\n        \"max\": 83558058.24,\n        \"num_unique_values\": 60162,\n        \"samples\": [\n          36155.3,\n          272037.0,\n          7999.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Exchange Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 88584609.6560519,\n        \"min\": 0.0036254886666666664,\n        \"max\": 6723052073.276047,\n        \"num_unique_values\": 4017,\n        \"samples\": [\n          919.56590735,\n          15.35,\n          57.75199666666666\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Land Temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.448991085763215,\n        \"min\": -0.3715,\n        \"max\": 3.6908000000000003,\n        \"num_unique_values\": 3735,\n        \"samples\": [\n          0.861,\n          1.2544,\n          2.4752\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Preprocessing\n",
        "Encoding categorical data"
      ],
      "metadata": {
        "id": "EbJxePEPqqW8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "encoder_1 = LabelEncoder()\n",
        "df['Area'] = encoder_1.fit_transform(df['Area'])\n",
        "encoder_2 = LabelEncoder()\n",
        "df['Products'] = encoder_2.fit_transform(df['Products'])\n",
        "encoder_3 = LabelEncoder()\n",
        "df['Year'] = encoder_3.fit_transform(df['Year'])\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "r0gbN0Ijq4Pq",
        "outputId": "d30c2b21-773e-4da7-f014-fe874f1e0213"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Area  Products  Year  Export Value  Exchange Rate  Land Temperature\n",
              "0         0         1     0      41600.00      50.599605               NaN\n",
              "1         0         1     1      25600.00      50.599605               NaN\n",
              "2         0         1     2      40000.00      50.599605               NaN\n",
              "3         0         1     3      25700.00     425.099934               NaN\n",
              "4         0         1     4      37720.00     833.333333               NaN\n",
              "...     ...       ...   ...           ...            ...               ...\n",
              "73909   202        11    27      24977.84     322.206265            0.4625\n",
              "73910   202        11    28       7215.50      16.923764            0.6983\n",
              "73911   202        11    29      18370.85      51.329013            0.4306\n",
              "73912   202        11    30      18265.04      88.552447            0.1736\n",
              "73913   202        11    31      27138.09     349.518396           -0.0091\n",
              "\n",
              "[73914 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f7125615-1836-47eb-a315-69b76cd3aa47\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Area</th>\n",
              "      <th>Products</th>\n",
              "      <th>Year</th>\n",
              "      <th>Export Value</th>\n",
              "      <th>Exchange Rate</th>\n",
              "      <th>Land Temperature</th>\n",
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              "      <th>0</th>\n",
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              "      <td>41600.00</td>\n",
              "      <td>50.599605</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
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              "      <td>25600.00</td>\n",
              "      <td>50.599605</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>40000.00</td>\n",
              "      <td>50.599605</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>25700.00</td>\n",
              "      <td>425.099934</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>37720.00</td>\n",
              "      <td>833.333333</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73909</th>\n",
              "      <td>202</td>\n",
              "      <td>11</td>\n",
              "      <td>27</td>\n",
              "      <td>24977.84</td>\n",
              "      <td>322.206265</td>\n",
              "      <td>0.4625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73910</th>\n",
              "      <td>202</td>\n",
              "      <td>11</td>\n",
              "      <td>28</td>\n",
              "      <td>7215.50</td>\n",
              "      <td>16.923764</td>\n",
              "      <td>0.6983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73911</th>\n",
              "      <td>202</td>\n",
              "      <td>11</td>\n",
              "      <td>29</td>\n",
              "      <td>18370.85</td>\n",
              "      <td>51.329013</td>\n",
              "      <td>0.4306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73912</th>\n",
              "      <td>202</td>\n",
              "      <td>11</td>\n",
              "      <td>30</td>\n",
              "      <td>18265.04</td>\n",
              "      <td>88.552447</td>\n",
              "      <td>0.1736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73913</th>\n",
              "      <td>202</td>\n",
              "      <td>11</td>\n",
              "      <td>31</td>\n",
              "      <td>27138.09</td>\n",
              "      <td>349.518396</td>\n",
              "      <td>-0.0091</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>73914 rows × 6 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f7125615-1836-47eb-a315-69b76cd3aa47')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f7125615-1836-47eb-a315-69b76cd3aa47 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f7125615-1836-47eb-a315-69b76cd3aa47');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8b84a317-af56-4240-a50c-2bffeede1c90\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8b84a317-af56-4240-a50c-2bffeede1c90')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 73914,\n  \"fields\": [\n    {\n      \"column\": \"Area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 58,\n        \"min\": 0,\n        \"max\": 202,\n        \"num_unique_values\": 203,\n        \"samples\": [\n          15,\n          9,\n          115\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Products\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 11,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          7,\n          8,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9,\n        \"min\": 0,\n        \"max\": 31,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          29,\n          15,\n          24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Export Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1888520.6881238678,\n        \"min\": 0.0,\n        \"max\": 83558058.24,\n        \"num_unique_values\": 60162,\n        \"samples\": [\n          36155.3,\n          272037.0,\n          7999.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Exchange Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 88584609.6560519,\n        \"min\": 0.0036254886666666664,\n        \"max\": 6723052073.276047,\n        \"num_unique_values\": 4017,\n        \"samples\": [\n          919.56590735,\n          15.35,\n          57.75199666666666\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Land Temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.448991085763215,\n        \"min\": -0.3715,\n        \"max\": 3.6908000000000003,\n        \"num_unique_values\": 3735,\n        \"samples\": [\n          0.861,\n          1.2544,\n          2.4752\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Shifting Export Value for three years"
      ],
      "metadata": {
        "id": "6_IWrt1jq8sw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def apply_shifts(group):\n",
        "    group['Export Value Lag1'] = group['Export Value'].shift(1)\n",
        "    group['Export Value Lag2'] = group['Export Value'].shift(2)\n",
        "    group['Export Value Lag3'] = group['Export Value'].shift(3)\n",
        "    return group\n",
        "\n",
        "\n",
        "df = df.groupby('Area').apply(apply_shifts).reset_index(drop=True)\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "8X2eE8lNrLlX",
        "outputId": "ac48f1aa-1eec-4dc4-df2b-9bf13320092d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Area  Products  Year  Export Value  Exchange Rate  Land Temperature  \\\n",
              "0         0         1     0      41600.00      50.599605               NaN   \n",
              "1         0         1     1      25600.00      50.599605               NaN   \n",
              "2         0         1     2      40000.00      50.599605               NaN   \n",
              "3         0         1     3      25700.00     425.099934               NaN   \n",
              "4         0         1     4      37720.00     833.333333               NaN   \n",
              "...     ...       ...   ...           ...            ...               ...   \n",
              "73909   202        11    27      24977.84     322.206265            0.4625   \n",
              "73910   202        11    28       7215.50      16.923764            0.6983   \n",
              "73911   202        11    29      18370.85      51.329013            0.4306   \n",
              "73912   202        11    30      18265.04      88.552447            0.1736   \n",
              "73913   202        11    31      27138.09     349.518396           -0.0091   \n",
              "\n",
              "       Export Value Lag1  Export Value Lag2  Export Value Lag3  \n",
              "0                    NaN                NaN                NaN  \n",
              "1               41600.00                NaN                NaN  \n",
              "2               25600.00           41600.00                NaN  \n",
              "3               40000.00           25600.00           41600.00  \n",
              "4               25700.00           40000.00           25600.00  \n",
              "...                  ...                ...                ...  \n",
              "73909           19845.61           15335.59            8382.56  \n",
              "73910           24977.84           19845.61           15335.59  \n",
              "73911            7215.50           24977.84           19845.61  \n",
              "73912           18370.85            7215.50           24977.84  \n",
              "73913           18265.04           18370.85            7215.50  \n",
              "\n",
              "[73914 rows x 9 columns]"
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              "<p>73914 rows × 9 columns</p>\n",
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              "        document.querySelector('#df-ec37afef-302e-4fd0-ad0f-19a4ee319c17 button.colab-df-convert');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-94d7be9f-52bd-4ccd-9432-52f290239667 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 73914,\n  \"fields\": [\n    {\n      \"column\": \"Area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 58,\n        \"min\": 0,\n        \"max\": 202,\n        \"num_unique_values\": 203,\n        \"samples\": [\n          15,\n          9,\n          115\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Products\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 11,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          7,\n          8,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9,\n        \"min\": 0,\n        \"max\": 31,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          29,\n          15,\n          24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Export Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1888520.6881238678,\n        \"min\": 0.0,\n        \"max\": 83558058.24,\n        \"num_unique_values\": 60162,\n        \"samples\": [\n          36155.3,\n          272037.0,\n          7999.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Exchange Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 88584609.6560519,\n        \"min\": 0.0036254886666666664,\n        \"max\": 6723052073.276047,\n        \"num_unique_values\": 4017,\n        \"samples\": [\n          919.56590735,\n          15.35,\n          57.75199666666666\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Land Temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.448991085763215,\n        \"min\": -0.3715,\n        \"max\": 3.6908000000000003,\n        \"num_unique_values\": 3735,\n        \"samples\": [\n          0.861,\n          1.2544,\n          2.4752\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Export Value Lag1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1890926.8777146554,\n        \"min\": 0.0,\n        \"max\": 83558058.24,\n        \"num_unique_values\": 59962,\n        \"samples\": [\n          2376197.0,\n          56270.39,\n          412862.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Export Value Lag2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1893242.8692607237,\n        \"min\": 0.0,\n        \"max\": 83558058.24,\n        \"num_unique_values\": 59765,\n        \"samples\": [\n          32633.95,\n          343436.0,\n          1068506.97\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Export Value Lag3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1895580.260423162,\n        \"min\": 0.0,\n        \"max\": 83558058.24,\n        \"num_unique_values\": 59566,\n        \"samples\": [\n          12812498.35,\n          338388.78,\n          28986.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Normalization"
      ],
      "metadata": {
        "id": "-VUpOaLZrepJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Drop Null value"
      ],
      "metadata": {
        "id": "NrWV22-uryue"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.fillna(0, inplace=True)\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Xej3fQKzrhii",
        "outputId": "7cb7f4b3-5029-45ef-f7f9-27b973611fc2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Area  Products  Year  Export Value  Exchange Rate  Land Temperature  \\\n",
              "0         0         1     0      41600.00      50.599605            0.0000   \n",
              "1         0         1     1      25600.00      50.599605            0.0000   \n",
              "2         0         1     2      40000.00      50.599605            0.0000   \n",
              "3         0         1     3      25700.00     425.099934            0.0000   \n",
              "4         0         1     4      37720.00     833.333333            0.0000   \n",
              "...     ...       ...   ...           ...            ...               ...   \n",
              "73909   202        11    27      24977.84     322.206265            0.4625   \n",
              "73910   202        11    28       7215.50      16.923764            0.6983   \n",
              "73911   202        11    29      18370.85      51.329013            0.4306   \n",
              "73912   202        11    30      18265.04      88.552447            0.1736   \n",
              "73913   202        11    31      27138.09     349.518396           -0.0091   \n",
              "\n",
              "       Export Value Lag1  Export Value Lag2  Export Value Lag3  \n",
              "0                   0.00               0.00               0.00  \n",
              "1               41600.00               0.00               0.00  \n",
              "2               25600.00           41600.00               0.00  \n",
              "3               40000.00           25600.00           41600.00  \n",
              "4               25700.00           40000.00           25600.00  \n",
              "...                  ...                ...                ...  \n",
              "73909           19845.61           15335.59            8382.56  \n",
              "73910           24977.84           19845.61           15335.59  \n",
              "73911            7215.50           24977.84           19845.61  \n",
