nvidiastockpredcit.ipynb

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  "metadata": {
    "colab": {
      "name": "nvidiastockpredcit.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Elathius/nvidiapredict/blob/master/nvidiastockpredcit.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lpPzDMY8kOSB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ApVILABElBuM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import necessary packages\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import datetime\n",
        "import pandas_datareader.data as web\n",
        "from pandas import Series, DataFrame\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import sklearn\n",
        "from sklearn.linear_model import Ridge, LinearRegression, Lasso\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "from sklearn.preprocessing import MinMaxScaler, PolynomialFeatures\n",
        "from sklearn import metrics\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q1424DATl4Bw",
        "colab_type": "code",
        "outputId": "2be2f6b7-4695-4dc3-85a2-a0872857cd34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "start = datetime.datetime(2017, 1, 1)\n",
        "end = datetime.datetime(2019, 1, 11)\n",
        "\n",
        "\n",
        "df = web.DataReader(\"NVDA\", 'yahoo', start, end)\n",
        "\n",
        "#df = pd.read_csv('NVDA.csv')\n",
        "#df.set_index('Date', inplace = True)\n",
        "df.tail()\n"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-01-07</th>\n",
              "      <td>144.889999</td>\n",
              "      <td>136.429993</td>\n",
              "      <td>138.500000</td>\n",
              "      <td>143.399994</td>\n",
              "      <td>17729000.0</td>\n",
              "      <td>142.947525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-08</th>\n",
              "      <td>146.779999</td>\n",
              "      <td>136.899994</td>\n",
              "      <td>146.690002</td>\n",
              "      <td>139.830002</td>\n",
              "      <td>19650400.0</td>\n",
              "      <td>139.388794</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-09</th>\n",
              "      <td>144.490005</td>\n",
              "      <td>139.860001</td>\n",
              "      <td>141.899994</td>\n",
              "      <td>142.580002</td>\n",
              "      <td>15431500.0</td>\n",
              "      <td>142.130142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-10</th>\n",
              "      <td>145.580002</td>\n",
              "      <td>139.360001</td>\n",
              "      <td>141.800003</td>\n",
              "      <td>145.229996</td>\n",
              "      <td>13078900.0</td>\n",
              "      <td>144.771759</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-11</th>\n",
              "      <td>149.750000</td>\n",
              "      <td>143.210007</td>\n",
              "      <td>144.330002</td>\n",
              "      <td>148.830002</td>\n",
              "      <td>21869100.0</td>\n",
              "      <td>148.360397</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  High         Low  ...      Volume   Adj Close\n",
              "Date                                ...                        \n",
              "2019-01-07  144.889999  136.429993  ...  17729000.0  142.947525\n",
              "2019-01-08  146.779999  136.899994  ...  19650400.0  139.388794\n",
              "2019-01-09  144.490005  139.860001  ...  15431500.0  142.130142\n",
              "2019-01-10  145.580002  139.360001  ...  13078900.0  144.771759\n",
              "2019-01-11  149.750000  143.210007  ...  21869100.0  148.360397\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5XmrJ-Glbet7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "e7c07702-7f02-40a0-af29-d195f252eec6"
      },
      "source": [
        "\n",
        "df[\"Adj Close\"].plot()\n",
        "df.shape"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(510, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        },
        {
          "output_type": "display_data",
          "data": {
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ZNndMZoeqjgn2IqVGT++Wq3t9huYW34AUJFNiB6+9NuPKR1dSVd9W6NMSXG0y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\nWeglqecs9JLUcxZ6Seo5C70k9dy/FrL/VYuixZsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IqMlGYp9l9nv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "window_size=32\n",
        "num_samples=len(df)-window_size\n",
        "\n",
        "# Get indices of access for the data\n",
        "indices=np.arange(num_samples).astype(np.int)[:,None]+np.arange(window_size+1).astype(np.int)\n",
        "\n",
        "\n",
        "data = df['Adj Close'].values[indices] # Create the 2D matrix of training samples\n",
        "x = data[:,:-1] # Each row represents 32 days in the past\n",
        "y = data[:,-1] # Each output value represents the 33rd day\n",
        "\n",
        "\n",
        "split_fraction=0.75\n",
        "ind_split=int(split_fraction*num_samples)\n",
        "\n",
        "\n",
        "x_train = x[:ind_split]\n",
        "y_train = y[:ind_split]\n",
        "x_test = x[ind_split:]\n",
        "y_test = y[ind_split:]\n",
        "\n",
        "x_train_orig = x_train"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MmuPSzIFmyNR",
        "colab_type": "text"
      },
      "source": [
        "**Performance functions:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lGzjAB0Jm5kT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def performance(model):\n",
