chapter_4_Conjoint_Analysis.ipynb

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        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import statsmodels.api as sm"
      ],
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            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ]
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        "# data: PC and spec data\n",
        "data = pd.DataFrame([[6000, '4GB', '128GB', 3],\n",
        "                     [6000, '8GB', '512GB', 9],\n",
        "                     [8000, '4GB', '512GB', 5],\n",
        "                     [8000, '8GB', '128GB', 7],\n",
        "                     [6000, '4GB', '128GB', 4]],\n",
        "                    columns=['price', 'memory', 'storage', 'score'])\n",
        "data.head()"
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              "   price memory storage  score\n",
              "0   6000    4GB   128GB      3\n",
              "1   6000    8GB   512GB      9\n",
              "2   8000    4GB   512GB      5\n",
              "3   8000    8GB   128GB      7\n",
              "4   6000    4GB   128GB      4"
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              "   price memory storage\n",
              "0   6000    4GB   128GB\n",
              "1   6000    8GB   512GB\n",
              "2   8000    4GB   512GB\n",
              "3   8000    8GB   128GB\n",
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              "   price_6000  price_8000  memory_4GB  memory_8GB  storage_128GB  \\\n",
              "0           1           0           1           0              1   \n",
              "1           1           0           0           1              0   \n",
              "2           0           1           1           0              0   \n",
              "3           0           1           0           1              1   \n",
              "4           1           0           1           0              1   \n",
              "\n",
              "   storage_512GB  \n",
              "0              0  \n",
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              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\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",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bc5a979f-fcc2-4cbd-8bbe-671424a4dcea 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-bc5a979f-fcc2-4cbd-8bbe-671424a4dcea');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = sm.OLS(y, sm.add_constant(X_dum))\n",
        "result = model.fit()\n",
        "result.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        },
        "id": "8fu8R3BGHUro",
        "outputId": "3fbc071c-f811-4198-ea82-3e8934d4c42f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/tsatools.py:117: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
            "  x = pd.concat(x[::order], 1)\n",
            "/usr/local/lib/python3.7/dist-packages/statsmodels/stats/stattools.py:71: ValueWarning: omni_normtest is not valid with less than 8 observations; 5 samples were given.\n",
            "  \"samples were given.\" % int(n), ValueWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:                  score   R-squared:                       0.978\n",
              "Model:                            OLS   Adj. R-squared:                  0.914\n",
              "Method:                 Least Squares   F-statistic:                     15.13\n",
              "Date:                Thu, 23 Jun 2022   Prob (F-statistic):              0.186\n",
              "Time:                        09:16:10   Log-Likelihood:                -1.3382\n",
              "No. Observations:                   5   AIC:                             10.68\n",
              "Df Residuals:                       1   BIC:                             9.114\n",
              "Df Model:                           3                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "=================================================================================\n",
              "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
              "---------------------------------------------------------------------------------\n",
              "const             2.4500      0.132     18.520      0.034       0.769       4.131\n",
              "price_6000        1.3500      0.328      4.117      0.152      -2.816       5.516\n",
              "price_8000        1.1000      0.346      3.175      0.194      -3.302       5.502\n",
              "memory_4GB       -0.6500      0.328     -1.982      0.297      -4.816       3.516\n",
              "memory_8GB        3.1000      0.346      8.949      0.071      -1.302       7.502\n",
              "storage_128GB     0.3500      0.328      1.067      0.479      -3.816       4.516\n",
              "storage_512GB     2.1000      0.346      6.062      0.104      -2.302       6.502\n",
              "==============================================================================\n",
              "Omnibus:                          nan   Durbin-Watson:                   1.000\n",
              "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.052\n",
              "Skew:                          -0.000   Prob(JB):                        0.974\n",
              "Kurtosis:                       2.500   Cond. No.                     2.06e+16\n",
              "==============================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "[2] The input rank is higher than the number of observations.\n",
              "[3] The smallest eigenvalue is 3.05e-32. This might indicate that there are\n",