              "73912           18370.85            7215.50           24977.84  \n",
              "73913           18265.04           18370.85            7215.50  \n",
              "\n",
              "[73914 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c56d2e6b-a5ec-445a-9f36-d8e4dabee190\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Area</th>\n",
              "      <th>Products</th>\n",
              "      <th>Year</th>\n",
              "      <th>Export Value</th>\n",
              "      <th>Exchange Rate</th>\n",
              "      <th>Land Temperature</th>\n",
              "      <th>Export Value Lag1</th>\n",
              "      <th>Export Value Lag2</th>\n",
              "      <th>Export Value Lag3</th>\n",
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              "      <td>41600.00</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
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              "      <td>25700.00</td>\n",
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              "      <td>25600.00</td>\n",
              "      <td>41600.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
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              "      <td>4</td>\n",
              "      <td>37720.00</td>\n",
              "      <td>833.333333</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>25700.00</td>\n",
              "      <td>40000.00</td>\n",
              "      <td>25600.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>73909</th>\n",
              "      <td>202</td>\n",
              "      <td>11</td>\n",
              "      <td>27</td>\n",
              "      <td>24977.84</td>\n",
              "      <td>322.206265</td>\n",
              "      <td>0.4625</td>\n",
              "      <td>19845.61</td>\n",
              "      <td>15335.59</td>\n",
              "      <td>8382.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73910</th>\n",
              "      <td>202</td>\n",
              "      <td>11</td>\n",
              "      <td>28</td>\n",
              "      <td>7215.50</td>\n",
              "      <td>16.923764</td>\n",
              "      <td>0.6983</td>\n",
              "      <td>24977.84</td>\n",
              "      <td>19845.61</td>\n",
              "      <td>15335.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73911</th>\n",
              "      <td>202</td>\n",
              "      <td>11</td>\n",
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              "      <td>18370.85</td>\n",
              "      <td>51.329013</td>\n",
              "      <td>0.4306</td>\n",
              "      <td>7215.50</td>\n",
              "      <td>24977.84</td>\n",
              "      <td>19845.61</td>\n",
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              "    <tr>\n",
              "      <th>73912</th>\n",
              "      <td>202</td>\n",
              "      <td>11</td>\n",
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              "      <td>18265.04</td>\n",
              "      <td>88.552447</td>\n",
              "      <td>0.1736</td>\n",
              "      <td>18370.85</td>\n",
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              "      <td>24977.84</td>\n",
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              "      <th>73913</th>\n",
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              "      <td>27138.09</td>\n",
              "      <td>349.518396</td>\n",
              "      <td>-0.0091</td>\n",
              "      <td>18265.04</td>\n",
              "      <td>18370.85</td>\n",
              "      <td>7215.50</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>73914 rows × 9 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c56d2e6b-a5ec-445a-9f36-d8e4dabee190')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c56d2e6b-a5ec-445a-9f36-d8e4dabee190 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c56d2e6b-a5ec-445a-9f36-d8e4dabee190');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
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              "      --hover-fill-color: #174EA6;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7c533374-bc97-4b05-a99d-de648d0540cd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 73914,\n  \"fields\": [\n    {\n      \"column\": \"Area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 58,\n        \"min\": 0,\n        \"max\": 202,\n        \"num_unique_values\": 203,\n        \"samples\": [\n          15,\n          9,\n          115\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Products\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 11,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          7,\n          8,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9,\n        \"min\": 0,\n        \"max\": 31,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          29,\n          15,\n          24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Export Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1888520.6881238678,\n        \"min\": 0.0,\n        \"max\": 83558058.24,\n        \"num_unique_values\": 60162,\n        \"samples\": [\n          36155.3,\n          272037.0,\n          7999.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Exchange Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 85656701.6883728,\n        \"min\": 0.0,\n        \"max\": 6723052073.276047,\n        \"num_unique_values\": 4018,\n        \"samples\": [\n          919.7550166666666,\n          15.375,\n          59.72378166666667\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Land Temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5431848264956592,\n        \"min\": -0.3715,\n        \"max\": 3.6908000000000003,\n        \"num_unique_values\": 3736,\n        \"samples\": [\n          0.861,\n          0.8012,\n          0.8586\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Export Value Lag1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1888480.142647381,\n        \"min\": 0.0,\n        \"max\": 83558058.24,\n        \"num_unique_values\": 59962,\n        \"samples\": [\n          2376197.0,\n          56270.39,\n          412862.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Export Value Lag2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1888339.403126502,\n        \"min\": 0.0,\n        \"max\": 83558058.24,\n        \"num_unique_values\": 59765,\n        \"samples\": [\n          32633.95,\n          343436.0,\n          1068506.97\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Export Value Lag3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1888209.9125935743,\n        \"min\": 0.0,\n        \"max\": 83558058.24,\n        \"num_unique_values\": 59566,\n        \"samples\": [\n          12812498.35,\n          338388.78,\n          28986.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "features = ['Year', 'Area', 'Products', 'Exchange Rate', 'Land Temperature',\n",
        "            'Export Value Lag1', 'Export Value Lag2', 'Export Value', 'Export Value Lag3']\n",
        "\n",
        "normalized_features = ['Exchange Rate', 'Land Temperature', 'Export Value', 'Export Value Lag1', 'Export Value Lag2',\n",
        "                       'Export Value Lag3']\n",
        "\n",
        "scaler = RobustScaler()\n",
        "df[normalized_features] = scaler.fit_transform(df[normalized_features])\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "0Frh4QDtr2wx",
        "outputId": "15aac5f6-f65e-46e2-d163-e59dde20044a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Area  Products  Year  Export Value  Exchange Rate  Land Temperature  \\\n",
              "0         0         1     0      0.024447       0.234375         -0.667761   \n",
              "1         0         1     1     -0.055376       0.234375         -0.667761   \n",
              "2         0         1     2      0.016465       0.234375         -0.667761   \n",
              "3         0         1     3     -0.054877       2.360152         -0.667761   \n",
              "4         0         1     4      0.005090       4.677407         -0.667761   \n",
              "...     ...       ...   ...           ...            ...               ...   \n",
              "73909   202        11    27     -0.058480       1.776096         -0.144038   \n",
              "73910   202        11    28     -0.147095       0.043221          0.122976   \n",
              "73911   202        11    29     -0.091442       0.238515         -0.180161   \n",
              "73912   202        11    30     -0.091970       0.449807         -0.471181   \n",
              "73913   202        11    31     -0.047703       1.931128         -0.678066   \n",
              "\n",
              "       Export Value Lag1  Export Value Lag2  Export Value Lag3  \n",
              "0              -0.181886          -0.181107          -0.180283  \n",
              "1               0.026686          -0.181107          -0.180283  \n",
              "2              -0.053534           0.028539          -0.180283  \n",
              "3               0.018664          -0.052094           0.030450  \n",
              "4              -0.053032           0.020476          -0.050601  \n",
              "...                  ...                ...                ...  \n",
              "73909          -0.082385          -0.103822          -0.137820  \n",
              "73910          -0.056653          -0.081094          -0.102598  \n",
              "73911          -0.145709          -0.055229          -0.079751  \n",
              "73912          -0.089779          -0.144744          -0.053753  \n",
              "73913          -0.090310          -0.088526          -0.143732  \n",
              "\n",
              "[73914 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9953c7be-027b-466b-b4db-af8712839306\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Area</th>\n",
              "      <th>Products</th>\n",
              "      <th>Year</th>\n",
              "      <th>Export Value</th>\n",
              "      <th>Exchange Rate</th>\n",
              "      <th>Land Temperature</th>\n",
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              "      <th>Export Value Lag2</th>\n",
              "      <th>Export Value Lag3</th>\n",
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              "  <tbody>\n",
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              "      <td>0.024447</td>\n",
              "      <td>0.234375</td>\n",
              "      <td>-0.667761</td>\n",
              "      <td>-0.181886</td>\n",
              "      <td>-0.181107</td>\n",
              "      <td>-0.180283</td>\n",
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              "      <th>1</th>\n",
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              "      <td>1</td>\n",
              "      <td>-0.055376</td>\n",
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              "      <td>-0.181107</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.016465</td>\n",
              "      <td>0.234375</td>\n",
              "      <td>-0.667761</td>\n",
              "      <td>-0.053534</td>\n",
              "      <td>0.028539</td>\n",
              "      <td>-0.180283</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>-0.054877</td>\n",
              "      <td>2.360152</td>\n",
              "      <td>-0.667761</td>\n",
              "      <td>0.018664</td>\n",
              "      <td>-0.052094</td>\n",
              "      <td>0.030450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
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              "      <td>-0.053032</td>\n",
              "      <td>0.020476</td>\n",
              "      <td>-0.050601</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>73909</th>\n",
              "      <td>202</td>\n",
              "      <td>11</td>\n",
              "      <td>27</td>\n",
              "      <td>-0.058480</td>\n",
              "      <td>1.776096</td>\n",
              "      <td>-0.144038</td>\n",
              "      <td>-0.082385</td>\n",
              "      <td>-0.103822</td>\n",
              "      <td>-0.137820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73910</th>\n",
              "      <td>202</td>\n",
              "      <td>11</td>\n",
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              "      <td>-0.147095</td>\n",
              "      <td>0.043221</td>\n",
              "      <td>0.122976</td>\n",
              "      <td>-0.056653</td>\n",
              "      <td>-0.081094</td>\n",
              "      <td>-0.102598</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73911</th>\n",
              "      <td>202</td>\n",
              "      <td>11</td>\n",
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              "      <td>-0.091442</td>\n",
              "      <td>0.238515</td>\n",
              "      <td>-0.180161</td>\n",
              "      <td>-0.145709</td>\n",
              "      <td>-0.055229</td>\n",
              "      <td>-0.079751</td>\n",
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              "    <tr>\n",
              "      <th>73912</th>\n",
              "      <td>202</td>\n",
              "      <td>11</td>\n",
              "      <td>30</td>\n",
              "      <td>-0.091970</td>\n",
              "      <td>0.449807</td>\n",
              "      <td>-0.471181</td>\n",
              "      <td>-0.089779</td>\n",
              "      <td>-0.144744</td>\n",
              "      <td>-0.053753</td>\n",
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              "    <tr>\n",
              "      <th>73913</th>\n",
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              "      <td>1.931128</td>\n",
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              "      <td>-0.090310</td>\n",
              "      <td>-0.088526</td>\n",
              "      <td>-0.143732</td>\n",
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              "  </tbody>\n",
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              "<p>73914 rows × 9 columns</p>\n",
              "</div>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c0c7dcef-bbec-4e0d-b8a2-86197154b2ee button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 73914,\n  \"fields\": [\n    {\n      \"column\": \"Area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 58,\n        \"min\": 0,\n        \"max\": 202,\n        \"num_unique_values\": 203,\n        \"samples\": [\n          15,\n          9,\n          115\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Products\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 11,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          7,\n          8,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9,\n        \"min\": 0,\n        \"max\": 31,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          29,\n          15,\n          24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Export Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.421717142514884,\n        \"min\": -0.18309301482740525,\n        \"max\": 416.68307883376434,\n        \"num_unique_values\": 60162,\n        \"samples\": [\n          -0.0027163782647532182,\n          1.1740833626495943,\n          -0.14318648063007722\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Exchange Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 486213.2665291647,\n        \"min\": -0.052843787072753064,\n        \"max\": 38162070.69190143,\n        \"num_unique_values\": 4018,\n        \"samples\": [\n          5.167963017828884,\n          0.0344293488936841,\n          0.28616640595650267\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Land Temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6150886949333703,\n        \"min\": -1.08843845544106,\n        \"max\": 3.511606839542521,\n        \"num_unique_values\": 3736,\n        \"samples\": [\n          0.3072132261352055,\n          0.2394972256822557,\n          0.3044955271203715\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Export Value Lag1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.468383733523643,\n        \"min\": -0.18188607950580382,\n        \"max\": 418.7580548696977,\n        \"num_unique_values\": 59962,\n        \"samples\": [\n          11.731792287748636,\n          0.10024007756786076,\n          1.8881043623899658\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Export Value Lag2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.516428457712722,\n        \"min\": -0.1811071641857951,\n        \"max\": 420.9160123418693,\n        \"num_unique_values\": 59765,\n        \"samples\": [\n          -0.016645918525515058,\n          1.5496643901183556,\n          5.203714081262318\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Export Value Lag3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.56512344213346,\n        \"min\": -0.18028341911754042,\n        \"max\": 423.1005903438883,\n        \"num_unique_values\": 59566,\n        \"samples\": [\n          64.72411492266795,\n          1.533895936972743,\n          -0.033448755108784145\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Select Input and Label"