        "  print(\"MAE :\", metrics.mean_absolute_error(y_test,model).round(5))\n",
        "  print(\"MSE :\", metrics.mean_squared_error(y_test,model).round(5))\n",
        "  print(\"RMSE:\", np.sqrt(metrics.mean_squared_error(y_test,model)).round(5))\n",
        "  \n",
        "def perfplot (model,colr='gray',linecolr='red'):\n",
        "  plt.scatter(model, y_test, color=colr)\n",
        "  plt.plot(y_test, y_test, color=linecolr, linewidth=2)\n",
        "  \n",
        "def compareall(modelone,model):\n",
        "  print(\"Percent difference from baseline metrics: Lower is better (Error minimizing)\")\n",
        "  print(\"MAE % :\", (metrics.mean_absolute_error(y_test,modelone).round(5)/ metrics.mean_absolute_error(y_test,model).round(5) )*100 )\n",
        "  print(\"MSE % :\", (metrics.mean_squared_error(y_test,modelone).round(5) / metrics.mean_squared_error(y_test,model).round(5) )*100 )\n",
        "  print(\"RMSE %:\", (np.sqrt( metrics.mean_absolute_error(y_test,modelone) )/np.sqrt( metrics.mean_absolute_error(y_test,model) )*100).round(5))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iiaYazVooLb4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ypredlag = np.roll(y_test,1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h-skv8npogje",
        "colab_type": "text"
      },
      "source": [
        "Base line metrics:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N6ma5hE9oQMj",
        "colab_type": "code",
        "outputId": "fc33bd90-8240-4da1-d32a-49198dcdfce1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "performance(ypredlag)"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MAE : 6.11452\n",
            "MSE : 139.10746\n",
            "RMSE: 11.79438\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nVqC4MYEoTTt",
        "colab_type": "code",
        "outputId": "5375f35a-0a8a-4321-aeda-cffbbcc65ded",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "perfplot(ypredlag)"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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46CG47z4FeZESoha9BFu1ymvFr1vnPb/wQm/lSa0bI1Jy1KKXaLt2weWXwwkn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\nypwCvYhImVOgFxEpcwr0IiJl7v8DRJAId8TLt3IAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TFporBgHoZ9L",
        "colab_type": "code",
        "outputId": "750390e7-0689-449b-dce1-b56b91c1077c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "modellinear = LinearRegression()\n",
        "modellinear.fit(x_train,y_train)"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "440aj-mIo3xq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ypredlinear = modellinear.predict(x_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LuFM9cavpNae",
        "colab_type": "code",
        "outputId": "52e4dc48-24c9-45eb-b8a2-b84f296d0b9a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "performance(ypredlinear)"
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MAE : 5.84323\n",
            "MSE : 69.84169\n",
            "RMSE: 8.35713\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1eO0Sb4EtCEo",
        "colab_type": "text"
      },
      "source": [
        "Ridge Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DD1ZEm9QpQSo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "modelridge = Ridge()\n",
        "modelridge.fit(x_train,y_train)\n",
        "\n",
        "ypredridge = modelridge.predict(x_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A7_wxrwGu-rx",
        "colab_type": "code",
        "outputId": "f3b917b2-aaa3-4a67-8962-38223c6dfa74",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        }
      },
      "source": [
        "performance(ypredridge)\n",
        "perfplot(ypredridge)"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MAE : 5.84308\n",
            "MSE : 69.84092\n",
            "RMSE: 8.35709\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ppe6ZiGSgO2MlsxdfhHHj4I47vOdTp8JrrynIi1QI1bqR1LZvh+uvh/vv954f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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XR0I4BPnvD0M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "modellasso = Lasso()\n",
        "modellasso.fit(x_train,y_train)\n",
        "\n",
        "ypredlasso = modellasso.predict(x_test)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ieDJtgxvfne",
        "colab_type": "code",
        "outputId": "c5293759-63c5-4d75-cf40-5752ef675a26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        }
      },
      "source": [
        "performance(ypredlasso)\n",
        "perfplot(ypredlasso)"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MAE : 5.56354\n",
            "MSE : 64.21149\n",
            "RMSE: 8.01321\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kRSqURvRVKpedlnjuOfjud+Htt2G33bz18M3NsMceRemziBSGAn0Viu60FN2E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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rqoAoYmbrGPy",
        "colab_type": "text"
      },
      "source": [
        "Polynomial Regression:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CHOtwTsYx2GU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "105a683a-0ec2-4112-8403-36c7139747e9"
      },
      "source": [
        "modelpoly = PolynomialFeatures(degree = 2)\n",
        "modelpoly.fit_transform(x_train)\n",
        "\n",
        "modellinear.fit(x_train,y_train)\n"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BIzjC6-DzkvX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "804abde1-c2a1-4883-cbb9-ebf87e58781f"
      },
      "source": [
        "ypredpoly = modellinear.predict(x_test)\n",
        "print(x_test[0])"
      ],