              "strong multicollinearity problems or that the design matrix is singular.\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>          <td>score</td>      <th>  R-squared:         </th> <td>   0.978</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.914</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   15.13</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Thu, 23 Jun 2022</td> <th>  Prob (F-statistic):</th>  <td> 0.186</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>09:16:10</td>     <th>  Log-Likelihood:    </th> <td> -1.3382</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>     5</td>      <th>  AIC:               </th> <td>   10.68</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>     1</td>      <th>  BIC:               </th> <td>   9.114</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "        <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>         <td>    2.4500</td> <td>    0.132</td> <td>   18.520</td> <td> 0.034</td> <td>    0.769</td> <td>    4.131</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>price_6000</th>    <td>    1.3500</td> <td>    0.328</td> <td>    4.117</td> <td> 0.152</td> <td>   -2.816</td> <td>    5.516</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>price_8000</th>    <td>    1.1000</td> <td>    0.346</td> <td>    3.175</td> <td> 0.194</td> <td>   -3.302</td> <td>    5.502</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>memory_4GB</th>    <td>   -0.6500</td> <td>    0.328</td> <td>   -1.982</td> <td> 0.297</td> <td>   -4.816</td> <td>    3.516</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>memory_8GB</th>    <td>    3.1000</td> <td>    0.346</td> <td>    8.949</td> <td> 0.071</td> <td>   -1.302</td> <td>    7.502</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>storage_128GB</th> <td>    0.3500</td> <td>    0.328</td> <td>    1.067</td> <td> 0.479</td> <td>   -3.816</td> <td>    4.516</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>storage_512GB</th> <td>    2.1000</td> <td>    0.346</td> <td>    6.062</td> <td> 0.104</td> <td>   -2.302</td> <td>    6.502</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td>   nan</td> <th>  Durbin-Watson:     </th> <td>   1.000</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td>   nan</td> <th>  Jarque-Bera (JB):  </th> <td>   0.052</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td>-0.000</td> <th>  Prob(JB):          </th> <td>   0.974</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 2.500</td> <th>  Cond. No.          </th> <td>2.06e+16</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The input rank is higher than the number of observations.<br/>[3] The smallest eigenvalue is 3.05e-32. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Q5NcSZdIHbSd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_res = pd.DataFrame({'name': result.params.keys(),\n",
        "                         'weight': result.params.values,\n",
        "                         'p_val': result.pvalues})\n",
        "data_res = data_res[1:]\n",
        "data_res"
      ],
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          "height": 238
        },
        "id": "d5vbPOg8HwhN",
        "outputId": "d9afcbae-b0b4-447a-de8d-42f10455b0ed"
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      "execution_count": null,
      "outputs": [
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              "                        name  weight     p_val\n",
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              "    <tr>\n",
              "      <th>memory_4GB</th>\n",
              "      <td>memory_4GB</td>\n",
              "      <td>-0.65</td>\n",
              "      <td>0.297414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>memory_8GB</th>\n",
              "      <td>memory_8GB</td>\n",
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              "      <td>storage_512GB</td>\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "          const element = document.querySelector('#df-339dd1d3-fb90-4c31-b5a2-3ec017ebfaa3');\n",
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              "            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",
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              "  "
            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ・ If the significance level is 5%, it can be seen that all the factors are not statistically significant.\n",
        "data_res = data_res.sort_values(by='weight')\n",
        "data_res"
      ],
      "metadata": {
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      "execution_count": null,
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              "      <td>0.151678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>storage_512GB</th>\n",
              "      <td>storage_512GB</td>\n",
              "      <td>2.10</td>\n",
              "      <td>0.104078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>memory_8GB</th>\n",
              "      <td>memory_8GB</td>\n",
              "      <td>3.10</td>\n",
              "      <td>0.070845</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-73b32a43-dac7-4f72-be7d-f7c063a9785e')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-73b32a43-dac7-4f72-be7d-f7c063a9785e 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-73b32a43-dac7-4f72-be7d-f7c063a9785e');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set()\n",
        "\n",
        "xbar = np.arange(len(data_res['weight']))\n",
        "plt.barh(xbar, data_res['weight'])\n",
        "plt.yticks(xbar, labels=data_res['name'])\n",
        "plt.xlabel('weight')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "aBwRZPMtIQ9c",
        "outputId": "4245a990-b2c7-42b0-c043-83982fbff576"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from __future__ import division, print_function\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set()\n",
        "import statsmodels.api as sm\n",
        "import statsmodels.formula.api as smf\n",
        "from patsy.contrasts import Sum"
      ],
      "metadata": {
        "id": "1yiXQNDQNBWA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load data\n",
        "\n",
        "d = \"\"\"flavor\tprice\tweight\tfat_free\tsodium_free\tgluten_free\torganic\tmultipack\tranking\n",