      ],
      "metadata": {
        "id": "4sxATELosGXZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "target = 'Export Value Lag3'\n",
        "\n",
        "X = df[features]\n",
        "Y = df[target]"
      ],
      "metadata": {
        "id": "bUvL3oNSsM0u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Split Test and Train data"
      ],
      "metadata": {
        "id": "9uLQrf4ysSk7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)\n",
        "x_test_df = deepcopy(X_test)\n",
        "y_test_df = deepcopy(y_train)"
      ],
      "metadata": {
        "id": "QolIjH0qsWXb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Convert Vectors to Pytorch Tensors"
      ],
      "metadata": {
        "id": "wkpln9nmscid"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "X_train = torch.tensor(X_train.values.astype(np.float32)).to(device)\n",
        "X_test = torch.tensor(X_test.values.astype(np.float32)).to(device)\n",
        "y_train = torch.tensor(y_train.values.astype(np.float32)).to(device)\n",
        "y_test = torch.tensor(y_test.values.astype(np.float32)).to(device)"
      ],
      "metadata": {
        "id": "lQbZXqMKsqUZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model\n",
        "## Define a MLP model"
      ],
      "metadata": {
        "id": "wFUXwEryss-a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class MLP(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(MLP, self).__init__()\n",
        "        self.layers = nn.Sequential(\n",
        "            nn.Linear(len(features), 64),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(64, 64),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(64, 1))\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.layers(x)\n",
        "\n",
        "model = MLP().to(device)\n"
      ],
      "metadata": {
        "id": "mgr9q9Zis5Hg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hyperparameters"
      ],
      "metadata": {
        "id": "SgNIjtq1tsqw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "criterion = nn.MSELoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001, weight_decay=1e-5)\n",
        "\n",
        "epochs = 1000\n",
        "train_losses = []\n",
        "test_losses = []"
      ],
      "metadata": {
        "id": "AkF4gw4rs61K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training model"
      ],
      "metadata": {
        "id": "kfiT73C_t4kw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for epoch in range(epochs):\n",
        "    model.train()\n",
        "    optimizer.zero_grad()\n",
        "    outputs = model(X_train)\n",
        "    loss = criterion(outputs.squeeze(), y_train)\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    train_losses.append(loss.item())\n",
        "\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        test_outputs = model(X_test)\n",
        "        test_loss = criterion(test_outputs.squeeze(), y_test)\n",
        "        test_losses.append(test_loss.item())\n",
        "        print(f'Epoch {epoch + 1}: Loss = {loss.item()}, Test Loss = {test_loss.item()}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y0JdbIXktzjn",
        "outputId": "3c848b2a-3964-4174-b8e0-ec99bcfec49b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1: Loss = 1286313472.0, Test Loss = 515.31298828125\n",
            "Epoch 2: Loss = 885110272.0, Test Loss = 366.1660461425781\n",
            "Epoch 3: Loss = 568093824.0, Test Loss = 258.38824462890625\n",
            "Epoch 4: Loss = 336602560.0, Test Loss = 181.80477905273438\n",
            "Epoch 5: Loss = 171113232.0, Test Loss = 132.9900360107422\n",
            "Epoch 6: Loss = 67238584.0, Test Loss = 106.45787048339844\n",
            "Epoch 7: Loss = 13885695.0, Test Loss = 96.89295959472656\n",
            "Epoch 8: Loss = 25588.41015625, Test Loss = 98.33260345458984\n",
            "Epoch 9: Loss = 12419355.0, Test Loss = 106.22813415527344\n",
            "Epoch 10: Loss = 40218412.0, Test Loss = 116.31409454345703\n",
            "Epoch 11: Loss = 73017056.0, Test Loss = 125.13461303710938\n",
            "Epoch 12: Loss = 102115512.0, Test Loss = 130.47779846191406\n",
            "Epoch 13: Loss = 121847192.0, Test Loss = 131.40501403808594\n",
            "Epoch 14: Loss = 129857344.0, Test Loss = 127.88726043701172\n",
            "Epoch 15: Loss = 126518496.0, Test Loss = 120.59712219238281\n",
            "Epoch 16: Loss = 113996120.0, Test Loss = 110.63233184814453\n",
            "Epoch 17: Loss = 95344040.0, Test Loss = 99.22396850585938\n",
            "Epoch 18: Loss = 73795616.0, Test Loss = 87.52531433105469\n",
            "Epoch 19: Loss = 52275124.0, Test Loss = 76.56680297851562\n",
            "Epoch 20: Loss = 33101436.0, Test Loss = 67.13497161865234\n",
            "Epoch 21: Loss = 17843272.0, Test Loss = 59.61365509033203\n",
            "Epoch 22: Loss = 7291517.5, Test Loss = 54.129722595214844\n",
            "Epoch 23: Loss = 1520943.625, Test Loss = 50.595787048339844\n",
            "Epoch 24: Loss = 17035.38671875, Test Loss = 48.705421447753906\n",
            "Epoch 25: Loss = 1837217.5, Test Loss = 48.01382827758789\n",
            "Epoch 26: Loss = 5820673.0, Test Loss = 47.96824264526367\n",
            "Epoch 27: Loss = 10768860.0, Test Loss = 48.023590087890625\n",
            "Epoch 28: Loss = 15600141.0, Test Loss = 47.73321533203125\n",
            "Epoch 29: Loss = 19466046.0, Test Loss = 46.74851989746094\n",
            "Epoch 30: Loss = 21819432.0, Test Loss = 44.886383056640625\n",
            "Epoch 31: Loss = 22431566.0, Test Loss = 42.15027618408203\n",
            "Epoch 32: Loss = 21365012.0, Test Loss = 38.852169036865234\n",
            "Epoch 33: Loss = 19372510.0, Test Loss = 34.95645523071289\n",
            "Epoch 34: Loss = 15502434.0, Test Loss = 31.08078956604004\n",
            "Epoch 35: Loss = 11946798.0, Test Loss = 27.21002197265625\n",
            "Epoch 36: Loss = 7783464.0, Test Loss = 23.747882843017578\n",
            "Epoch 37: Loss = 4140005.75, Test Loss = 20.867351531982422\n",
            "Epoch 38: Loss = 1500235.625, Test Loss = 18.70822525024414\n",
            "Epoch 39: Loss = 167226.453125, Test Loss = 17.28641700744629\n",
            "Epoch 40: Loss = 133193.484375, Test Loss = 16.46493911743164\n",
            "Epoch 41: Loss = 1087334.125, Test Loss = 15.97834587097168\n",
            "Epoch 42: Loss = 2482760.25, Test Loss = 15.591340065002441\n",
            "Epoch 43: Loss = 3772403.5, Test Loss = 15.147151947021484\n",
            "Epoch 44: Loss = 4590412.5, Test Loss = 14.575857162475586\n",
            "Epoch 45: Loss = 4792207.0, Test Loss = 13.85195255279541\n",
            "Epoch 46: Loss = 4414380.0, Test Loss = 13.0538969039917\n",
            "Epoch 47: Loss = 3613054.5, Test Loss = 12.266256332397461\n",
            "Epoch 48: Loss = 2601717.5, Test Loss = 11.577448844909668\n",
            "Epoch 49: Loss = 1595835.375, Test Loss = 11.053014755249023\n",
            "Epoch 50: Loss = 789338.125, Test Loss = 10.761445045471191\n",
            "Epoch 51: Loss = 403719.90625, Test Loss = 10.593366622924805\n",
            "Epoch 52: Loss = 141837.171875, Test Loss = 10.521418571472168\n",
            "Epoch 53: Loss = 18.055557250976562, Test Loss = 10.571870803833008\n",
            "Epoch 54: Loss = 106199.3828125, Test Loss = 10.55449390411377\n",
            "Epoch 55: Loss = 128247.8671875, Test Loss = 10.461382865905762\n",
            "Epoch 56: Loss = 50274.8515625, Test Loss = 10.353958129882812\n",
            "Epoch 57: Loss = 2237.83984375, Test Loss = 10.25993537902832\n",
            "Epoch 58: Loss = 23229.98046875, Test Loss = 10.15390682220459\n",
            "Epoch 59: Loss = 37633.71875, Test Loss = 10.04328441619873\n",
            "Epoch 60: Loss = 23643.25, Test Loss = 9.936108589172363\n",
            "Epoch 61: Loss = 3824.64404296875, Test Loss = 9.825759887695312\n",
            "Epoch 62: Loss = 1860.4307861328125, Test Loss = 9.694643020629883\n",
            "Epoch 63: Loss = 15269.6201171875, Test Loss = 9.53821849822998\n",
            "Epoch 64: Loss = 24083.490234375, Test Loss = 9.370959281921387\n",
            "Epoch 65: Loss = 17958.142578125, Test Loss = 9.197607040405273\n",
            "Epoch 66: Loss = 5197.8076171875, Test Loss = 9.013479232788086\n",
            "Epoch 67: Loss = 77.96695709228516, Test Loss = 8.823558807373047\n",
            "Epoch 68: Loss = 6373.0732421875, Test Loss = 8.64240837097168\n",
            "Epoch 69: Loss = 13438.8125, Test Loss = 8.484955787658691\n",
            "Epoch 70: Loss = 8104.4453125, Test Loss = 8.345865249633789\n",
            "Epoch 71: Loss = 230.40187072753906, Test Loss = 8.210779190063477\n",
            "Epoch 72: Loss = 4556.44140625, Test Loss = 8.062490463256836\n",
            "Epoch 73: Loss = 6937.23583984375, Test Loss = 7.914691925048828\n",
            "Epoch 74: Loss = 2020.8778076171875, Test Loss = 7.7761921882629395\n",
            "Epoch 75: Loss = 2035.38037109375, Test Loss = 7.653595924377441\n",
            "Epoch 76: Loss = 1525.9345703125, Test Loss = 7.525174617767334\n",
            "Epoch 77: Loss = 1582.7139892578125, Test Loss = 7.406102180480957\n",
            "Epoch 78: Loss = 372.1690368652344, Test Loss = 7.291762828826904\n",
            "Epoch 79: Loss = 89.67146301269531, Test Loss = 7.166661739349365\n",
            "Epoch 80: Loss = 59.90497970581055, Test Loss = 7.032005786895752\n",
            "Epoch 81: Loss = 4960.24462890625, Test Loss = 6.932297706604004\n",
            "Epoch 82: Loss = 3931.671630859375, Test Loss = 6.814391136169434\n",
            "Epoch 83: Loss = 5020.2001953125, Test Loss = 6.678977966308594\n",
            "Epoch 84: Loss = 105.21819305419922, Test Loss = 6.5467209815979\n",
            "Epoch 85: Loss = 4242.6455078125, Test Loss = 6.426694869995117\n",
            "Epoch 86: Loss = 297.9393310546875, Test Loss = 6.31887674331665\n",
            "Epoch 87: Loss = 3040.513916015625, Test Loss = 6.195517063140869\n",
            "Epoch 88: Loss = 1002.200927734375, Test Loss = 6.068419933319092\n",
            "Epoch 89: Loss = 709.8736572265625, Test Loss = 5.9591474533081055\n",
            "Epoch 90: Loss = 11694.0068359375, Test Loss = 5.839754104614258\n",
            "Epoch 91: Loss = 20738.826171875, Test Loss = 5.729139804840088\n",
            "Epoch 92: Loss = 3189.73583984375, Test Loss = 5.638898849487305\n",
            "Epoch 93: Loss = 13463.978515625, Test Loss = 5.528530597686768\n",
            "Epoch 94: Loss = 5675.10400390625, Test Loss = 5.413018703460693\n",
            "Epoch 95: Loss = 4975.8759765625, Test Loss = 5.310380458831787\n",
            "Epoch 96: Loss = 9532.6513671875, Test Loss = 5.214768886566162\n",
            "Epoch 97: Loss = 93.51396942138672, Test Loss = 5.1255927085876465\n",
            "Epoch 98: Loss = 8626.537109375, Test Loss = 5.018918037414551\n",
            "Epoch 99: Loss = 2091.854736328125, Test Loss = 4.915327072143555\n",
            "Epoch 100: Loss = 2880.742431640625, Test Loss = 4.817977428436279\n",
            "Epoch 101: Loss = 5663.46484375, Test Loss = 4.724228858947754\n",
            "Epoch 102: Loss = 14.24316692352295, Test Loss = 4.6387810707092285\n",
            "Epoch 103: Loss = 4253.333984375, Test Loss = 4.543600559234619\n",
            "Epoch 104: Loss = 2364.9716796875, Test Loss = 4.443885326385498\n",
            "Epoch 105: Loss = 537.6493530273438, Test Loss = 4.352017879486084\n",
            "Epoch 106: Loss = 3651.210693359375, Test Loss = 4.263258457183838\n",
            "Epoch 107: Loss = 580.2537231445312, Test Loss = 4.177711486816406\n",
            "Epoch 108: Loss = 1313.2701416015625, Test Loss = 4.088936805725098\n",
            "Epoch 109: Loss = 2384.669189453125, Test Loss = 3.993826389312744\n",
            "Epoch 110: Loss = 23.848535537719727, Test Loss = 3.9003498554229736\n",
            "Epoch 111: Loss = 1625.542236328125, Test Loss = 3.810361385345459\n",
            "Epoch 112: Loss = 1237.1575927734375, Test Loss = 3.724158525466919\n",
            "Epoch 113: Loss = 82.559326171875, Test Loss = 3.6389145851135254\n",
            "Epoch 114: Loss = 1494.18603515625, Test Loss = 3.5500760078430176\n",
            "Epoch 115: Loss = 480.8035888671875, Test Loss = 3.461259126663208\n",
            "Epoch 116: Loss = 315.6705017089844, Test Loss = 3.3750762939453125\n",
            "Epoch 117: Loss = 1123.6617431640625, Test Loss = 3.2921409606933594\n",
            "Epoch 118: Loss = 105.27021789550781, Test Loss = 3.212454080581665\n",
            "Epoch 119: Loss = 497.2538146972656, Test Loss = 3.130840301513672\n",
            "Epoch 120: Loss = 703.2383422851562, Test Loss = 3.0474836826324463\n",
            "Epoch 121: Loss = 1.8825113773345947, Test Loss = 2.965420722961426\n",
            "Epoch 122: Loss = 551.5201416015625, Test Loss = 2.8852827548980713\n",
            "Epoch 123: Loss = 350.6089782714844, Test Loss = 2.807685136795044\n",
            "Epoch 124: Loss = 46.06153106689453, Test Loss = 2.7305924892425537\n",
            "Epoch 125: Loss = 486.1846008300781, Test Loss = 2.6526150703430176\n",
            "Epoch 126: Loss = 120.55744171142578, Test Loss = 2.5772387981414795\n",
            "Epoch 127: Loss = 133.26348876953125, Test Loss = 2.504507064819336\n",
            "Epoch 128: Loss = 348.3885498046875, Test Loss = 2.43291974067688\n",
            "Epoch 129: Loss = 16.095901489257812, Test Loss = 2.36362886428833\n",
            "Epoch 130: Loss = 194.3589324951172, Test Loss = 2.2932324409484863\n",
            "Epoch 131: Loss = 196.6772003173828, Test Loss = 2.223180055618286\n",
            "Epoch 132: Loss = 5.24219274520874, Test Loss = 2.1558151245117188\n",
            "Epoch 133: Loss = 200.72901916503906, Test Loss = 2.0895755290985107\n",
            "Epoch 134: Loss = 77.55631256103516, Test Loss = 2.0262534618377686\n",
            "Epoch 135: Loss = 40.213932037353516, Test Loss = 1.9630709886550903\n",
            "Epoch 136: Loss = 157.80409240722656, Test Loss = 1.899261474609375\n",
            "Epoch 137: Loss = 13.852900505065918, Test Loss = 1.8354138135910034\n",
            "Epoch 138: Loss = 76.40422058105469, Test Loss = 1.7717772722244263\n",
            "Epoch 139: Loss = 92.47704315185547, Test Loss = 1.7098101377487183\n",