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[263.90481567 261.66540527 261.04830933 259.39605713 261.34686279\n",
            " 261.16775513 265.65658569 264.01431274 263.84509277 258.94824219\n",
            " 261.07818604 255.90260315 249.77151489 237.99705505 240.85359192\n",
            " 234.61302185 239.72889709 235.78749084 241.10240173 235.7277832\n",
            " 241.59010315 246.16851807 248.07948303 252.06069946 246.36756897\n",
            " 250.05018616 248.14918518 247.03442383 252.49865723 250.51797485\n",
            " 250.84645081 249.71180725]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hBv_n3kyzxoF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "aec9b0b7-9545-4884-e3be-cb33f4128f87"
      },
      "source": [
        "performance(ypredpoly)"
      ],
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MAE : 5.84323\n",
            "MSE : 69.84169\n",
            "RMSE: 8.35713\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lM7KnkGsxyuQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "c54ecf00-c03e-4b2c-bb78-4984ee13511d"
      },
      "source": [
        "perfplot(ypredpoly)"
      ],
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HR+TOuR3AWmCw//I9wFS8kX7UBGCB8ywFDjSzw/LbbREpCLPkIP/qq91BHrwg29DQEFe+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\nehGRKvf/AQOUal9u7RreAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t3VzDvhTW9gt",
        "colab_type": "text"
      },
      "source": [
        "Gradient Boosting:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c3lbjm7YwicS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "55504820-bab8-45ac-e654-8fd6509fac1e"
      },
      "source": [
        "modelgbr = GradientBoostingRegressor()\n",
        "modelgbr.fit(x_train, y_train)\n",
        "\n",
        "ypredgbr = modelgbr.predict(x_test)\n",
        "\n",
        "performance(ypredgbr)\n",
        "perfplot(ypredgbr)"
      ],
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MAE : 12.10686\n",
            "MSE : 209.4408\n",
            "RMSE: 14.47207\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GzO40s3fNbE1C+6Vm9pKZrTWzm2ParzSz9Wb2spl9pRCdlgDnnJMc5NetCwzy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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fd9qc4_PO3rE",
        "colab_type": "text"
      },
      "source": [
        "Comparision:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g_8p4RRbO5Wu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "e5b28f00-442e-4d7e-b475-40c7b28179dc"
      },
      "source": [
        "perfplot(ypredlinear,colr='blue')\n",
        "perfplot(ypredridge,colr= 'green')\n",
        "perfplot(ypredlasso, colr='purple')\n",
        "perfplot(ypredpoly, colr='pink')\n",
        "perfplot(ypredgbr, colr='yellow')"
      ],
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BTzG0dZt1IyITROR1EflARDaIyJxg+z0i8m8RWRf8mm57zp0isklENorIl/vyB1BK9a2S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\n8N5eAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SCnEla_sPBxV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "52fdcf02-7610-4168-89d6-e32ea4255ce9"
      },
      "source": [
        "#Linear\n",
        "compareall(ypredlinear,ypredlag)"
      ],
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Percent difference from baseline metrics: Lower is better (Error minimizing)\n",
            "MAE % : 95.56318402752792\n",
            "MSE % : 50.20700543306592\n",
            "RMSE %: 97.75642\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eL5FVeCqZc3Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "af7ae08f-caa8-4429-fc71-62c64e811fb0"
      },
      "source": [
        "#Ridge\n",
        "compareall(ypredridge,ypredlag)"
      ],
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Percent difference from baseline metrics: Lower is better (Error minimizing)\n",
            "MAE % : 95.56073085050012\n",
            "MSE % : 50.206451904161\n",
            "RMSE %: 97.75512\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3k_Q3v5OaZtc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "74b55f8a-c47b-498e-e7c0-b8428a014cb2"
      },
      "source": [
        "#Lasso\n",
        "compareall(ypredlasso,ypredlag)"
      ],
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Percent difference from baseline metrics: Lower is better (Error minimizing)\n",
            "MAE % : 90.98899014149924\n",
            "MSE % : 46.15963083503933\n",
            "RMSE %: 95.38811\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jq0G_tC2ahdE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "8fe67185-6aca-4aa4-cbda-26bd71449b02"
      },
      "source": [
        "#Poly\n",
        "compareall(ypredpoly,ypredlag)"
      ],
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Percent difference from baseline metrics: Lower is better (Error minimizing)\n",
            "MAE % : 95.56318402752792\n",
            "MSE % : 50.20700543306592\n",
            "RMSE %: 97.75642\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kuf9OVvfatNf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "9a330a29-4cb2-4491-8be5-509049896d6f"
      },
      "source": [
        "#GradientBoosting\n",
        "compareall(ypredgbr,ypredlag)"
      ],
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Percent difference from baseline metrics: Lower is better (Error minimizing)\n",
            "MAE % : 198.00180553829244\n",
            "MSE % : 150.56043723320084\n",
            "RMSE %: 140.71304\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YoJGOOBpax3e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}