        "\"cheese_onion\"\t\"1.00\"\t\"100g\"\t\"NO\"\t\"NO\"\t\"NO\"\t\"NO\"\t\"NO\"\t11\n",
        "\"salt_vinegar\"\t\"3.00\"\t\"100g\"\t\"NO\"\t\"YES\"\t\"YES\"\t\"YES\"\t\"NO\"\t12\n",
        "\"chicken_flavored\"\t\"4.00\"\t\"200g\"\t\"NO\"\t\"NO\"\t\"NO\"\t\"YES\"\t\"NO\"\t9\n",
        "\"salt_vinegar\"\t\"4.00\"\t\"400g\"\t\"YES\"\t\"YES\"\t\"NO\"\t\"NO\"\t\"NO\"\t2\n",
        "\"salt_vinegar\"\t\"2.00\"\t\"300g\"\t\"NO\"\t\"NO\"\t\"NO\"\t\"YES\"\t\"YES\"\t8\n",
        "\"salt_vinegar\"\t\"1.00\"\t\"200g\"\t\"YES\"\t\"NO\"\t\"YES\"\t\"NO\"\t\"YES\"\t13\n",
        "\"chicken_flavored\"\t\"3.00\"\t\"300g\"\t\"YES\"\t\"NO\"\t\"YES\"\t\"NO\"\t\"NO\"\t7\n",
        "\"cheese_onion\"\t\"4.00\"\t\"300g\"\t\"NO\"\t\"YES\"\t\"YES\"\t\"NO\"\t\"YES\"\t4\n",
        "\"cheese_onion\"\t\"2.00\"\t\"400g\"\t\"YES\"\t\"NO\"\t\"YES\"\t\"YES\"\t\"NO\"\t5\n",
        "\"tomato_ketchup\"\t\"4.00\"\t\"100g\"\t\"YES\"\t\"NO\"\t\"YES\"\t\"YES\"\t\"YES\"\t16\n",
        "\"chicken_flavored\"\t\"1.00\"\t\"400g\"\t\"NO\"\t\"YES\"\t\"YES\"\t\"YES\"\t\"YES\"\t3\n",
        "\"tomato_ketchup\"\t\"2.00\"\t\"200g\"\t\"NO\"\t\"YES\"\t\"YES\"\t\"NO\"\t\"NO\"\t6\n",
        "\"tomato_ketchup\"\t\"1.00\"\t\"300g\"\t\"YES\"\t\"YES\"\t\"NO\"\t\"YES\"\t\"NO\"\t10\n",
        "\"chicken_flavored\"\t\"2.00\"\t\"100g\"\t\"YES\"\t\"YES\"\t\"NO\"\t\"NO\"\t\"YES\"\t15\n",
        "\"tomato_ketchup\"\t\"3.00\"\t\"400g\"\t\"NO\"\t\"NO\"\t\"NO\"\t\"NO\"\t\"YES\"\t1\n",
        "\"cheese_onion\"\t\"3.00\"\t\"200g\"\t\"YES\"\t\"YES\"\t\"NO\"\t\"YES\"\t\"YES\"\t14\"\"\"\n",
        "\n",
        "conjoint_dat = pd.DataFrame([di.replace('\"','').split('\\t') for di in d.split('\\n')])\n",
        "conjoint_dat.columns = conjoint_dat.loc[0]\n",
        "conjoint_dat = conjoint_dat[1:]\n",
        "conjoint_dat"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "Xu6CVmN_NJzc",
        "outputId": "63b5303c-181a-4f5c-cd92-597cfacfc50c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0             flavor price weight fat_free sodium_free gluten_free organic  \\\n",
              "1       cheese_onion  1.00   100g       NO          NO          NO      NO   \n",
              "2       salt_vinegar  3.00   100g       NO         YES         YES     YES   \n",
              "3   chicken_flavored  4.00   200g       NO          NO          NO     YES   \n",
              "4       salt_vinegar  4.00   400g      YES         YES          NO      NO   \n",
              "5       salt_vinegar  2.00   300g       NO          NO          NO     YES   \n",
              "6       salt_vinegar  1.00   200g      YES          NO         YES      NO   \n",
              "7   chicken_flavored  3.00   300g      YES          NO         YES      NO   \n",
              "8       cheese_onion  4.00   300g       NO         YES         YES      NO   \n",
              "9       cheese_onion  2.00   400g      YES          NO         YES     YES   \n",
              "10    tomato_ketchup  4.00   100g      YES          NO         YES     YES   \n",
              "11  chicken_flavored  1.00   400g       NO         YES         YES     YES   \n",
              "12    tomato_ketchup  2.00   200g       NO         YES         YES      NO   \n",
              "13    tomato_ketchup  1.00   300g      YES         YES          NO     YES   \n",
              "14  chicken_flavored  2.00   100g      YES         YES          NO      NO   \n",
              "15    tomato_ketchup  3.00   400g       NO          NO          NO      NO   \n",
              "16      cheese_onion  3.00   200g      YES         YES          NO     YES   \n",
              "\n",
              "0  multipack ranking  \n",
              "1         NO      11  \n",
              "2         NO      12  \n",
              "3         NO       9  \n",
              "4         NO       2  \n",
              "5        YES       8  \n",
              "6        YES      13  \n",
              "7         NO       7  \n",
              "8        YES       4  \n",
              "9         NO       5  \n",
              "10       YES      16  \n",
              "11       YES       3  \n",
              "12        NO       6  \n",
              "13        NO      10  \n",
              "14       YES      15  \n",
              "15       YES       1  \n",
              "16       YES      14  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-60c08ba0-3ae6-4de7-9921-c491b623c886\">\n",
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              "\n",
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              "        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>flavor</th>\n",
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              "      <th>ranking</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>cheese_onion</td>\n",
              "      <td>1.00</td>\n",
              "      <td>100g</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>salt_vinegar</td>\n",
              "      <td>3.00</td>\n",
              "      <td>100g</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>4.00</td>\n",
              "      <td>200g</td>\n",
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              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>9</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>salt_vinegar</td>\n",
              "      <td>4.00</td>\n",
              "      <td>400g</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>salt_vinegar</td>\n",
              "      <td>2.00</td>\n",
              "      <td>300g</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>salt_vinegar</td>\n",
              "      <td>1.00</td>\n",
              "      <td>200g</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>chicken_flavored</td>\n",
              "      <td>3.00</td>\n",
              "      <td>300g</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>cheese_onion</td>\n",
              "      <td>4.00</td>\n",