            "Epoch 140: Loss = 1.7776564359664917, Test Loss = 1.6486564874649048\n",
            "Epoch 141: Loss = 874.5892333984375, Test Loss = 1.5907057523727417\n",
            "Epoch 142: Loss = 328.8056640625, Test Loss = 1.536021113395691\n",
            "Epoch 143: Loss = 694.749755859375, Test Loss = 1.4844069480895996\n",
            "Epoch 144: Loss = 49.4791145324707, Test Loss = 1.434280276298523\n",
            "Epoch 145: Loss = 588.9584350585938, Test Loss = 1.381344199180603\n",
            "Epoch 146: Loss = 119.33329772949219, Test Loss = 1.3287765979766846\n",
            "Epoch 147: Loss = 202.5020751953125, Test Loss = 1.277711033821106\n",
            "Epoch 148: Loss = 393.0379638671875, Test Loss = 1.2306658029556274\n",
            "Epoch 149: Loss = 1.8188197612762451, Test Loss = 1.1834452152252197\n",
            "Epoch 150: Loss = 295.02008056640625, Test Loss = 1.1358026266098022\n",
            "Epoch 151: Loss = 165.2233428955078, Test Loss = 1.0860786437988281\n",
            "Epoch 152: Loss = 44.6534309387207, Test Loss = 1.0408705472946167\n",
            "Epoch 153: Loss = 260.8370361328125, Test Loss = 0.9989220499992371\n",
            "Epoch 154: Loss = 25.99427032470703, Test Loss = 0.9610717296600342\n",
            "Epoch 155: Loss = 7665.65869140625, Test Loss = 0.928669810295105\n",
            "Epoch 156: Loss = 8057.13037109375, Test Loss = 0.9006185531616211\n",
            "Epoch 157: Loss = 261.760498046875, Test Loss = 0.8577516674995422\n",
            "Epoch 158: Loss = 3750.822021484375, Test Loss = 0.8384070992469788\n",
            "Epoch 159: Loss = 45.16816711425781, Test Loss = 0.8013344407081604\n",
            "Epoch 160: Loss = 2851.65771484375, Test Loss = 0.7792625427246094\n",
            "Epoch 161: Loss = 1.1304031610488892, Test Loss = 0.7517538070678711\n",
            "Epoch 162: Loss = 2295.0205078125, Test Loss = 0.7253417372703552\n",
            "Epoch 163: Loss = 96.48505401611328, Test Loss = 0.7029784917831421\n",
            "Epoch 164: Loss = 1576.4725341796875, Test Loss = 0.6732069253921509\n",
            "Epoch 165: Loss = 396.4388122558594, Test Loss = 0.6600842475891113\n",
            "Epoch 166: Loss = 846.623779296875, Test Loss = 0.6286866664886475\n",
            "Epoch 167: Loss = 767.1931762695312, Test Loss = 0.6146435737609863\n",
            "Epoch 168: Loss = 200.46218872070312, Test Loss = 0.5810191035270691\n",
            "Epoch 169: Loss = 869.9906005859375, Test Loss = 0.5697943568229675\n",
            "Epoch 170: Loss = 2.418313980102539, Test Loss = 0.5437229871749878\n",
            "Epoch 171: Loss = 667.9063110351562, Test Loss = 0.5292068719863892\n",
            "Epoch 172: Loss = 192.15101623535156, Test Loss = 0.5037103295326233\n",
            "Epoch 173: Loss = 270.2245788574219, Test Loss = 0.48462650179862976\n",
            "Epoch 174: Loss = 421.1647644042969, Test Loss = 0.4693804085254669\n",
            "Epoch 175: Loss = 21.744760513305664, Test Loss = 0.4503888189792633\n",
            "Epoch 176: Loss = 431.6154479980469, Test Loss = 0.43779456615448\n",
            "Epoch 177: Loss = 37.93522262573242, Test Loss = 0.41353487968444824\n",
            "Epoch 178: Loss = 232.7755584716797, Test Loss = 0.40451887249946594\n",
            "Epoch 179: Loss = 180.2209930419922, Test Loss = 0.3856479227542877\n",
            "Epoch 180: Loss = 19.642410278320312, Test Loss = 0.3786109685897827\n",
            "Epoch 181: Loss = 230.06448364257812, Test Loss = 0.35964810848236084\n",
            "Epoch 182: Loss = 42.573944091796875, Test Loss = 0.34780192375183105\n",
            "Epoch 183: Loss = 84.83078002929688, Test Loss = 0.3337959051132202\n",
            "Epoch 184: Loss = 148.46705627441406, Test Loss = 0.3248225450515747\n",
            "Epoch 185: Loss = 0.22522452473640442, Test Loss = 0.3149523437023163\n",
            "Epoch 186: Loss = 120.65577697753906, Test Loss = 0.3032650351524353\n",
            "Epoch 187: Loss = 55.5965461730957, Test Loss = 0.2928016483783722\n",
            "Epoch 188: Loss = 24.105743408203125, Test Loss = 0.2823638916015625\n",
            "Epoch 189: Loss = 100.09991455078125, Test Loss = 0.27500826120376587\n",
            "Epoch 190: Loss = 5.307845115661621, Test Loss = 0.26598793268203735\n",
            "Epoch 191: Loss = 56.68109130859375, Test Loss = 0.25822341442108154\n",
            "Epoch 192: Loss = 49.35358810424805, Test Loss = 0.24898119270801544\n",
            "Epoch 193: Loss = 4.882250785827637, Test Loss = 0.24159695208072662\n",
            "Epoch 194: Loss = 60.206363677978516, Test Loss = 0.23374450206756592\n",
            "Epoch 195: Loss = 9.733282089233398, Test Loss = 0.2277001142501831\n",
            "Epoch 196: Loss = 25.037670135498047, Test Loss = 0.22105902433395386\n",
            "Epoch 197: Loss = 36.20708084106445, Test Loss = 0.21388942003250122\n",
            "Epoch 198: Loss = 0.5775829553604126, Test Loss = 0.2008865922689438\n",
            "Epoch 199: Loss = 34.09364700317383, Test Loss = 0.19647915661334991\n",
            "Epoch 200: Loss = 6704.9404296875, Test Loss = 0.19884346425533295\n",
            "Epoch 201: Loss = 10399.60546875, Test Loss = 0.19897615909576416\n",
            "Epoch 202: Loss = 4793.26220703125, Test Loss = 0.19122545421123505\n",
            "Epoch 203: Loss = 1993.293701171875, Test Loss = 0.1873428076505661\n",
            "Epoch 204: Loss = 8540.3984375, Test Loss = 0.18736663460731506\n",
            "Epoch 205: Loss = 561.23193359375, Test Loss = 0.19361917674541473\n",
            "Epoch 206: Loss = 4421.3603515625, Test Loss = 0.19238056242465973\n",
            "Epoch 207: Loss = 4609.38623046875, Test Loss = 0.17600888013839722\n",
            "Epoch 208: Loss = 121.6628189086914, Test Loss = 0.17178991436958313\n",
            "Epoch 209: Loss = 4759.74658203125, Test Loss = 0.16773031651973724\n",
            "Epoch 210: Loss = 947.0748291015625, Test Loss = 0.3317927420139313\n",
            "Epoch 211: Loss = 335536.40625, Test Loss = 0.23249882459640503\n",
            "Epoch 212: Loss = 155291.453125, Test Loss = 0.21908314526081085\n",
            "Epoch 213: Loss = 236823.953125, Test Loss = 0.21264664828777313\n",
            "Epoch 214: Loss = 40967.15625, Test Loss = 0.19485560059547424\n",
            "Epoch 215: Loss = 32085.455078125, Test Loss = 0.3241878151893616\n",
            "Epoch 216: Loss = 159252.5625, Test Loss = 0.2600798010826111\n",
            "Epoch 217: Loss = 119587.578125, Test Loss = 0.19169287383556366\n",
            "Epoch 218: Loss = 10377.5244140625, Test Loss = 0.16771288216114044\n",
            "Epoch 219: Loss = 29930.84375, Test Loss = 0.16769848763942719\n",
            "Epoch 220: Loss = 101929.2578125, Test Loss = 0.1688428521156311\n",
            "Epoch 221: Loss = 74255.421875, Test Loss = 0.14768476784229279\n",
            "Epoch 222: Loss = 7736.00048828125, Test Loss = 0.18663367629051208\n",
            "Epoch 223: Loss = 14619.7177734375, Test Loss = 0.20655354857444763\n",
            "Epoch 224: Loss = 59876.515625, Test Loss = 0.21587085723876953\n",
            "Epoch 225: Loss = 54168.078125, Test Loss = 0.1730118989944458\n",
            "Epoch 226: Loss = 11945.23828125, Test Loss = 0.14618775248527527\n",
            "Epoch 227: Loss = 2864.741943359375, Test Loss = 0.14943969249725342\n",
            "Epoch 228: Loss = 30089.658203125, Test Loss = 0.13842077553272247\n",
            "Epoch 229: Loss = 38985.04296875, Test Loss = 0.14171768724918365\n",
            "Epoch 230: Loss = 15824.3251953125, Test Loss = 0.14199629426002502\n",
            "Epoch 231: Loss = 13.197151184082031, Test Loss = 0.1625560075044632\n",
            "Epoch 232: Loss = 11470.9716796875, Test Loss = 0.17117998003959656\n",
            "Epoch 233: Loss = 24741.06640625, Test Loss = 0.16439653933048248\n",
            "Epoch 234: Loss = 17270.826171875, Test Loss = 0.14820896089076996\n",
            "Epoch 235: Loss = 2457.502685546875, Test Loss = 0.1307317614555359\n",
            "Epoch 236: Loss = 2120.80810546875, Test Loss = 0.13077835738658905\n",
            "Epoch 237: Loss = 12403.4638671875, Test Loss = 0.12493415921926498\n",
            "Epoch 238: Loss = 14460.6103515625, Test Loss = 0.1264605075120926\n",
            "Epoch 239: Loss = 5601.96923828125, Test Loss = 0.13059376180171967\n",
            "Epoch 240: Loss = 1.4622466564178467, Test Loss = 0.1391184777021408\n",
            "Epoch 241: Loss = 4280.1943359375, Test Loss = 0.14478813111782074\n",
            "Epoch 242: Loss = 9333.859375, Test Loss = 0.13968682289123535\n",
            "Epoch 243: Loss = 6828.56494140625, Test Loss = 0.13167615234851837\n",
            "Epoch 244: Loss = 1170.884521484375, Test Loss = 0.12126566469669342\n",
            "Epoch 245: Loss = 587.9696655273438, Test Loss = 0.11852540820837021\n",
            "Epoch 246: Loss = 4400.5263671875, Test Loss = 0.11589151620864868\n",
            "Epoch 247: Loss = 5665.92822265625, Test Loss = 0.11648492515087128\n",
            "Epoch 248: Loss = 2550.730224609375, Test Loss = 0.11993654817342758\n",
            "Epoch 249: Loss = 39.67993927001953, Test Loss = 0.12436886876821518\n",
            "Epoch 250: Loss = 1309.8531494140625, Test Loss = 0.1278505176305771\n",
            "Epoch 251: Loss = 3449.4013671875, Test Loss = 0.125615194439888\n",
            "Epoch 252: Loss = 2874.4990234375, Test Loss = 0.12121069431304932\n",
            "Epoch 253: Loss = 666.1695556640625, Test Loss = 0.11505722999572754\n",
            "Epoch 254: Loss = 109.38790130615234, Test Loss = 0.11211206018924713\n",
            "Epoch 255: Loss = 1489.8121337890625, Test Loss = 0.11032815277576447\n",
            "Epoch 256: Loss = 2221.47998046875, Test Loss = 0.11056619137525558\n",
            "Epoch 257: Loss = 1173.4122314453125, Test Loss = 0.11397874355316162\n",
            "Epoch 258: Loss = 59.594993591308594, Test Loss = 0.11529918015003204\n",
            "Epoch 259: Loss = 382.0842590332031, Test Loss = 0.11732848733663559\n",
            "Epoch 260: Loss = 1265.2947998046875, Test Loss = 0.11637897789478302\n",
            "Epoch 261: Loss = 1189.778564453125, Test Loss = 0.11374647915363312\n",
            "Epoch 262: Loss = 340.4681091308594, Test Loss = 0.11015007644891739\n",
            "Epoch 263: Loss = 16.88334846496582, Test Loss = 0.10779574513435364\n",
            "Epoch 264: Loss = 512.2817993164062, Test Loss = 0.106503926217556\n",
            "Epoch 265: Loss = 863.2924194335938, Test Loss = 0.10653700679540634\n",
            "Epoch 266: Loss = 506.6676025390625, Test Loss = 0.10775735974311829\n",
            "Epoch 267: Loss = 40.810577392578125, Test Loss = 0.10928350687026978\n",
            "Epoch 268: Loss = 120.48699951171875, Test Loss = 0.11029330641031265\n",
            "Epoch 269: Loss = 473.4820556640625, Test Loss = 0.10965730994939804\n",
            "Epoch 270: Loss = 477.37591552734375, Test Loss = 0.10787546634674072\n",
            "Epoch 271: Loss = 149.99708557128906, Test Loss = 0.10550566762685776\n",
            "Epoch 272: Loss = 3.5539450645446777, Test Loss = 0.10373733937740326\n",
            "Epoch 273: Loss = 189.15269470214844, Test Loss = 0.10264084488153458\n",
            "Epoch 274: Loss = 336.1189270019531, Test Loss = 0.10255343466997147\n",
            "Epoch 275: Loss = 203.3985595703125, Test Loss = 0.10314641892910004\n",
            "Epoch 276: Loss = 17.694475173950195, Test Loss = 0.10398473590612411\n",
            "Epoch 277: Loss = 45.74577713012695, Test Loss = 0.10433708131313324\n",
            "Epoch 278: Loss = 184.93743896484375, Test Loss = 0.10381414741277695\n",
            "Epoch 279: Loss = 186.3048095703125, Test Loss = 0.10251177847385406\n",
            "Epoch 280: Loss = 56.85292053222656, Test Loss = 0.10088779777288437\n",
            "Epoch 281: Loss = 2.020223379135132, Test Loss = 0.09954166412353516\n",
            "Epoch 282: Loss = 77.55768585205078, Test Loss = 0.09867589920759201\n",
            "Epoch 283: Loss = 132.18360900878906, Test Loss = 0.0984819307923317\n",
            "Epoch 284: Loss = 75.58740997314453, Test Loss = 0.09869561344385147\n",
            "Epoch 285: Loss = 4.939445972442627, Test Loss = 0.0990261659026146\n",
            "Epoch 286: Loss = 21.73472785949707, Test Loss = 0.09898966550827026\n",
            "Epoch 287: Loss = 75.86459350585938, Test Loss = 0.09845276921987534\n",
            "Epoch 288: Loss = 70.16377258300781, Test Loss = 0.09742418676614761\n",
            "Epoch 289: Loss = 17.98178482055664, Test Loss = 0.09625808894634247\n",
            "Epoch 290: Loss = 2.1939752101898193, Test Loss = 0.09523828327655792\n",
            "Epoch 291: Loss = 34.9610595703125, Test Loss = 0.09459956735372543\n",
            "Epoch 292: Loss = 51.81486129760742, Test Loss = 0.09436187893152237\n",
            "Epoch 293: Loss = 25.349077224731445, Test Loss = 0.09439044445753098\n",
            "Epoch 294: Loss = 0.6392530202865601, Test Loss = 0.09440509974956512\n",
            "Epoch 295: Loss = 12.099156379699707, Test Loss = 0.09419208765029907\n",
            "Epoch 296: Loss = 32.00531768798828, Test Loss = 0.09363990277051926\n",
            "Epoch 297: Loss = 24.88259506225586, Test Loss = 0.09281528741121292\n",
            "Epoch 298: Loss = 4.30396032333374, Test Loss = 0.09192392230033875\n",
            "Epoch 299: Loss = 2.475515127182007, Test Loss = 0.09116111695766449\n",
            "Epoch 300: Loss = 16.624448776245117, Test Loss = 0.09065283089876175\n",
            "Epoch 301: Loss = 19.730939865112305, Test Loss = 0.09040506184101105\n",
            "Epoch 302: Loss = 7.228532791137695, Test Loss = 0.09029258042573929\n",
            "Epoch 303: Loss = 0.1148393303155899, Test Loss = 0.09012935310602188\n",
            "Epoch 304: Loss = 7.160933494567871, Test Loss = 0.08979731798171997\n",
            "Epoch 305: Loss = 13.34811019897461, Test Loss = 0.08922194689512253\n",
            "Epoch 306: Loss = 7.9264373779296875, Test Loss = 0.08847379684448242\n",
            "Epoch 307: Loss = 0.5857751369476318, Test Loss = 0.08773332089185715\n",
            "Epoch 308: Loss = 2.3735766410827637, Test Loss = 0.08642636239528656\n",
            "Epoch 309: Loss = 96.30256652832031, Test Loss = 0.08706103265285492\n",
            "Epoch 310: Loss = 0.620792031288147, Test Loss = 0.08699095994234085\n",
            "Epoch 311: Loss = 12.824380874633789, Test Loss = 0.08668898046016693\n",
            "Epoch 312: Loss = 19.520517349243164, Test Loss = 0.08612550050020218\n",
            "Epoch 313: Loss = 9.383330345153809, Test Loss = 0.08540405333042145\n",
            "Epoch 314: Loss = 0.20566822588443756, Test Loss = 0.08469293266534805\n",
            "Epoch 315: Loss = 5.122699737548828, Test Loss = 0.08413457125425339\n",
            "Epoch 316: Loss = 12.371684074401855, Test Loss = 0.08380228281021118\n",
            "Epoch 317: Loss = 8.734101295471191, Test Loss = 0.08365003764629364\n",
            "Epoch 318: Loss = 1.1003155708312988, Test Loss = 0.08353926241397858\n",
            "Epoch 319: Loss = 1.5835176706314087, Test Loss = 0.08334030210971832\n",
            "Epoch 320: Loss = 7.054586410522461, Test Loss = 0.08342847228050232\n",
            "Epoch 321: Loss = 156.3469696044922, Test Loss = 0.08352193236351013\n",
            "Epoch 322: Loss = 12.252945899963379, Test Loss = 0.08144757896661758\n",
            "Epoch 323: Loss = 53.438255310058594, Test Loss = 0.08288680762052536\n",
            "Epoch 324: Loss = 51.359764099121094, Test Loss = 0.08158877491950989\n",
            "Epoch 325: Loss = 12.408169746398926, Test Loss = 0.0830901712179184\n",
            "Epoch 326: Loss = 2.3416926860809326, Test Loss = 0.08261389285326004\n",
            "Epoch 327: Loss = 28.006418228149414, Test Loss = 0.08336225897073746\n",
            "Epoch 328: Loss = 37.1169319152832, Test Loss = 0.08137246966362\n",
            "Epoch 329: Loss = 14.515886306762695, Test Loss = 0.08091666549444199\n",
            "Epoch 330: Loss = 0.09795824438333511, Test Loss = 0.07949084043502808\n",
            "Epoch 331: Loss = 13.281723022460938, Test Loss = 0.07947355508804321\n",
            "Epoch 332: Loss = 24.750730514526367, Test Loss = 0.078584223985672\n",
            "Epoch 333: Loss = 13.710565567016602, Test Loss = 0.07879618555307388\n",
            "Epoch 334: Loss = 0.5684951543807983, Test Loss = 0.07846959680318832\n",
            "Epoch 335: Loss = 5.588855266571045, Test Loss = 0.07850898802280426\n",
            "Epoch 336: Loss = 15.416776657104492, Test Loss = 0.07773435860872269\n",
            "Epoch 337: Loss = 11.437217712402344, Test Loss = 0.07710107415914536\n",
            "Epoch 338: Loss = 1.4679572582244873, Test Loss = 0.07630712538957596\n",
            "Epoch 339: Loss = 1.9904396533966064, Test Loss = 0.075693778693676\n",
            "Epoch 340: Loss = 9.078824043273926, Test Loss = 0.07515750825405121\n",
            "Epoch 341: Loss = 8.748550415039062, Test Loss = 0.07485844194889069\n",
            "Epoch 342: Loss = 2.0315098762512207, Test Loss = 0.07480131834745407\n",
            "Epoch 343: Loss = 0.5639625787734985, Test Loss = 0.07443543523550034\n",
            "Epoch 344: Loss = 5.087305068969727, Test Loss = 0.07409530133008957\n",
            "Epoch 345: Loss = 6.270871162414551, Test Loss = 0.073454849421978\n",
            "Epoch 346: Loss = 2.1537234783172607, Test Loss = 0.07299099117517471\n",
            "Epoch 347: Loss = 0.1254410445690155, Test Loss = 0.07216641306877136\n",
            "Epoch 348: Loss = 2.7167470455169678, Test Loss = 0.07165294885635376\n",
            "Epoch 349: Loss = 4.26851749420166, Test Loss = 0.07114900648593903\n",
            "Epoch 350: Loss = 1.9676533937454224, Test Loss = 0.07087439298629761\n",
            "Epoch 351: Loss = 0.07179589569568634, Test Loss = 0.07034885883331299\n",
            "Epoch 352: Loss = 1.3925832509994507, Test Loss = 0.06988026946783066\n",
            "Epoch 353: Loss = 2.814929962158203, Test Loss = 0.06936819106340408\n",
            "Epoch 354: Loss = 1.633401870727539, Test Loss = 0.06864602118730545\n",
            "Epoch 355: Loss = 0.12345166504383087, Test Loss = 0.06787758320569992\n",
            "Epoch 356: Loss = 0.6920595169067383, Test Loss = 0.06691092252731323\n",
            "Epoch 357: Loss = 1.8046448230743408, Test Loss = 0.055604007095098495\n",
            "Epoch 358: Loss = 108.80257415771484, Test Loss = 0.08047421276569366\n",
            "Epoch 359: Loss = 20.585716247558594, Test Loss = 0.07477912306785583\n",
            "Epoch 360: Loss = 52.534278869628906, Test Loss = 0.08327687531709671\n",
            "Epoch 361: Loss = 34.94496536254883, Test Loss = 0.07797326147556305\n",
            "Epoch 362: Loss = 2.65924072265625, Test Loss = 0.08298797160387039\n",
            "Epoch 363: Loss = 9.735302925109863, Test Loss = 0.07766012847423553\n",
            "Epoch 364: Loss = 32.876182556152344, Test Loss = 0.08092836290597916\n",
            "Epoch 365: Loss = 25.81863784790039, Test Loss = 0.07799669355154037\n",
            "Epoch 366: Loss = 3.350538730621338, Test Loss = 0.08135105669498444\n",
            "Epoch 367: Loss = 4.412745475769043, Test Loss = 0.07826481759548187\n",
            "Epoch 368: Loss = 20.26858139038086, Test Loss = 0.08033760637044907\n",
            "Epoch 369: Loss = 18.45273208618164, Test Loss = 0.07786834239959717\n",
            "Epoch 370: Loss = 3.4165279865264893, Test Loss = 0.07948563247919083\n",
            "Epoch 371: Loss = 1.9214050769805908, Test Loss = 0.07696093618869781\n",
            "Epoch 372: Loss = 12.4031343460083, Test Loss = 0.07840033620595932\n",
            "Epoch 373: Loss = 12.88184928894043, Test Loss = 0.07732956856489182\n",
            "Epoch 374: Loss = 3.0623295307159424, Test Loss = 0.07888481765985489\n",
            "Epoch 375: Loss = 0.8173121213912964, Test Loss = 0.07735899090766907\n",
            "Epoch 376: Loss = 7.560357570648193, Test Loss = 0.07798197865486145\n",
            "Epoch 377: Loss = 8.852154731750488, Test Loss = 0.07663482427597046\n",
            "Epoch 378: Loss = 2.5435595512390137, Test Loss = 0.07697471976280212\n",
            "Epoch 379: Loss = 0.3509504795074463, Test Loss = 0.07545757293701172\n",
            "Epoch 380: Loss = 4.6349263191223145, Test Loss = 0.0757436752319336\n",
            "Epoch 381: Loss = 5.978353023529053, Test Loss = 0.07523108273744583\n",
            "Epoch 382: Loss = 1.9795105457305908, Test Loss = 0.0758005678653717\n",
            "Epoch 383: Loss = 0.16673928499221802, Test Loss = 0.07504016906023026\n",
            "Epoch 384: Loss = 2.842906951904297, Test Loss = 0.07518398761749268\n",
            "Epoch 385: Loss = 4.014794826507568, Test Loss = 0.07468429952859879\n",
            "Epoch 386: Loss = 1.4863300323486328, Test Loss = 0.0746825709939003\n",
            "Epoch 387: Loss = 0.09834589064121246, Test Loss = 0.07390633970499039\n",
            "Epoch 388: Loss = 1.7827860116958618, Test Loss = 0.07385746389627457\n",
            "Epoch 389: Loss = 2.672820806503296, Test Loss = 0.07367550581693649\n",
            "Epoch 390: Loss = 1.0893837213516235, Test Loss = 0.07376377284526825\n",
            "Epoch 391: Loss = 0.07613355666399002, Test Loss = 0.07339917123317719\n",
            "Epoch 392: Loss = 1.131922960281372, Test Loss = 0.07330375909805298\n",
            "Epoch 393: Loss = 1.7872247695922852, Test Loss = 0.07309923321008682\n",
            "Epoch 394: Loss = 0.7818536758422852, Test Loss = 0.07288915663957596\n",
            "Epoch 395: Loss = 0.06881826370954514, Test Loss = 0.07251454889774323\n",
            "Epoch 396: Loss = 0.7399695515632629, Test Loss = 0.0723583921790123\n",
            "Epoch 397: Loss = 1.2064706087112427, Test Loss = 0.07234684377908707\n",
            "Epoch 398: Loss = 0.5511555075645447, Test Loss = 0.07227179408073425\n",
            "Epoch 399: Loss = 0.0665220320224762, Test Loss = 0.07215698808431625\n",
            "Epoch 400: Loss = 0.5000989437103271, Test Loss = 0.0720311775803566\n",
            "Epoch 401: Loss = 0.8149719834327698, Test Loss = 0.07197091728448868\n",
            "Epoch 402: Loss = 0.39198440313339233, Test Loss = 0.07171495258808136\n",
            "Epoch 403: Loss = 0.06540952622890472, Test Loss = 0.07151640206575394\n",
            "Epoch 404: Loss = 0.3538557291030884, Test Loss = 0.07135514169931412\n",
            "Epoch 405: Loss = 0.5565141439437866, Test Loss = 0.07138481736183167\n",
            "Epoch 406: Loss = 0.274610310792923, Test Loss = 0.07126021385192871\n",
            "Epoch 407: Loss = 0.06467527151107788, Test Loss = 0.0712062418460846\n",
            "Epoch 408: Loss = 0.2566233277320862, Test Loss = 0.0710899606347084\n",
            "Epoch 409: Loss = 0.38775885105133057, Test Loss = 0.0710308775305748\n",
            "Epoch 410: Loss = 0.19698978960514069, Test Loss = 0.07078798115253448\n",
            "Epoch 411: Loss = 0.06415794044733047, Test Loss = 0.07063504308462143\n",
            "Epoch 412: Loss = 0.19441498816013336, Test Loss = 0.07050233334302902\n",
            "Epoch 413: Loss = 0.2775648534297943, Test Loss = 0.07043313980102539\n",
            "Epoch 414: Loss = 0.14703036844730377, Test Loss = 0.07024737447500229\n",
            "Epoch 415: Loss = 0.06354869902133942, Test Loss = 0.0701104924082756\n",
            "Epoch 416: Loss = 0.15353475511074066, Test Loss = 0.06996651738882065\n",
            "Epoch 417: Loss = 0.2046307623386383, Test Loss = 0.06980419903993607\n",
            "Epoch 418: Loss = 0.11452534049749374, Test Loss = 0.06952724605798721\n",
            "Epoch 419: Loss = 0.0629969984292984, Test Loss = 0.06926609575748444\n",
            "Epoch 420: Loss = 0.12667539715766907, Test Loss = 0.06906682252883911\n",
            "Epoch 421: Loss = 0.15205927193164825, Test Loss = 0.06883673369884491\n",
            "Epoch 422: Loss = 0.09137451648712158, Test Loss = 0.06853773444890976\n",
            "Epoch 423: Loss = 0.06260079145431519, Test Loss = 0.0682615116238594\n",
            "Epoch 424: Loss = 0.10443393886089325, Test Loss = 0.06799832731485367\n",
            "Epoch 425: Loss = 0.11659915000200272, Test Loss = 0.06767406314611435\n",
            "Epoch 426: Loss = 0.07663407921791077, Test Loss = 0.06735820323228836\n",
            "Epoch 427: Loss = 0.06179146096110344, Test Loss = 0.06707227975130081\n",
            "Epoch 428: Loss = 0.0895780399441719, Test Loss = 0.06680038571357727\n",
            "Epoch 429: Loss = 0.09464922547340393, Test Loss = 0.06649000197649002\n",
            "Epoch 430: Loss = 0.06790868937969208, Test Loss = 0.06619467586278915\n",
            "Epoch 431: Loss = 0.06118004396557808, Test Loss = 0.06590886414051056\n",
            "Epoch 432: Loss = 0.08041801303625107, Test Loss = 0.06560201942920685\n",
            "Epoch 433: Loss = 0.08099808543920517, Test Loss = 0.0652545616030693\n",
            "Epoch 434: Loss = 0.06236846745014191, Test Loss = 0.06491564959287643\n",
            "Epoch 435: Loss = 0.06094355881214142, Test Loss = 0.0645904466509819\n",
            "Epoch 436: Loss = 0.07350476086139679, Test Loss = 0.0642600730061531\n",
            "Epoch 437: Loss = 0.07169423997402191, Test Loss = 0.06394530832767487\n",
            "Epoch 438: Loss = 0.05920621380209923, Test Loss = 0.06365656107664108\n",
            "Epoch 439: Loss = 0.060558389872312546, Test Loss = 0.06338508427143097\n",
            "Epoch 440: Loss = 0.0673467293381691, Test Loss = 0.06308043748140335\n",
            "Epoch 441: Loss = 0.06507168710231781, Test Loss = 0.06277148425579071\n",
            "Epoch 442: Loss = 0.057847317308187485, Test Loss = 0.06248730048537254\n",
            "Epoch 443: Loss = 0.059087157249450684, Test Loss = 0.06222159415483475\n",
            "Epoch 444: Loss = 0.06367989629507065, Test Loss = 0.061954643577337265\n",
            "Epoch 445: Loss = 0.06173359602689743, Test Loss = 0.06171251833438873\n",
            "Epoch 446: Loss = 0.05682889744639397, Test Loss = 0.061485372483730316\n",
            "Epoch 447: Loss = 0.0575801357626915, Test Loss = 0.06124752759933472\n",
            "Epoch 448: Loss = 0.0605299137532711, Test Loss = 0.06100019812583923\n",
            "Epoch 449: Loss = 0.059403516352176666, Test Loss = 0.06076246127486229\n",
            "Epoch 450: Loss = 0.05605083703994751, Test Loss = 0.06053121015429497\n",
            "Epoch 451: Loss = 0.056244831532239914, Test Loss = 0.06028570979833603\n",
            "Epoch 452: Loss = 0.05794256180524826, Test Loss = 0.06004457175731659\n",
            "Epoch 453: Loss = 0.057515427470207214, Test Loss = 0.0598137266933918\n",
            "Epoch 454: Loss = 0.055452197790145874, Test Loss = 0.05958309769630432\n",
            "Epoch 455: Loss = 0.05511393025517464, Test Loss = 0.0593409426510334\n",
            "Epoch 456: Loss = 0.05623466148972511, Test Loss = 0.05910357087850571\n",
            "Epoch 457: Loss = 0.05611319839954376, Test Loss = 0.058877501636743546\n",
            "Epoch 458: Loss = 0.05475851520895958, Test Loss = 0.058658160269260406\n",
            "Epoch 459: Loss = 0.05422750860452652, Test Loss = 0.05843629688024521\n",
            "Epoch 460: Loss = 0.05486452579498291, Test Loss = 0.05822521820664406\n",
            "Epoch 461: Loss = 0.05496765673160553, Test Loss = 0.05801843851804733\n",
            "Epoch 462: Loss = 0.054096147418022156, Test Loss = 0.057810232043266296\n",
            "Epoch 463: Loss = 0.05352238938212395, Test Loss = 0.057607464492321014\n",
            "Epoch 464: Loss = 0.05382753908634186, Test Loss = 0.05741129815578461\n",
            "Epoch 465: Loss = 0.05397579073905945, Test Loss = 0.05723494291305542\n",
            "Epoch 466: Loss = 0.05348091945052147, Test Loss = 0.05705360323190689\n",
            "Epoch 467: Loss = 0.052950046956539154, Test Loss = 0.05686488375067711\n",
            "Epoch 468: Loss = 0.052997007966041565, Test Loss = 0.05666753649711609\n",
            "Epoch 469: Loss = 0.053125083446502686, Test Loss = 0.0564611591398716\n",
            "Epoch 470: Loss = 0.05283358320593834, Test Loss = 0.05625976622104645\n",
            "Epoch 471: Loss = 0.05242251977324486, Test Loss = 0.05606933683156967\n",
            "Epoch 472: Loss = 0.052314870059490204, Test Loss = 0.0558842308819294\n",
            "Epoch 473: Loss = 0.052378099411726, Test Loss = 0.055702343583106995\n",
            "Epoch 474: Loss = 0.05224736034870148, Test Loss = 0.05552614852786064\n",
            "Epoch 475: Loss = 0.05192187428474426, Test Loss = 0.05535120889544487\n",
            "Epoch 476: Loss = 0.05174721032381058, Test Loss = 0.055175408720970154\n",
            "Epoch 477: Loss = 0.05175221338868141, Test Loss = 0.05500750243663788\n",
            "Epoch 478: Loss = 0.051662784069776535, Test Loss = 0.054849132895469666\n",
            "Epoch 479: Loss = 0.05144292488694191, Test Loss = 0.05469249561429024\n",
            "Epoch 480: Loss = 0.05125081166625023, Test Loss = 0.05454397574067116\n",
            "Epoch 481: Loss = 0.0511983186006546, Test Loss = 0.05440473183989525\n",
            "Epoch 482: Loss = 0.05112778767943382, Test Loss = 0.05426656827330589\n",
            "Epoch 483: Loss = 0.05097918584942818, Test Loss = 0.05412216857075691\n",
            "Epoch 484: Loss = 0.050802990794181824, Test Loss = 0.05397622659802437\n",
            "Epoch 485: Loss = 0.05070823058485985, Test Loss = 0.053832367062568665\n",
            "Epoch 486: Loss = 0.05063943192362785, Test Loss = 0.053665775805711746\n",
            "Epoch 487: Loss = 0.05052809417247772, Test Loss = 0.05348140746355057\n",
            "Epoch 488: Loss = 0.050373438745737076, Test Loss = 0.05330037698149681\n",
            "Epoch 489: Loss = 0.050250839442014694, Test Loss = 0.05311269313097\n",
            "Epoch 490: Loss = 0.05015650391578674, Test Loss = 0.052911143749952316\n",
            "Epoch 491: Loss = 0.04999828338623047, Test Loss = 0.052697207778692245\n",
            "Epoch 492: Loss = 0.04979975149035454, Test Loss = 0.05247161164879799\n",
            "Epoch 493: Loss = 0.04958876594901085, Test Loss = 0.05222943052649498\n",
            "Epoch 494: Loss = 0.04937636852264404, Test Loss = 0.05195780470967293\n",
            "Epoch 495: Loss = 0.04913711920380592, Test Loss = 0.0516679473221302\n",
            "Epoch 496: Loss = 0.04887405410408974, Test Loss = 0.0513741709291935\n",
            "Epoch 497: Loss = 0.048606861382722855, Test Loss = 0.051081303507089615\n",
            "Epoch 498: Loss = 0.04834908992052078, Test Loss = 0.05080672353506088\n",
            "Epoch 499: Loss = 0.04809688776731491, Test Loss = 0.05055103078484535\n",
            "Epoch 500: Loss = 0.047834113240242004, Test Loss = 0.05030132830142975\n",
            "Epoch 501: Loss = 0.0475674569606781, Test Loss = 0.05006277561187744\n",
            "Epoch 502: Loss = 0.047314539551734924, Test Loss = 0.049828171730041504\n",
            "Epoch 503: Loss = 0.04707471653819084, Test Loss = 0.049593809992074966\n",
            "Epoch 504: Loss = 0.046837203204631805, Test Loss = 0.04936986416578293\n",
            "Epoch 505: Loss = 0.04660611227154732, Test Loss = 0.04915813356637955\n",
            "Epoch 506: Loss = 0.04637754708528519, Test Loss = 0.04896429181098938\n",
            "Epoch 507: Loss = 0.04616626352071762, Test Loss = 0.04873050004243851\n",
            "Epoch 508: Loss = 0.04596905782818794, Test Loss = 0.048504170030355453\n",