              "      <td>300g</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>cheese_onion</td>\n",
              "      <td>2.00</td>\n",
              "      <td>400g</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>tomato_ketchup</td>\n",
              "      <td>4.00</td>\n",
              "      <td>100g</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>chicken_flavored</td>\n",
              "      <td>1.00</td>\n",
              "      <td>400g</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>tomato_ketchup</td>\n",
              "      <td>2.00</td>\n",
              "      <td>200g</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>tomato_ketchup</td>\n",
              "      <td>1.00</td>\n",
              "      <td>300g</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>chicken_flavored</td>\n",
              "      <td>2.00</td>\n",
              "      <td>100g</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>tomato_ketchup</td>\n",
              "      <td>3.00</td>\n",
              "      <td>400g</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>cheese_onion</td>\n",
              "      <td>3.00</td>\n",
              "      <td>200g</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>14</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conjoint_dat_dum = pd.get_dummies(conjoint_dat.iloc[:,:-1], columns=conjoint_dat.iloc[:,:-1].columns)\n",
        "conjoint_dat_dum.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "pf7B2K3uQLnB",
        "outputId": "1fdd0a08-1078-42c7-e253-bf8c514a5e29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   flavor_cheese_onion  flavor_chicken_flavored  flavor_salt_vinegar  \\\n",
              "1                    1                        0                    0   \n",
              "2                    0                        0                    1   \n",
              "3                    0                        1                    0   \n",
              "4                    0                        0                    1   \n",
              "5                    0                        0                    1   \n",
              "\n",
              "   flavor_tomato_ketchup  price_1.00  price_2.00  price_3.00  price_4.00  \\\n",
              "1                      0           1           0           0           0   \n",
              "2                      0           0           0           1           0   \n",
              "3                      0           0           0           0           1   \n",
              "4                      0           0           0           0           1   \n",
              "5                      0           0           1           0           0   \n",
              "\n",
              "   weight_100g  weight_200g  ...  fat_free_NO  fat_free_YES  sodium_free_NO  \\\n",
              "1            1            0  ...            1             0               1   \n",
              "2            1            0  ...            1             0               0   \n",
              "3            0            1  ...            1             0               1   \n",
              "4            0            0  ...            0             1               0   \n",
              "5            0            0  ...            1             0               1   \n",
              "\n",
              "   sodium_free_YES  gluten_free_NO  gluten_free_YES  organic_NO  organic_YES  \\\n",
              "1                0               1                0           1            0   \n",
              "2                1               0                1           0            1   \n",
              "3                0               1                0           0            1   \n",
              "4                1               1                0           1            0   \n",
              "5                0               1                0           0            1   \n",
              "\n",
              "   multipack_NO  multipack_YES  \n",
              "1             1              0  \n",
              "2             1              0  \n",
              "3             1              0  \n",
              "4             1              0  \n",
              "5             0              1  \n",
              "\n",
              "[5 rows x 22 columns]"
            ],
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              "\n",
              "  <div id=\"df-c1fc8e3e-0002-46f7-a9af-ce4b6486866d\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>flavor_cheese_onion</th>\n",
              "      <th>flavor_chicken_flavored</th>\n",
              "      <th>flavor_salt_vinegar</th>\n",
              "      <th>flavor_tomato_ketchup</th>\n",
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              "      <th>price_2.00</th>\n",
              "      <th>price_3.00</th>\n",
              "      <th>price_4.00</th>\n",
              "      <th>weight_100g</th>\n",
              "      <th>weight_200g</th>\n",
              "      <th>...</th>\n",
              "      <th>fat_free_NO</th>\n",
              "      <th>fat_free_YES</th>\n",
              "      <th>sodium_free_NO</th>\n",
              "      <th>sodium_free_YES</th>\n",
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              "      <th>gluten_free_YES</th>\n",
              "      <th>organic_NO</th>\n",
              "      <th>organic_YES</th>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "# set up sum contrasts for main effects coding as needed for conjoint analysis\n",
        "# using C(effect,Sum) notation within main effects model specification\n",
        "\n",
        "main_effects_model_fit = sm.OLS(conjoint_dat['ranking'].astype(int), sm.add_constant(conjoint_dat_dum))\n",
        "result = main_effects_model_fit.fit()\n",
        "result.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "NnWKcF5tRLpn",
        "outputId": "808950bc-4449-450c-ae90-7788242b4eb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/tsatools.py:117: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