            "Epoch 509: Loss = 0.04577174037694931, Test Loss = 0.0482717789709568\n",
            "Epoch 510: Loss = 0.045576080679893494, Test Loss = 0.048030201345682144\n",
            "Epoch 511: Loss = 0.0453939326107502, Test Loss = 0.04779608175158501\n",
            "Epoch 512: Loss = 0.04522963985800743, Test Loss = 0.04767415300011635\n",
            "Epoch 513: Loss = 0.04501119628548622, Test Loss = 0.04757813736796379\n",
            "Epoch 514: Loss = 0.044659025967121124, Test Loss = 0.04746808484196663\n",
            "Epoch 515: Loss = 0.044215865433216095, Test Loss = 0.04730284586548805\n",
            "Epoch 516: Loss = 0.043722476810216904, Test Loss = 0.047092899680137634\n",
            "Epoch 517: Loss = 0.043235667049884796, Test Loss = 0.04687437415122986\n",
            "Epoch 518: Loss = 0.04278305172920227, Test Loss = 0.04677977040410042\n",
            "Epoch 519: Loss = 0.042345356196165085, Test Loss = 0.04668258875608444\n",
            "Epoch 520: Loss = 0.04195934534072876, Test Loss = 0.04649784043431282\n",
            "Epoch 521: Loss = 0.04161706566810608, Test Loss = 0.04626745358109474\n",
            "Epoch 522: Loss = 0.04125339537858963, Test Loss = 0.04585883766412735\n",
            "Epoch 523: Loss = 0.04045229032635689, Test Loss = 0.045495372265577316\n",
            "Epoch 524: Loss = 0.0395825058221817, Test Loss = 0.04549834504723549\n",
            "Epoch 525: Loss = 0.03883799910545349, Test Loss = 0.04533199220895767\n",
            "Epoch 526: Loss = 0.03823859617114067, Test Loss = 0.045208144932985306\n",
            "Epoch 527: Loss = 0.037749033421278, Test Loss = 0.0536821223795414\n",
            "Epoch 528: Loss = 486.98577880859375, Test Loss = 0.053347148001194\n",
            "Epoch 529: Loss = 213.51568603515625, Test Loss = 0.04590544477105141\n",
            "Epoch 530: Loss = 327.1213073730469, Test Loss = 0.07030756771564484\n",
            "Epoch 531: Loss = 31081.9609375, Test Loss = 0.10319484025239944\n",
            "Epoch 532: Loss = 13904.34765625, Test Loss = 0.0598350390791893\n",
            "Epoch 533: Loss = 18886.92578125, Test Loss = 0.10397006571292877\n",
            "Epoch 534: Loss = 13507.4775390625, Test Loss = 0.07705843448638916\n",
            "Epoch 535: Loss = 2758.236328125, Test Loss = 0.09018126875162125\n",
            "Epoch 536: Loss = 2436.5927734375, Test Loss = 0.0793951079249382\n",
            "Epoch 537: Loss = 31.832868576049805, Test Loss = 0.0978221595287323\n",
            "Epoch 538: Loss = 15023.3115234375, Test Loss = 0.10273932665586472\n",
            "Epoch 539: Loss = 155.6367950439453, Test Loss = 0.10009673237800598\n",
            "Epoch 540: Loss = 946.8302001953125, Test Loss = 0.09403949975967407\n",
            "Epoch 541: Loss = 2454.068115234375, Test Loss = 0.11056743562221527\n",
            "Epoch 542: Loss = 1148.87646484375, Test Loss = 0.08105967938899994\n",
            "Epoch 543: Loss = 5.90969181060791, Test Loss = 0.10769668221473694\n",
            "Epoch 544: Loss = 1122.07421875, Test Loss = 0.07394599169492722\n",
            "Epoch 545: Loss = 1563.830810546875, Test Loss = 0.09672299772500992\n",
            "Epoch 546: Loss = 365.2127685546875, Test Loss = 0.07210947573184967\n",
            "Epoch 547: Loss = 140.3378143310547, Test Loss = 0.0868346244096756\n",
            "Epoch 548: Loss = 1021.047607421875, Test Loss = 0.07240226864814758\n",
            "Epoch 549: Loss = 842.8892822265625, Test Loss = 0.07454520463943481\n",
            "Epoch 550: Loss = 6608.4287109375, Test Loss = 0.06863206624984741\n",
            "Epoch 551: Loss = 1720.0899658203125, Test Loss = 0.07699860632419586\n",
            "Epoch 552: Loss = 2019.239990234375, Test Loss = 0.07236067205667496\n",
            "Epoch 553: Loss = 339.6765441894531, Test Loss = 0.07827069610357285\n",
            "Epoch 554: Loss = 308.4308166503906, Test Loss = 0.0794522762298584\n",
            "Epoch 555: Loss = 1474.026123046875, Test Loss = 0.08058135956525803\n",
            "Epoch 556: Loss = 1018.0852661132812, Test Loss = 0.08107513189315796\n",
            "Epoch 557: Loss = 25.596302032470703, Test Loss = 0.07738595455884933\n",
            "Epoch 558: Loss = 505.7032775878906, Test Loss = 0.07976476848125458\n",
            "Epoch 559: Loss = 1052.1168212890625, Test Loss = 0.07401067763566971\n",
            "Epoch 560: Loss = 396.751220703125, Test Loss = 0.07467124611139297\n",
            "Epoch 561: Loss = 20.619598388671875, Test Loss = 0.07040966302156448\n",
            "Epoch 562: Loss = 554.4625854492188, Test Loss = 0.06928327679634094\n",
            "Epoch 563: Loss = 629.4664306640625, Test Loss = 0.06640008091926575\n",
            "Epoch 564: Loss = 97.37682342529297, Test Loss = 0.0640660747885704\n",
            "Epoch 565: Loss = 108.18807220458984, Test Loss = 0.06232881918549538\n",
            "Epoch 566: Loss = 473.1075134277344, Test Loss = 0.05967750400304794\n",
            "Epoch 567: Loss = 306.18505859375, Test Loss = 0.0634445920586586\n",
            "Epoch 568: Loss = 1761.0244140625, Test Loss = 0.061743807047605515\n",
            "Epoch 569: Loss = 256.9206848144531, Test Loss = 0.06473679095506668\n",
            "Epoch 570: Loss = 322.6912841796875, Test Loss = 0.06403937935829163\n",
            "Epoch 571: Loss = 59.52397155761719, Test Loss = 0.06646256148815155\n",
            "Epoch 572: Loss = 44.52768325805664, Test Loss = 0.0627993568778038\n",
            "Epoch 573: Loss = 230.0700225830078, Test Loss = 0.06582754105329514\n",
            "Epoch 574: Loss = 160.9281768798828, Test Loss = 0.06255018711090088\n",
            "Epoch 575: Loss = 3.882266044616699, Test Loss = 0.06374862790107727\n",
            "Epoch 576: Loss = 81.45728302001953, Test Loss = 0.06165696308016777\n",
            "Epoch 577: Loss = 165.34664916992188, Test Loss = 0.0608447827398777\n",
            "Epoch 578: Loss = 58.84576416015625, Test Loss = 0.06093372777104378\n",
            "Epoch 579: Loss = 4.737918853759766, Test Loss = 0.058966558426618576\n",
            "Epoch 580: Loss = 91.43204498291016, Test Loss = 0.05859827622771263\n",
            "Epoch 581: Loss = 95.85100555419922, Test Loss = 0.056738756597042084\n",
            "Epoch 582: Loss = 11.541590690612793, Test Loss = 0.05711055174469948\n",
            "Epoch 583: Loss = 21.79910659790039, Test Loss = 0.05529066175222397\n",
            "Epoch 584: Loss = 76.85909271240234, Test Loss = 0.05634107068181038\n",
            "Epoch 585: Loss = 42.6285514831543, Test Loss = 0.05506265535950661\n",
            "Epoch 586: Loss = 0.056633688509464264, Test Loss = 0.054481931030750275\n",
            "Epoch 587: Loss = 58.88100051879883, Test Loss = 0.06751415133476257\n",
            "Epoch 588: Loss = 34.10222625732422, Test Loss = 0.061539482325315475\n",
            "Epoch 589: Loss = 3.782228708267212, Test Loss = 0.06699325144290924\n",
            "Epoch 590: Loss = 8.47976016998291, Test Loss = 0.06117638573050499\n",
            "Epoch 591: Loss = 27.905237197875977, Test Loss = 0.0638941302895546\n",
            "Epoch 592: Loss = 14.659231185913086, Test Loss = 0.06260023266077042\n",
            "Epoch 593: Loss = 0.06561087816953659, Test Loss = 0.06324401497840881\n",
            "Epoch 594: Loss = 12.773736953735352, Test Loss = 0.06356657296419144\n",
            "Epoch 595: Loss = 18.101097106933594, Test Loss = 0.06098959222435951\n",
            "Epoch 596: Loss = 3.876781940460205, Test Loss = 0.062054574489593506\n",
            "Epoch 597: Loss = 2.167105197906494, Test Loss = 0.05862630903720856\n",
            "Epoch 598: Loss = 12.495109558105469, Test Loss = 0.060086071491241455\n",
            "Epoch 599: Loss = 8.900218963623047, Test Loss = 0.057466570287942886\n",
            "Epoch 600: Loss = 0.22944335639476776, Test Loss = 0.05891196057200432\n",
            "Epoch 601: Loss = 4.726040840148926, Test Loss = 0.05794629082083702\n",
            "Epoch 602: Loss = 9.062153816223145, Test Loss = 0.058324236422777176\n",
            "Epoch 603: Loss = 2.8990848064422607, Test Loss = 0.057954564690589905\n",
            "Epoch 604: Loss = 0.4907037615776062, Test Loss = 0.05680279806256294\n",
            "Epoch 605: Loss = 5.429748058319092, Test Loss = 0.05672983080148697\n",
            "Epoch 606: Loss = 4.915392875671387, Test Loss = 0.05512933060526848\n",
            "Epoch 607: Loss = 0.37988388538360596, Test Loss = 0.055386412888765335\n",
            "Epoch 608: Loss = 1.7177540063858032, Test Loss = 0.05416363477706909\n",
            "Epoch 609: Loss = 4.367633819580078, Test Loss = 0.054457686841487885\n",
            "Epoch 610: Loss = 1.845975399017334, Test Loss = 0.05378078669309616\n",
            "Epoch 611: Loss = 0.11157926917076111, Test Loss = 0.05365218594670296\n",
            "Epoch 612: Loss = 2.349583387374878, Test Loss = 0.053213492035865784\n",
            "Epoch 613: Loss = 2.593656539916992, Test Loss = 0.052514173090457916\n",
            "Epoch 614: Loss = 0.3485768735408783, Test Loss = 0.05237777531147003\n",
            "Epoch 615: Loss = 0.6457158923149109, Test Loss = 0.05172044038772583\n",
            "Epoch 616: Loss = 2.08563494682312, Test Loss = 0.05179199203848839\n",
            "Epoch 617: Loss = 1.074648141860962, Test Loss = 0.05121238902211189\n",
            "Epoch 618: Loss = 0.0475376732647419, Test Loss = 0.051188766956329346\n",
            "Epoch 619: Loss = 1.0411150455474854, Test Loss = 0.05070694163441658\n",
            "Epoch 620: Loss = 1.3292816877365112, Test Loss = 0.050426557660102844\n",
            "Epoch 621: Loss = 0.24977044761180878, Test Loss = 0.0498846210539341\n",
            "Epoch 622: Loss = 0.2684675455093384, Test Loss = 0.04933466389775276\n",
            "Epoch 623: Loss = 1.004382610321045, Test Loss = 0.048941195011138916\n",
            "Epoch 624: Loss = 0.5896930694580078, Test Loss = 0.04845958575606346\n",
            "Epoch 625: Loss = 0.04088963568210602, Test Loss = 0.048240501433610916\n",
            "Epoch 626: Loss = 0.4856967031955719, Test Loss = 0.047794945538043976\n",
            "Epoch 627: Loss = 0.6738252639770508, Test Loss = 0.047631699591875076\n",
            "Epoch 628: Loss = 0.16274461150169373, Test Loss = 0.04729587957262993\n",
            "Epoch 629: Loss = 0.1319463700056076, Test Loss = 0.047144509851932526\n",
            "Epoch 630: Loss = 0.49844083189964294, Test Loss = 0.046828582882881165\n",
            "Epoch 631: Loss = 0.3222005069255829, Test Loss = 0.046625133603811264\n",
            "Epoch 632: Loss = 0.0395660474896431, Test Loss = 0.046393703669309616\n",
            "Epoch 633: Loss = 0.24709096550941467, Test Loss = 0.046219490468502045\n",
            "Epoch 634: Loss = 0.35272088646888733, Test Loss = 0.0460396409034729\n",
            "Epoch 635: Loss = 0.10339489579200745, Test Loss = 0.045811764895915985\n",
            "Epoch 636: Loss = 0.08108682185411453, Test Loss = 0.045626141130924225\n",
            "Epoch 637: Loss = 0.26020073890686035, Test Loss = 0.04538305103778839\n",
            "Epoch 638: Loss = 0.17657817900180817, Test Loss = 0.04518161714076996\n",
            "Epoch 639: Loss = 0.03795203939080238, Test Loss = 0.04488970339298248\n",
            "Epoch 640: Loss = 0.13862068951129913, Test Loss = 0.0446651428937912\n",
            "Epoch 641: Loss = 0.18791045248508453, Test Loss = 0.044417183846235275\n",
            "Epoch 642: Loss = 0.06604766100645065, Test Loss = 0.04424016550183296\n",
            "Epoch 643: Loss = 0.05901684612035751, Test Loss = 0.0440252460539341\n",
            "Epoch 644: Loss = 0.14268840849399567, Test Loss = 0.04384562745690346\n",
            "Epoch 645: Loss = 0.09833282232284546, Test Loss = 0.043666280806064606\n",
            "Epoch 646: Loss = 0.036369334906339645, Test Loss = 0.04351236671209335\n",
            "Epoch 647: Loss = 0.08655963093042374, Test Loss = 0.04334884509444237\n",
            "Epoch 648: Loss = 0.10344741493463516, Test Loss = 0.04317126050591469\n",
            "Epoch 649: Loss = 0.04735604673624039, Test Loss = 0.04300624132156372\n",
            "Epoch 650: Loss = 0.04810719192028046, Test Loss = 0.04283686727285385\n",
            "Epoch 651: Loss = 0.08602280169725418, Test Loss = 0.0426807701587677\n",
            "Epoch 652: Loss = 0.06347319483757019, Test Loss = 0.04250204935669899\n",
            "Epoch 653: Loss = 0.03517824411392212, Test Loss = 0.04234033077955246\n",
            "Epoch 654: Loss = 0.06064599007368088, Test Loss = 0.042169880121946335\n",
            "Epoch 655: Loss = 0.06681525707244873, Test Loss = 0.04200926795601845\n",
            "Epoch 656: Loss = 0.03904639557003975, Test Loss = 0.041825857013463974\n",
            "Epoch 657: Loss = 0.040885407477617264, Test Loss = 0.041644539684057236\n",
            "Epoch 658: Loss = 0.05874526500701904, Test Loss = 0.04146641492843628\n",
            "Epoch 659: Loss = 0.04742404446005821, Test Loss = 0.04130399227142334\n",
            "Epoch 660: Loss = 0.034075766801834106, Test Loss = 0.04113013297319412\n",
            "Epoch 661: Loss = 0.04827546700835228, Test Loss = 0.04096996411681175\n",
            "Epoch 662: Loss = 0.04963964223861694, Test Loss = 0.04081946611404419\n",
            "Epoch 663: Loss = 0.034944284707307816, Test Loss = 0.040678393095731735\n",
            "Epoch 664: Loss = 0.038108061999082565, Test Loss = 0.04053623601794243\n",
            "Epoch 665: Loss = 0.04524323716759682, Test Loss = 0.04038194566965103\n",
            "Epoch 666: Loss = 0.03824082389473915, Test Loss = 0.04022747278213501\n",
            "Epoch 667: Loss = 0.03312242403626442, Test Loss = 0.040072061121463776\n",
            "Epoch 668: Loss = 0.0405186302959919, Test Loss = 0.039915457367897034\n",
            "Epoch 669: Loss = 0.03974393010139465, Test Loss = 0.039755500853061676\n",
            "Epoch 670: Loss = 0.03259889781475067, Test Loss = 0.03954441100358963\n",
            "Epoch 671: Loss = 0.03545110672712326, Test Loss = 0.03932376578450203\n",
            "Epoch 672: Loss = 0.03774801641702652, Test Loss = 0.039108168333768845\n",
            "Epoch 673: Loss = 0.03371863439679146, Test Loss = 0.03888532891869545\n",
            "Epoch 674: Loss = 0.03197827562689781, Test Loss = 0.0386645570397377\n",
            "Epoch 675: Loss = 0.03494475036859512, Test Loss = 0.03844669833779335\n",
            "Epoch 676: Loss = 0.03450646623969078, Test Loss = 0.03823321685194969\n",
            "Epoch 677: Loss = 0.03140399977564812, Test Loss = 0.03801775723695755\n",
            "Epoch 678: Loss = 0.03206051513552666, Test Loss = 0.0377778522670269\n",
            "Epoch 679: Loss = 0.03339128568768501, Test Loss = 0.0375327542424202\n",
            "Epoch 680: Loss = 0.03196995332837105, Test Loss = 0.037295062094926834\n",
            "Epoch 681: Loss = 0.030441518872976303, Test Loss = 0.037058666348457336\n",
            "Epoch 682: Loss = 0.03161698952317238, Test Loss = 0.03682290017604828\n",
            "Epoch 683: Loss = 0.031790591776371, Test Loss = 0.036582332104444504\n",
            "Epoch 684: Loss = 0.0301919337362051, Test Loss = 0.03634164482355118\n",
            "Epoch 685: Loss = 0.029803557321429253, Test Loss = 0.03610388934612274\n",
            "Epoch 686: Loss = 0.030435306951403618, Test Loss = 0.035874057561159134\n",
            "Epoch 687: Loss = 0.030039044097065926, Test Loss = 0.035651396960020065\n",
            "Epoch 688: Loss = 0.028934014961123466, Test Loss = 0.03511813282966614\n",
            "Epoch 689: Loss = 0.028826838359236717, Test Loss = 0.03450449928641319\n",
            "Epoch 690: Loss = 0.029116246849298477, Test Loss = 0.0339159332215786\n",
            "Epoch 691: Loss = 0.02843041904270649, Test Loss = 0.033344488590955734\n",
            "Epoch 692: Loss = 0.027642497792840004, Test Loss = 0.03278835490345955\n",