            "  x = pd.concat(x[::order], 1)\n",
            "/usr/local/lib/python3.7/dist-packages/scipy/stats/stats.py:1535: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=16\n",
            "  \"anyway, n=%i\" % int(n))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:                ranking   R-squared:                       0.999\n",
              "Model:                            OLS   Adj. R-squared:                  0.989\n",
              "Method:                 Least Squares   F-statistic:                     97.07\n",
              "Date:                Thu, 23 Jun 2022   Prob (F-statistic):             0.0794\n",
              "Time:                        09:16:11   Log-Likelihood:                 10.568\n",
              "No. Observations:                  16   AIC:                             8.864\n",
              "Df Residuals:                       1   BIC:                             20.45\n",
              "Df Model:                          14                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "===========================================================================================\n",
              "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
              "-------------------------------------------------------------------------------------------\n",
              "const                       2.0000      0.029     68.000      0.009       1.626       2.374\n",
              "flavor_cheese_onion         0.5000      0.217      2.308      0.260      -2.253       3.253\n",
              "flavor_chicken_flavored     0.5000      0.217      2.308      0.260      -2.253       3.253\n",
              "flavor_salt_vinegar         0.7500      0.217      3.462      0.179      -2.003       3.503\n",
              "flavor_tomato_ketchup       0.2500      0.217      1.154      0.455      -2.503       3.003\n",
              "price_1.00                  1.2500      0.217      5.770      0.109      -1.503       4.003\n",
              "price_2.00                  0.5000      0.217      2.308      0.260      -2.253       3.253\n",
              "price_3.00                  0.5000      0.217      2.308      0.260      -2.253       3.253\n",
              "price_4.00                 -0.2500      0.217     -1.154      0.455      -3.003       2.503\n",
              "weight_100g                 5.5000      0.217     25.389      0.025       2.747       8.253\n",
              "weight_200g                 2.5000      0.217     11.540      0.055      -0.253       5.253\n",
              "weight_300g                -0.7500      0.217     -3.462      0.179      -3.503       2.003\n",
              "weight_400g                -5.2500      0.217    -24.235      0.026      -8.003      -2.497\n",
              "fat_free_NO                -0.7500      0.126     -5.959      0.106      -2.349       0.849\n",
              "fat_free_YES                2.7500      0.126     21.849      0.029       1.151       4.349\n",
              "sodium_free_NO              1.2500      0.126      9.932      0.064      -0.349       2.849\n",
              "sodium_free_YES             0.7500      0.126      5.959      0.106      -0.849       2.349\n",
              "gluten_free_NO              1.2500      0.126      9.932      0.064      -0.349       2.849\n",
              "gluten_free_YES             0.7500      0.126      5.959      0.106      -0.849       2.349\n",
              "organic_NO                 -0.1250      0.126     -0.993      0.502      -1.724       1.474\n",
              "organic_YES                 2.1250      0.126     16.884      0.038       0.526       3.724\n",
              "multipack_NO                0.2500      0.126      1.986      0.297      -1.349       1.849\n",
              "multipack_YES               1.7500      0.126     13.904      0.046       0.151       3.349\n",
              "==============================================================================\n",
              "Omnibus:                       30.796   Durbin-Watson:                   2.000\n",
              "Prob(Omnibus):                  0.000   Jarque-Bera (JB):                2.667\n",
              "Skew:                           0.000   Prob(JB):                        0.264\n",
              "Kurtosis:                       1.000   Cond. No.                     2.07e+16\n",
              "==============================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "[2] The input rank is higher than the number of observations.\n",
              "[3] The smallest eigenvalue is 1.58e-31. This might indicate that there are\n",
              "strong multicollinearity problems or that the design matrix is singular.\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>         <td>ranking</td>     <th>  R-squared:         </th> <td>   0.999</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.989</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   97.07</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Thu, 23 Jun 2022</td> <th>  Prob (F-statistic):</th>  <td>0.0794</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>09:16:11</td>     <th>  Log-Likelihood:    </th> <td>  10.568</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>    16</td>      <th>  AIC:               </th> <td>   8.864</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>     1</td>      <th>  BIC:               </th> <td>   20.45</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>    14</td>      <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "             <td></td>                <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>                   <td>    2.0000</td> <td>    0.029</td> <td>   68.000</td> <td> 0.009</td> <td>    1.626</td> <td>    2.374</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>flavor_cheese_onion</th>     <td>    0.5000</td> <td>    0.217</td> <td>    2.308</td> <td> 0.260</td> <td>   -2.253</td> <td>    3.253</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>flavor_chicken_flavored</th> <td>    0.5000</td> <td>    0.217</td> <td>    2.308</td> <td> 0.260</td> <td>   -2.253</td> <td>    3.253</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>flavor_salt_vinegar</th>     <td>    0.7500</td> <td>    0.217</td> <td>    3.462</td> <td> 0.179</td> <td>   -2.003</td> <td>    3.503</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>flavor_tomato_ketchup</th>   <td>    0.2500</td> <td>    0.217</td> <td>    1.154</td> <td> 0.455</td> <td>   -2.503</td> <td>    3.003</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>price_1.00</th>              <td>    1.2500</td> <td>    0.217</td> <td>    5.770</td> <td> 0.109</td> <td>   -1.503</td> <td>    4.003</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>price_2.00</th>              <td>    0.5000</td> <td>    0.217</td> <td>    2.308</td> <td> 0.260</td> <td>   -2.253</td> <td>    3.253</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>price_3.00</th>              <td>    0.5000</td> <td>    0.217</td> <td>    2.308</td> <td> 0.260</td> <td>   -2.253</td> <td>    3.253</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>price_4.00</th>              <td>   -0.2500</td> <td>    0.217</td> <td>   -1.154</td> <td> 0.455</td> <td>   -3.003</td> <td>    2.503</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>weight_100g</th>             <td>    5.5000</td> <td>    0.217</td> <td>   25.389</td> <td> 0.025</td> <td>    2.747</td> <td>    8.253</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>weight_200g</th>             <td>    2.5000</td> <td>    0.217</td> <td>   11.540</td> <td> 0.055</td> <td>   -0.253</td> <td>    5.253</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>weight_300g</th>             <td>   -0.7500</td> <td>    0.217</td> <td>   -3.462</td> <td> 0.179</td> <td>   -3.503</td> <td>    2.003</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>weight_400g</th>             <td>   -5.2500</td> <td>    0.217</td> <td>  -24.235</td> <td> 0.026</td> <td>   -8.003</td> <td>   -2.497</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>fat_free_NO</th>             <td>   -0.7500</td> <td>    0.126</td> <td>   -5.959</td> <td> 0.106</td> <td>   -2.349</td> <td>    0.849</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>fat_free_YES</th>            <td>    2.7500</td> <td>    0.126</td> <td>   21.849</td> <td> 0.029</td> <td>    1.151</td> <td>    4.349</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>sodium_free_NO</th>          <td>    1.2500</td> <td>    0.126</td> <td>    9.932</td> <td> 0.064</td> <td>   -0.349</td> <td>    2.849</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>sodium_free_YES</th>         <td>    0.7500</td> <td>    0.126</td> <td>    5.959</td> <td> 0.106</td> <td>   -0.849</td> <td>    2.349</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>gluten_free_NO</th>          <td>    1.2500</td> <td>    0.126</td> <td>    9.932</td> <td> 0.064</td> <td>   -0.349</td> <td>    2.849</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>gluten_free_YES</th>         <td>    0.7500</td> <td>    0.126</td> <td>    5.959</td> <td> 0.106</td> <td>   -0.849</td> <td>    2.349</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>organic_NO</th>              <td>   -0.1250</td> <td>    0.126</td> <td>   -0.993</td> <td> 0.502</td> <td>   -1.724</td> <td>    1.474</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>organic_YES</th>             <td>    2.1250</td> <td>    0.126</td> <td>   16.884</td> <td> 0.038</td> <td>    0.526</td> <td>    3.724</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>multipack_NO</th>            <td>    0.2500</td> <td>    0.126</td> <td>    1.986</td> <td> 0.297</td> <td>   -1.349</td> <td>    1.849</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>multipack_YES</th>           <td>    1.7500</td> <td>    0.126</td> <td>   13.904</td> <td> 0.046</td> <td>    0.151</td> <td>    3.349</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td>30.796</td> <th>  Durbin-Watson:     </th> <td>   2.000</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>   2.667</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td> 0.000</td> <th>  Prob(JB):          </th> <td>   0.264</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 1.000</td> <th>  Cond. No.          </th> <td>2.07e+16</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The input rank is higher than the number of observations.<br/>[3] The smallest eigenvalue is 1.58e-31. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_res = pd.DataFrame({'name': result.params.keys(),\n",
        "                         'weight': result.params.values,\n",
        "                         'p_val': result.pvalues})\n",
        "data_res = data_res[1:]\n",
        "data_res"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 739
        },
        "id": "jXv6rrEJSUzf",
        "outputId": "20d82ced-9e80-4f23-c50b-76f9b9a55cef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            name  weight     p_val\n",
              "flavor_cheese_onion          flavor_cheese_onion   0.500  0.260281\n",
              "flavor_chicken_flavored  flavor_chicken_flavored   0.500  0.260281\n",
              "flavor_salt_vinegar          flavor_salt_vinegar   0.750  0.179010\n",
              "flavor_tomato_ketchup      flavor_tomato_ketchup   0.250  0.454553\n",
              "price_1.00                            price_1.00   1.250  0.109244\n",
              "price_2.00                            price_2.00   0.500  0.260281\n",
              "price_3.00                            price_3.00   0.500  0.260281\n",
              "price_4.00                            price_4.00  -0.250  0.454553\n",
              "weight_100g                          weight_100g   5.500  0.025062\n",
              "weight_200g                          weight_200g   2.500  0.055027\n",