            "Epoch 693: Loss = 0.027516400441527367, Test Loss = 0.03176511451601982\n",
            "Epoch 694: Loss = 0.02719191648066044, Test Loss = 0.031874749809503555\n",
            "Epoch 695: Loss = 0.027174612507224083, Test Loss = 0.031634047627449036\n",
            "Epoch 696: Loss = 0.026253825053572655, Test Loss = 0.03247364982962608\n",
            "Epoch 697: Loss = 0.026731114834547043, Test Loss = 0.032640259712934494\n",
            "Epoch 698: Loss = 0.026855334639549255, Test Loss = 0.031733497977256775\n",
            "Epoch 699: Loss = 0.026090508326888084, Test Loss = 0.031295403838157654\n",
            "Epoch 700: Loss = 0.026044335216283798, Test Loss = 0.031226571649312973\n",
            "Epoch 701: Loss = 0.02619495987892151, Test Loss = 0.031250789761543274\n",
            "Epoch 702: Loss = 0.025831017643213272, Test Loss = 0.03184284642338753\n",
            "Epoch 703: Loss = 0.025752639397978783, Test Loss = 0.031502578407526016\n",
            "Epoch 704: Loss = 0.02551734447479248, Test Loss = 0.030716631561517715\n",
            "Epoch 705: Loss = 0.02541409619152546, Test Loss = 0.030630165711045265\n",
            "Epoch 706: Loss = 0.025585273280739784, Test Loss = 0.030551157891750336\n",
            "Epoch 707: Loss = 20.965930938720703, Test Loss = 0.040844302624464035\n",
            "Epoch 708: Loss = 12.47856616973877, Test Loss = 0.035084277391433716\n",
            "Epoch 709: Loss = 15.956082344055176, Test Loss = 0.04752583056688309\n",
            "Epoch 710: Loss = 1.8102622032165527, Test Loss = 0.04850253835320473\n",
            "Epoch 711: Loss = 4.41748571395874, Test Loss = 0.05544451251626015\n",
            "Epoch 712: Loss = 12.725706100463867, Test Loss = 0.05165410041809082\n",
            "Epoch 713: Loss = 4.561339378356934, Test Loss = 0.055168863385915756\n",
            "Epoch 714: Loss = 0.7044565081596375, Test Loss = 0.05198395252227783\n",
            "Epoch 715: Loss = 8.049583435058594, Test Loss = 0.05437091737985611\n",
            "Epoch 716: Loss = 6.053960800170898, Test Loss = 0.05195479467511177\n",
            "Epoch 717: Loss = 0.08965691924095154, Test Loss = 0.05323108658194542\n",
            "Epoch 718: Loss = 3.949455738067627, Test Loss = 0.05171476677060127\n",
            "Epoch 719: Loss = 5.81463623046875, Test Loss = 0.05300305411219597\n",
            "Epoch 720: Loss = 0.8890898823738098, Test Loss = 0.05244675651192665\n",
            "Epoch 721: Loss = 1.3399254083633423, Test Loss = 0.05276814103126526\n",
            "Epoch 722: Loss = 4.453200340270996, Test Loss = 0.051939357072114944\n",
            "Epoch 723: Loss = 1.8067739009857178, Test Loss = 0.051633115857839584\n",
            "Epoch 724: Loss = 0.21454660594463348, Test Loss = 0.05128457769751549\n",
            "Epoch 725: Loss = 2.763742208480835, Test Loss = 0.05104539915919304\n",
            "Epoch 726: Loss = 2.2357189655303955, Test Loss = 0.051308825612068176\n",
            "Epoch 727: Loss = 0.07490944862365723, Test Loss = 0.05131860449910164\n",
            "Epoch 728: Loss = 1.3660780191421509, Test Loss = 0.05170964077115059\n",
            "Epoch 729: Loss = 2.083164691925049, Test Loss = 0.0512540303170681\n",
            "Epoch 730: Loss = 0.3616548478603363, Test Loss = 0.05128610506653786\n",
            "Epoch 731: Loss = 0.4858907163143158, Test Loss = 0.050762683153152466\n",
            "Epoch 732: Loss = 1.5912030935287476, Test Loss = 0.05096492916345596\n",
            "Epoch 733: Loss = 0.6632781624794006, Test Loss = 0.050545837730169296\n",
            "Epoch 734: Loss = 0.10737242549657822, Test Loss = 0.04761573672294617\n",
            "Epoch 735: Loss = 1.0044220685958862, Test Loss = 0.041881315410137177\n",
            "Epoch 736: Loss = 2325.189453125, Test Loss = 0.05493004620075226\n",
            "Epoch 737: Loss = 1202.6544189453125, Test Loss = 0.05377418175339699\n",
            "Epoch 738: Loss = 2096.13330078125, Test Loss = 0.054665740579366684\n",
            "Epoch 739: Loss = 248.6199951171875, Test Loss = 0.047422051429748535\n",
            "Epoch 740: Loss = 566.4053955078125, Test Loss = 0.05031968280673027\n",
            "Epoch 741: Loss = 1643.939208984375, Test Loss = 0.04846491292119026\n",
            "Epoch 742: Loss = 545.8402709960938, Test Loss = 0.05497382953763008\n",
            "Epoch 743: Loss = 114.1036605834961, Test Loss = 0.05561602860689163\n",
            "Epoch 744: Loss = 1077.950439453125, Test Loss = 0.05660976842045784\n",
            "Epoch 745: Loss = 713.2457885742188, Test Loss = 0.05136098340153694\n",
            "Epoch 746: Loss = 0.04950406774878502, Test Loss = 0.049939606338739395\n",
            "Epoch 747: Loss = 581.8260498046875, Test Loss = 0.04847349971532822\n",
            "Epoch 748: Loss = 705.3392944335938, Test Loss = 0.051916565746068954\n",
            "Epoch 749: Loss = 56.900264739990234, Test Loss = 0.053383734077215195\n",
            "Epoch 750: Loss = 243.270263671875, Test Loss = 0.055421315133571625\n",
            "Epoch 751: Loss = 574.9935302734375, Test Loss = 0.05282478407025337\n",
            "Epoch 752: Loss = 153.5276336669922, Test Loss = 0.050998035818338394\n",
            "Epoch 753: Loss = 63.392578125, Test Loss = 0.04896806925535202\n",
            "Epoch 754: Loss = 396.94464111328125, Test Loss = 0.049618061631917953\n",
            "Epoch 755: Loss = 216.70948791503906, Test Loss = 0.051135316491127014\n",
            "Epoch 756: Loss = 3.1913278102874756, Test Loss = 0.053040511906147\n",
            "Epoch 757: Loss = 231.1682891845703, Test Loss = 0.05265835300087929\n",
            "Epoch 758: Loss = 227.98960876464844, Test Loss = 0.05114958435297012\n",
            "Epoch 759: Loss = 8.364692687988281, Test Loss = 0.0492783859372139\n",
            "Epoch 760: Loss = 109.15818786621094, Test Loss = 0.04904385656118393\n",
            "Epoch 761: Loss = 197.26792907714844, Test Loss = 0.04970908164978027\n",
            "Epoch 762: Loss = 35.55234146118164, Test Loss = 0.05109019577503204\n",
            "Epoch 763: Loss = 37.53467559814453, Test Loss = 0.051739711314439774\n",
            "Epoch 764: Loss = 145.65611267089844, Test Loss = 0.05114269629120827\n",
            "Epoch 765: Loss = 59.06822204589844, Test Loss = 0.04982661083340645\n",
            "Epoch 766: Loss = 6.181698322296143, Test Loss = 0.04886065050959587\n",
            "Epoch 767: Loss = 92.68077850341797, Test Loss = 0.048934925347566605\n",
            "Epoch 768: Loss = 68.42333984375, Test Loss = 0.049856942147016525\n",
            "Epoch 769: Loss = 0.163338765501976, Test Loss = 0.0506778322160244\n",
            "Epoch 770: Loss = 50.074031829833984, Test Loss = 0.0507136732339859\n",
            "Epoch 771: Loss = 64.23687744140625, Test Loss = 0.04993221163749695\n",
            "Epoch 772: Loss = 5.57522439956665, Test Loss = 0.04906834289431572\n",
            "Epoch 773: Loss = 21.777738571166992, Test Loss = 0.04871293157339096\n",
            "Epoch 774: Loss = 51.651756286621094, Test Loss = 0.048994243144989014\n",
            "Epoch 775: Loss = 13.015974998474121, Test Loss = 0.04967188462615013\n",
            "Epoch 776: Loss = 6.61313533782959, Test Loss = 0.05004679411649704\n",
            "Epoch 777: Loss = 36.33652114868164, Test Loss = 0.04976026341319084\n",
            "Epoch 778: Loss = 17.887802124023438, Test Loss = 0.04905476048588753\n",
            "Epoch 779: Loss = 0.773184061050415, Test Loss = 0.048539161682128906\n",
            "Epoch 780: Loss = 22.39958381652832, Test Loss = 0.04857172444462776\n",
            "Epoch 781: Loss = 18.938621520996094, Test Loss = 0.04897911474108696\n",
            "Epoch 782: Loss = 0.23593293130397797, Test Loss = 0.04937822371721268\n",
            "Epoch 783: Loss = 11.894878387451172, Test Loss = 0.04940760135650635\n",
            "Epoch 784: Loss = 16.938568115234375, Test Loss = 0.04903348162770271\n",
            "Epoch 785: Loss = 1.8165220022201538, Test Loss = 0.0485495999455452\n",
            "Epoch 786: Loss = 5.170608043670654, Test Loss = 0.04831596091389656\n",
            "Epoch 787: Loss = 13.294485092163086, Test Loss = 0.048476219177246094\n",
            "Epoch 788: Loss = 3.5782506465911865, Test Loss = 0.048827141523361206\n",
            "Epoch 789: Loss = 1.622732162475586, Test Loss = 0.048987891525030136\n",
            "Epoch 790: Loss = 9.32295036315918, Test Loss = 0.04881523922085762\n",
            "Epoch 791: Loss = 4.616316795349121, Test Loss = 0.04844256490468979\n",
            "Epoch 792: Loss = 0.24380998313426971, Test Loss = 0.048172831535339355\n",
            "Epoch 793: Loss = 5.8259596824646, Test Loss = 0.04813580960035324\n",
            "Epoch 794: Loss = 4.771805286407471, Test Loss = 0.04831256344914436\n",
            "Epoch 795: Loss = 0.07308217138051987, Test Loss = 0.04849608987569809\n",
            "Epoch 796: Loss = 3.2179112434387207, Test Loss = 0.04849487170577049\n",
            "Epoch 797: Loss = 4.268096923828125, Test Loss = 0.048261746764183044\n",
            "Epoch 798: Loss = 0.3966529667377472, Test Loss = 0.04799063503742218\n",
            "Epoch 799: Loss = 1.5232518911361694, Test Loss = 0.047868892550468445\n",
            "Epoch 800: Loss = 3.405294179916382, Test Loss = 0.04795151576399803\n",
            "Epoch 801: Loss = 0.7855353951454163, Test Loss = 0.048087362200021744\n",
            "Epoch 802: Loss = 0.5850036144256592, Test Loss = 0.04813769459724426\n",
            "Epoch 803: Loss = 2.466327428817749, Test Loss = 0.04802438244223595\n",
            "Epoch 804: Loss = 1.0420573949813843, Test Loss = 0.04782869294285774\n",
            "Epoch 805: Loss = 0.16120962798595428, Test Loss = 0.04766653850674629\n",
            "Epoch 806: Loss = 1.64175546169281, Test Loss = 0.04764606058597565\n",
            "Epoch 807: Loss = 1.1144719123840332, Test Loss = 0.047731202095746994\n",
            "Epoch 808: Loss = 0.04620466008782387, Test Loss = 0.04780475050210953\n",
            "Epoch 809: Loss = 0.998140811920166, Test Loss = 0.04775320366024971\n",
            "Epoch 810: Loss = 1.0306862592697144, Test Loss = 0.04760487750172615\n",
            "Epoch 811: Loss = 0.07580084353685379, Test Loss = 0.04745190218091011\n",
            "Epoch 812: Loss = 0.5495766401290894, Test Loss = 0.04738595709204674\n",
            "Epoch 813: Loss = 0.8779640793800354, Test Loss = 0.047404609620571136\n",
            "Epoch 814: Loss = 0.14784882962703705, Test Loss = 0.04745399206876755\n",
            "Epoch 815: Loss = 0.2755802571773529, Test Loss = 0.04744885861873627\n",
            "Epoch 816: Loss = 0.6797231435775757, Test Loss = 0.047359440475702286\n",
            "Epoch 817: Loss = 0.2110324203968048, Test Loss = 0.047227032482624054\n",
            "Epoch 818: Loss = 0.12680034339427948, Test Loss = 0.04713643342256546\n",
            "Epoch 819: Loss = 0.4930412769317627, Test Loss = 0.047124311327934265\n",
            "Epoch 820: Loss = 0.2430351972579956, Test Loss = 0.047152403742074966\n",
            "Epoch 821: Loss = 0.061718445271253586, Test Loss = 0.04715458303689957\n",
            "Epoch 822: Loss = 0.3367559015750885, Test Loss = 0.04709961265325546\n",
            "Epoch 823: Loss = 0.244310662150383, Test Loss = 0.04700103774666786\n",
            "Epoch 824: Loss = 0.044010862708091736, Test Loss = 0.04690339043736458\n",
            "Epoch 825: Loss = 0.22067400813102722, Test Loss = 0.0468473955988884\n",
            "Epoch 826: Loss = 0.22250615060329437, Test Loss = 0.04683323949575424\n",
            "Epoch 827: Loss = 0.04761126637458801, Test Loss = 0.04682274907827377\n",
            "Epoch 828: Loss = 0.1421460211277008, Test Loss = 0.04677317291498184\n",
            "Epoch 829: Loss = 0.19428187608718872, Test Loss = 0.046682123094797134\n",
            "Epoch 830: Loss = 0.057812463492155075, Test Loss = 0.04658478498458862\n",
            "Epoch 831: Loss = 0.09477575868368149, Test Loss = 0.04651663452386856\n",
            "Epoch 832: Loss = 0.16071486473083496, Test Loss = 0.046482887119054794\n",
            "Epoch 833: Loss = 0.06720087677240372, Test Loss = 0.04646151512861252\n",
            "Epoch 834: Loss = 0.06503869593143463, Test Loss = 0.04642379656434059\n",
            "Epoch 835: Loss = 0.12931932508945465, Test Loss = 0.04635746404528618\n",
            "Epoch 836: Loss = 0.07242044061422348, Test Loss = 0.04627351462841034\n",
            "Epoch 837: Loss = 0.050309084355831146, Test Loss = 0.046201229095458984\n",
            "Epoch 838: Loss = 0.10083254426717758, Test Loss = 0.04615793377161026\n",
            "Epoch 839: Loss = 0.07439357787370682, Test Loss = 0.04613849148154259\n",
            "Epoch 840: Loss = 0.044316548854112625, Test Loss = 0.046114616096019745\n",
            "Epoch 841: Loss = 0.08163464069366455, Test Loss = 0.046071793884038925\n",
            "Epoch 842: Loss = 0.07316403090953827, Test Loss = 0.04601287096738815\n",
            "Epoch 843: Loss = 0.042776256799697876, Test Loss = 0.045957330614328384\n",
            "Epoch 844: Loss = 0.0673416331410408, Test Loss = 0.045919012278318405\n",
            "Epoch 845: Loss = 0.0687030628323555, Test Loss = 0.04589683562517166\n",
            "Epoch 846: Loss = 0.04328426346182823, Test Loss = 0.0458754263818264\n",
            "Epoch 847: Loss = 0.05631992220878601, Test Loss = 0.04583803936839104\n",
            "Epoch 848: Loss = 0.062235165387392044, Test Loss = 0.04578576609492302\n",
            "Epoch 849: Loss = 0.04409617558121681, Test Loss = 0.0457332469522953\n",
            "Epoch 850: Loss = 0.04921827092766762, Test Loss = 0.04569261893630028\n",
            "Epoch 851: Loss = 0.056042883545160294, Test Loss = 0.04566437005996704\n",
            "Epoch 852: Loss = 0.0448005273938179, Test Loss = 0.04564026743173599\n",
            "Epoch 853: Loss = 0.04548795148730278, Test Loss = 0.04560957849025726\n",
            "Epoch 854: Loss = 0.051286742091178894, Test Loss = 0.04556920379400253\n",
            "Epoch 855: Loss = 0.045519959181547165, Test Loss = 0.0455208495259285\n",
            "Epoch 856: Loss = 0.04329841956496239, Test Loss = 0.04547783359885216\n",
            "Epoch 857: Loss = 0.04852854087948799, Test Loss = 0.04544387385249138\n",
            "Epoch 858: Loss = 0.04571814835071564, Test Loss = 0.045417819172143936\n",
            "Epoch 859: Loss = 0.04235365614295006, Test Loss = 0.0453873835504055\n",
            "Epoch 860: Loss = 0.0460258424282074, Test Loss = 0.04535127431154251\n",
            "Epoch 861: Loss = 0.045104943215847015, Test Loss = 0.045309893786907196\n",
            "Epoch 862: Loss = 0.04195323586463928, Test Loss = 0.045267459005117416\n",
            "Epoch 863: Loss = 0.04397128149867058, Test Loss = 0.04522889107465744\n",
            "Epoch 864: Loss = 0.04469175264239311, Test Loss = 0.04519536346197128\n",
            "Epoch 865: Loss = 0.042167771607637405, Test Loss = 0.04516326263546944\n",
            "Epoch 866: Loss = 0.04247564449906349, Test Loss = 0.045127205550670624\n",
            "Epoch 867: Loss = 0.04375144839286804, Test Loss = 0.04508652910590172\n",
            "Epoch 868: Loss = 0.04253237321972847, Test Loss = 0.04504336416721344\n",
            "Epoch 869: Loss = 0.04172423481941223, Test Loss = 0.04500150308012962\n",
            "Epoch 870: Loss = 0.04283555597066879, Test Loss = 0.04496337100863457\n",
            "Epoch 871: Loss = 0.042614735662937164, Test Loss = 0.04492948204278946\n",
            "Epoch 872: Loss = 0.04155316948890686, Test Loss = 0.0448971726000309\n",
            "Epoch 873: Loss = 0.04204680398106575, Test Loss = 0.04486318305134773\n",
            "Epoch 874: Loss = 0.04242309555411339, Test Loss = 0.04482683166861534\n",