              "weight_300g                          weight_300g  -0.750  0.179010\n",
              "weight_400g                          weight_400g  -5.250  0.026254\n",
              "fat_free_NO                          fat_free_NO  -0.750  0.105849\n",
              "fat_free_YES                        fat_free_YES   2.750  0.029117\n",
              "sodium_free_NO                    sodium_free_NO   1.250  0.063886\n",
              "sodium_free_YES                  sodium_free_YES   0.750  0.105849\n",
              "gluten_free_NO                    gluten_free_NO   1.250  0.063886\n",
              "gluten_free_YES                  gluten_free_YES   0.750  0.105849\n",
              "organic_NO                            organic_NO  -0.125  0.502188\n",
              "organic_YES                          organic_YES   2.125  0.037662\n",
              "multipack_NO                        multipack_NO   0.250  0.296921\n",
              "multipack_YES                      multipack_YES   1.750  0.045708"
            ],
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              "    <tr>\n",
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              "      <td>0.500</td>\n",
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              "      <th>weight_100g</th>\n",
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              "      <td>5.500</td>\n",
              "      <td>0.025062</td>\n",
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              "    <tr>\n",
              "      <th>weight_200g</th>\n",
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              "      <td>2.500</td>\n",
              "      <td>0.055027</td>\n",
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              "      <th>weight_300g</th>\n",
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              "      <td>-0.750</td>\n",
              "      <td>0.179010</td>\n",
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              "      <td>-5.250</td>\n",
              "      <td>0.026254</td>\n",
              "    </tr>\n",
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              "      <th>fat_free_NO</th>\n",
              "      <td>fat_free_NO</td>\n",
              "      <td>-0.750</td>\n",
              "      <td>0.105849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fat_free_YES</th>\n",
              "      <td>fat_free_YES</td>\n",
              "      <td>2.750</td>\n",
              "      <td>0.029117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sodium_free_NO</th>\n",
              "      <td>sodium_free_NO</td>\n",
              "      <td>1.250</td>\n",
              "      <td>0.063886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sodium_free_YES</th>\n",
              "      <td>sodium_free_YES</td>\n",
              "      <td>0.750</td>\n",
              "      <td>0.105849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gluten_free_NO</th>\n",
              "      <td>gluten_free_NO</td>\n",
              "      <td>1.250</td>\n",
              "      <td>0.063886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gluten_free_YES</th>\n",
              "      <td>gluten_free_YES</td>\n",
              "      <td>0.750</td>\n",
              "      <td>0.105849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>organic_NO</th>\n",
              "      <td>organic_NO</td>\n",
              "      <td>-0.125</td>\n",
              "      <td>0.502188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>organic_YES</th>\n",
              "      <td>organic_YES</td>\n",
              "      <td>2.125</td>\n",
              "      <td>0.037662</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>multipack_NO</th>\n",
              "      <td>multipack_NO</td>\n",
              "      <td>0.250</td>\n",
              "      <td>0.296921</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>multipack_YES</th>\n",
              "      <td>multipack_YES</td>\n",
              "      <td>1.750</td>\n",
              "      <td>0.045708</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ・ If the significance level is 5%, it can be seen that all the factors are not statistically significant.\n",
        "# data_res['abs_weight'] = np.abs(data_res['weight'])\n",
        "data_res = data_res.sort_values(by='weight')\n",
        "data_res"
      ],
      "metadata": {
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          "height": 739
        },
        "id": "sTIrEEefScGZ",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            name  weight     p_val\n",
              "weight_400g                          weight_400g  -5.250  0.026254\n",
              "fat_free_NO                          fat_free_NO  -0.750  0.105849\n",
              "weight_300g                          weight_300g  -0.750  0.179010\n",
              "price_4.00                            price_4.00  -0.250  0.454553\n",
              "organic_NO                            organic_NO  -0.125  0.502188\n",
              "flavor_tomato_ketchup      flavor_tomato_ketchup   0.250  0.454553\n",
              "multipack_NO                        multipack_NO   0.250  0.296921\n",
              "flavor_cheese_onion          flavor_cheese_onion   0.500  0.260281\n",
              "price_2.00                            price_2.00   0.500  0.260281\n",
              "price_3.00                            price_3.00   0.500  0.260281\n",
              "flavor_chicken_flavored  flavor_chicken_flavored   0.500  0.260281\n",
              "sodium_free_YES                  sodium_free_YES   0.750  0.105849\n",
              "gluten_free_YES                  gluten_free_YES   0.750  0.105849\n",
              "flavor_salt_vinegar          flavor_salt_vinegar   0.750  0.179010\n",
              "price_1.00                            price_1.00   1.250  0.109244\n",
              "gluten_free_NO                    gluten_free_NO   1.250  0.063886\n",