            "Epoch 875: Loss = 0.041640445590019226, Test Loss = 0.04478927329182625\n",
            "Epoch 876: Loss = 0.041500214487314224, Test Loss = 0.044752977788448334\n",
            "Epoch 877: Loss = 0.042014725506305695, Test Loss = 0.04471808671951294\n",
            "Epoch 878: Loss = 0.04172668978571892, Test Loss = 0.04468424245715141\n",
            "Epoch 879: Loss = 0.04126140475273132, Test Loss = 0.044651105999946594\n",
            "Epoch 880: Loss = 0.041581492871046066, Test Loss = 0.04461681470274925\n",
            "Epoch 881: Loss = 0.04164411127567291, Test Loss = 0.04458053782582283\n",
            "Epoch 882: Loss = 0.0412355400621891, Test Loss = 0.04454369470477104\n",
            "Epoch 883: Loss = 0.04119683802127838, Test Loss = 0.04450812563300133\n",
            "Epoch 884: Loss = 0.0414128415286541, Test Loss = 0.04447386786341667\n",
            "Epoch 885: Loss = 0.04124193266034126, Test Loss = 0.04444055259227753\n",
            "Epoch 886: Loss = 0.041009802371263504, Test Loss = 0.04440740868449211\n",
            "Epoch 887: Loss = 0.04113760590553284, Test Loss = 0.04437345638871193\n",
            "Epoch 888: Loss = 0.041160643100738525, Test Loss = 0.044338859617710114\n",
            "Epoch 889: Loss = 0.04094716161489487, Test Loss = 0.04430461302399635\n",
            "Epoch 890: Loss = 0.040910013020038605, Test Loss = 0.04427075758576393\n",
            "Epoch 891: Loss = 0.04100538045167923, Test Loss = 0.0442376472055912\n",
            "Epoch 892: Loss = 0.040906935930252075, Test Loss = 0.04420485347509384\n",
            "Epoch 893: Loss = 0.040771033614873886, Test Loss = 0.04417217895388603\n",
            "Epoch 894: Loss = 0.040817342698574066, Test Loss = 0.04413921758532524\n",
            "Epoch 895: Loss = 0.040832601487636566, Test Loss = 0.04410562291741371\n",
            "Epoch 896: Loss = 0.040697768330574036, Test Loss = 0.04407154396176338\n",
            "Epoch 897: Loss = 0.04065551236271858, Test Loss = 0.044038597494363785\n",
            "Epoch 898: Loss = 0.04069221392273903, Test Loss = 0.0440068319439888\n",
            "Epoch 899: Loss = 0.04063243791460991, Test Loss = 0.0439753383398056\n",
            "Epoch 900: Loss = 0.04054529592394829, Test Loss = 0.04394382983446121\n",
            "Epoch 901: Loss = 0.04055098071694374, Test Loss = 0.04391263797879219\n",
            "Epoch 902: Loss = 0.04053814709186554, Test Loss = 0.043880920857191086\n",
            "Epoch 903: Loss = 0.04045819491147995, Test Loss = 0.04384869337081909\n",
            "Epoch 904: Loss = 0.040426939725875854, Test Loss = 0.04381652548909187\n",
            "Epoch 905: Loss = 0.04042062163352966, Test Loss = 0.043785210698843\n",
            "Epoch 906: Loss = 0.04038000851869583, Test Loss = 0.04375486075878143\n",
            "Epoch 907: Loss = 0.040325988084077835, Test Loss = 0.04372448846697807\n",
            "Epoch 908: Loss = 0.04030763730406761, Test Loss = 0.04369466379284859\n",
            "Epoch 909: Loss = 0.040282927453517914, Test Loss = 0.043665237724781036\n",
            "Epoch 910: Loss = 0.040239349007606506, Test Loss = 0.0436355397105217\n",
            "Epoch 911: Loss = 0.0402037687599659, Test Loss = 0.0436054989695549\n",
            "Epoch 912: Loss = 0.040187012404203415, Test Loss = 0.04357566684484482\n",
            "Epoch 913: Loss = 0.04015712812542915, Test Loss = 0.04354618489742279\n",
            "Epoch 914: Loss = 0.04011712223291397, Test Loss = 0.0435163713991642\n",
            "Epoch 915: Loss = 0.04008704796433449, Test Loss = 0.04348566383123398\n",
            "Epoch 916: Loss = 0.04006475955247879, Test Loss = 0.04345446825027466\n",
            "Epoch 917: Loss = 0.04003441333770752, Test Loss = 0.04342355206608772\n",
            "Epoch 918: Loss = 0.03999699652194977, Test Loss = 0.04339361563324928\n",
            "Epoch 919: Loss = 0.03997142240405083, Test Loss = 0.043364573270082474\n",
            "Epoch 920: Loss = 0.039944812655448914, Test Loss = 0.043336961418390274\n",
            "Epoch 921: Loss = 0.03991324082016945, Test Loss = 0.043310344219207764\n",
            "Epoch 922: Loss = 0.03988160565495491, Test Loss = 0.04328487813472748\n",
            "Epoch 923: Loss = 0.03986012190580368, Test Loss = 0.04325912520289421\n",
            "Epoch 924: Loss = 0.03983190283179283, Test Loss = 0.0432317852973938\n",
            "Epoch 925: Loss = 0.0397963710129261, Test Loss = 0.043203290551900864\n",
            "Epoch 926: Loss = 0.03977155685424805, Test Loss = 0.04317518696188927\n",
            "Epoch 927: Loss = 0.039743803441524506, Test Loss = 0.0431475006043911\n",
            "Epoch 928: Loss = 0.039715878665447235, Test Loss = 0.04312066361308098\n",
            "Epoch 929: Loss = 0.03968483954668045, Test Loss = 0.04309621825814247\n",
            "Epoch 930: Loss = 0.03965870290994644, Test Loss = 0.04307345673441887\n",
            "Epoch 931: Loss = 0.03963151574134827, Test Loss = 0.043051496148109436\n",
            "Epoch 932: Loss = 0.03960224613547325, Test Loss = 0.04302890971302986\n",
            "Epoch 933: Loss = 0.03957178443670273, Test Loss = 0.04300491139292717\n",
            "Epoch 934: Loss = 0.039545584470033646, Test Loss = 0.04298095032572746\n",
            "Epoch 935: Loss = 0.039518535137176514, Test Loss = 0.04295535385608673\n",
            "Epoch 936: Loss = 0.03949080407619476, Test Loss = 0.04292840510606766\n",
            "Epoch 937: Loss = 0.039460714906454086, Test Loss = 0.043471019715070724\n",
            "Epoch 938: Loss = 1382.1614990234375, Test Loss = 0.03942939639091492\n",
            "Epoch 939: Loss = 1221.4759521484375, Test Loss = 0.03996775299310684\n",
            "Epoch 940: Loss = 1124.512939453125, Test Loss = 0.043867968022823334\n",
            "Epoch 941: Loss = 0.5185064673423767, Test Loss = 0.04819318279623985\n",
            "Epoch 942: Loss = 870.734375, Test Loss = 0.04840140417218208\n",
            "Epoch 943: Loss = 839.3707275390625, Test Loss = 0.04497654363512993\n",
            "Epoch 944: Loss = 1.5990612506866455, Test Loss = 0.04216252639889717\n",
            "Epoch 945: Loss = 620.8661499023438, Test Loss = 0.042203035205602646\n",
            "Epoch 946: Loss = 623.2152099609375, Test Loss = 0.04478909447789192\n",
            "Epoch 947: Loss = 2.094747304916382, Test Loss = 0.047677524387836456\n",
            "Epoch 948: Loss = 445.08795166015625, Test Loss = 0.047730252146720886\n",
            "Epoch 949: Loss = 461.36328125, Test Loss = 0.045106835663318634\n",
            "Epoch 950: Loss = 2.452761173248291, Test Loss = 0.04289603233337402\n",
            "Epoch 951: Loss = 319.396728515625, Test Loss = 0.04273983836174011\n",
            "Epoch 952: Loss = 340.2491455078125, Test Loss = 0.04457703232765198\n",
            "Epoch 953: Loss = 2.254098892211914, Test Loss = 0.04657602310180664\n",
            "Epoch 954: Loss = 230.62518310546875, Test Loss = 0.04657753184437752\n",
            "Epoch 955: Loss = 250.2772674560547, Test Loss = 0.04463700205087662\n",
            "Epoch 956: Loss = 1.9934600591659546, Test Loss = 0.042869932949543\n",
            "Epoch 957: Loss = 167.07382202148438, Test Loss = 0.04272601380944252\n",
            "Epoch 958: Loss = 183.3505859375, Test Loss = 0.0440077967941761\n",
            "Epoch 959: Loss = 1.5264389514923096, Test Loss = 0.04543537274003029\n",
            "Epoch 960: Loss = 121.75137329101562, Test Loss = 0.04538179188966751\n",
            "Epoch 961: Loss = 134.0252227783203, Test Loss = 0.043963223695755005\n",
            "Epoch 962: Loss = 1.1301575899124146, Test Loss = 0.04260991886258125\n",
            "Epoch 963: Loss = 89.10575103759766, Test Loss = 0.04244672879576683\n",
            "Epoch 964: Loss = 97.52595520019531, Test Loss = 0.043394558131694794\n",
            "Epoch 965: Loss = 0.7465713024139404, Test Loss = 0.04437058046460152\n",
            "Epoch 966: Loss = 65.62511444091797, Test Loss = 0.044299207627773285\n",
            "Epoch 967: Loss = 70.73277282714844, Test Loss = 0.04321848601102829\n",
            "Epoch 968: Loss = 0.46029922366142273, Test Loss = 0.042221810668706894\n",
            "Epoch 969: Loss = 48.49138259887695, Test Loss = 0.04207981750369072\n",
            "Epoch 970: Loss = 51.12635040283203, Test Loss = 0.04276715964078903\n",
            "Epoch 971: Loss = 0.24985837936401367, Test Loss = 0.04347439482808113\n",
            "Epoch 972: Loss = 36.07585906982422, Test Loss = 0.04339292645454407\n",
            "Epoch 973: Loss = 36.75802993774414, Test Loss = 0.04260491207242012\n",
            "Epoch 974: Loss = 0.12415281683206558, Test Loss = 0.04188471660017967\n",
            "Epoch 975: Loss = 26.90253448486328, Test Loss = 0.04182394966483116\n",
            "Epoch 976: Loss = 26.35310173034668, Test Loss = 0.04233327507972717\n",
            "Epoch 977: Loss = 0.05750703811645508, Test Loss = 0.04282834753394127\n",
            "Epoch 978: Loss = 20.192001342773438, Test Loss = 0.04274572432041168\n",
            "Epoch 979: Loss = 18.798463821411133, Test Loss = 0.04215199500322342\n",
            "Epoch 980: Loss = 0.03846672177314758, Test Loss = 0.04162581264972687\n",
            "Epoch 981: Loss = 15.20789909362793, Test Loss = 0.041602037847042084\n",
            "Epoch 982: Loss = 13.312520980834961, Test Loss = 0.041992247104644775\n",
            "Epoch 983: Loss = 0.04939055070281029, Test Loss = 0.04233262687921524\n",
            "Epoch 984: Loss = 11.461989402770996, Test Loss = 0.04224201291799545\n",
            "Epoch 985: Loss = 9.368827819824219, Test Loss = 0.04180377721786499\n",
            "Epoch 986: Loss = 0.0777040645480156, Test Loss = 0.04142754152417183\n",
            "Epoch 987: Loss = 8.662845611572266, Test Loss = 0.04142947867512703\n",
            "Epoch 988: Loss = 6.544007301330566, Test Loss = 0.041732728481292725\n",
            "Epoch 989: Loss = 0.11574982851743698, Test Loss = 0.04197141155600548\n",
            "Epoch 990: Loss = 6.555463790893555, Test Loss = 0.04187369346618652\n",
            "Epoch 991: Loss = 4.52504301071167, Test Loss = 0.04153135418891907\n",
            "Epoch 992: Loss = 0.15250082314014435, Test Loss = 0.04125453531742096\n",
            "Epoch 993: Loss = 4.967160701751709, Test Loss = 0.041241079568862915\n",
            "Epoch 994: Loss = 3.090644121170044, Test Loss = 0.04142431169748306\n",
            "Epoch 995: Loss = 0.18671958148479462, Test Loss = 0.04153894633054733\n",
            "Epoch 996: Loss = 3.7525506019592285, Test Loss = 0.04140247032046318\n",
            "Epoch 997: Loss = 2.0829453468322754, Test Loss = 0.041102420538663864\n",
            "Epoch 998: Loss = 0.2107161581516266, Test Loss = 0.040878601372241974\n",
            "Epoch 999: Loss = 2.8282229900360107, Test Loss = 0.04086414724588394\n",
            "Epoch 1000: Loss = 1.390217661857605, Test Loss = 0.04097619652748108\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plot train/test loss"
      ],
      "metadata": {
        "id": "KWKGJl6FuBFy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(train_losses, label='Train Loss')\n",
        "plt.plot(test_losses, label='Test Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.yscale('log')\n",
        "plt.title('Train and Test Loss Over Epochs')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "BQVqaWu3uANt",
        "outputId": "cc291b4d-729d-426e-dcbe-73e68304b6e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Evaluate the model performance"
      ],
      "metadata": {
        "id": "uvn5g8qZuMZU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.eval()\n",
        "total = 0\n",
        "correct = 0\n",
        "\n",
        "threshold = 0.1\n",
        "\n",
        "with torch.no_grad():\n",
        "    predictions = model(X_test)\n",
        "    test_loss = criterion(predictions.squeeze(), y_test)\n",
        "\n",
        "    diff = torch.abs(predictions.squeeze() - y_test)\n",
        "    correct = (diff < threshold).sum().item()\n",
        "    total = y_test.size(0)\n",
        "\n",
        "    accuracy = 100 * correct / total\n",
        "\n",
        "    print(f'Final Test Loss: {test_loss.item()}')\n",
        "    print(f'Test Accuracy (within {threshold} threshold): {accuracy:.2f}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oE6xPWvDuQVA",
        "outputId": "c1902c1e-913c-4234-b9ce-81640da6ca3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Test Loss: 0.04097619652748108\n",
            "Test Accuracy (within 0.1 threshold): 90.85%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test_df = pd.DataFrame(deepcopy(X_test.cpu().numpy()), columns=features)\n",
        "\n",
        "#y_test_df = pd.Series(deepcopy(y_test.cpu().numpy()), name='Export Value Lag3')\n",
        "\n",
        "final_csv = pd.DataFrame()\n",
        "final_csv['Year'] = encoder_3.inverse_transform(x_test_df['Year'].astype(int))\n",
        "final_csv['Area'] = encoder_1.inverse_transform(x_test_df['Area'].astype(int))\n",
        "final_csv['Products'] = encoder_2.inverse_transform(x_test_df['Products'].astype(int))\n",
        "\n",
        "\n",
        "cpu_predictions = predictions.cpu().numpy().squeeze()\n",
        "predictions_df = pd.Series(cpu_predictions, name='Prediction')\n",
        "p = pd.DataFrame(scaler.inverse_transform(x_test_df[normalized_features]), columns=normalized_features)\n",
        "y_test_df = p['Export Value Lag3']\n",
        "x_test_df['Export Value Lag3'] = predictions_df\n",
        "p = pd.DataFrame(scaler.inverse_transform(x_test_df[normalized_features]), columns=normalized_features)\n",
        "predictions_df = p['Export Value Lag3']\n",
        "\n",
        "final_csv['True Label'] = y_test_df\n",
        "final_csv['Prediction'] = predictions_df\n",
        "final_csv.to_csv('predictions.csv', index=False)\n",
        "\n",
        "print(final_csv)"
      ],
      "metadata": {
        "id": "__QrmXn1pYKQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1652c7d-4aad-4c92-87aa-19ddf85533e8"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       Year               Area                          Products  \\\n",
            "0      2019            Ukraine                        Other food   \n",
            "1      2012           Honduras                        Other food   \n",
            "2      2017            Türkiye  Fats and Oils (excluding Butter)   \n",
            "3      2017            Belgium          Non-edible Fats and Oils   \n",
            "4      2019              Kenya           Non-alcoholic Beverages   \n",
            "...     ...                ...                               ...   \n",
            "14778  2011  Brunei Darussalam               Alcoholic Beverages   \n",
            "14779  2012            Czechia               Alcoholic Beverages   \n",
            "14780  1992   Papua New Guinea  Fats and Oils (excluding Butter)   \n",
            "14781  1993            Senegal                   Sugar and Honey   \n",
            "14782  1996              Italy              Fruit and Vegetables   \n",
            "\n",
            "         True Label    Prediction  \n",
            "0      1.037876e+06  1.040390e+06  \n",
            "1      1.636050e+05  1.618912e+05  \n",
            "2      2.042384e+06  2.042151e+06  \n",
            "3      2.652552e+05  2.602780e+05  \n",
            "4      8.273828e+03  1.144125e+04  \n",
            "...             ...           ...  \n",
            "14778  5.756000e+03  1.040772e+04  \n",
            "14779  3.245830e+05  3.091125e+05  \n",
            "14780  3.265322e+05  3.312259e+05  \n",
            "14781  3.368736e+04  3.095900e+04  \n",
            "14782  2.030284e+06  2.042202e+06  \n",
            "\n",
            "[14783 rows x 5 columns]\n"
          ]
        }
      ]
    }
  ]
}