              "sodium_free_NO                    sodium_free_NO   1.250  0.063886\n",
              "multipack_YES                      multipack_YES   1.750  0.045708\n",
              "organic_YES                          organic_YES   2.125  0.037662\n",
              "weight_200g                          weight_200g   2.500  0.055027\n",
              "fat_free_YES                        fat_free_YES   2.750  0.029117\n",
              "weight_100g                          weight_100g   5.500  0.025062"
            ],
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              "    </tr>\n",
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              "      <th>organic_NO</th>\n",
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              "      <td>-0.125</td>\n",
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              "    <tr>\n",
              "      <th>flavor_tomato_ketchup</th>\n",
              "      <td>flavor_tomato_ketchup</td>\n",
              "      <td>0.250</td>\n",
              "      <td>0.454553</td>\n",
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              "    <tr>\n",
              "      <th>multipack_NO</th>\n",
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              "      <th>flavor_chicken_flavored</th>\n",
              "      <td>flavor_chicken_flavored</td>\n",
              "      <td>0.500</td>\n",
              "      <td>0.260281</td>\n",
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              "      <th>sodium_free_YES</th>\n",
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              "      <th>gluten_free_YES</th>\n",
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              "      <td>0.750</td>\n",
              "      <td>0.105849</td>\n",
              "    </tr>\n",
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              "      <th>flavor_salt_vinegar</th>\n",
              "      <td>flavor_salt_vinegar</td>\n",
              "      <td>0.750</td>\n",
              "      <td>0.179010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>price_1.00</th>\n",
              "      <td>price_1.00</td>\n",
              "      <td>1.250</td>\n",
              "      <td>0.109244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gluten_free_NO</th>\n",
              "      <td>gluten_free_NO</td>\n",
              "      <td>1.250</td>\n",
              "      <td>0.063886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sodium_free_NO</th>\n",
              "      <td>sodium_free_NO</td>\n",
              "      <td>1.250</td>\n",
              "      <td>0.063886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>multipack_YES</th>\n",
              "      <td>multipack_YES</td>\n",
              "      <td>1.750</td>\n",
              "      <td>0.045708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>organic_YES</th>\n",
              "      <td>organic_YES</td>\n",
              "      <td>2.125</td>\n",
              "      <td>0.037662</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weight_200g</th>\n",
              "      <td>weight_200g</td>\n",
              "      <td>2.500</td>\n",
              "      <td>0.055027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fat_free_YES</th>\n",
              "      <td>fat_free_YES</td>\n",
              "      <td>2.750</td>\n",
              "      <td>0.029117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weight_100g</th>\n",
              "      <td>weight_100g</td>\n",
              "      <td>5.500</td>\n",
              "      <td>0.025062</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-48ccce52-7586-42eb-9fbe-8ac0177c0add')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-48ccce52-7586-42eb-9fbe-8ac0177c0add 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-48ccce52-7586-42eb-9fbe-8ac0177c0add');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xbar = np.arange(len(data_res['weight']))\n",
        "plt.barh(xbar, data_res['weight'])\n",
        "plt.yticks(xbar, labels=data_res['name'])\n",
        "plt.xlabel('weight')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "S-gv-xh6ShYl",
        "outputId": "fbfcc5ec-7c09-4a29-bb5c-1ec01a6c6d11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train a predictor to test the performance of new products\n",
        "\n",
        "# After going through the above EDA we will develop some predictive models and compare them.\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# We will use the data frame where we had created dummy variables scaling all the variables to a range of 0 to 1\n",
        "X = conjoint_dat_dum\n",
        "y = conjoint_dat['ranking'].astype(int)\n",
        "# The target variable will be normalized from a ranking to a 1 to 10 score\n",
        "y = y.apply(lambda x: int(x/len(y)*10))\n",
        "\n",
        "features = X.columns.values\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(X)\n",
        "X = pd.DataFrame(scaler.transform(X))\n",
        "X.columns = features"
      ],
      "metadata": {
        "id": "P1bJxpkG2uom"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "vSf8ZIHX3aPC",
        "outputId": "818d5cd9-2c7c-4bad-c827-47a582b0f658"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   flavor_cheese_onion  flavor_chicken_flavored  flavor_salt_vinegar  \\\n",
              "0             1.732051                -0.577350            -0.577350   \n",
              "1            -0.577350                -0.577350             1.732051   \n",
              "2            -0.577350                 1.732051            -0.577350   \n",
              "3            -0.577350                -0.577350             1.732051   \n",
              "4            -0.577350                -0.577350             1.732051   \n",
              "\n",
              "   flavor_tomato_ketchup  price_1.00  price_2.00  price_3.00  price_4.00  \\\n",
              "0               -0.57735    1.732051   -0.577350   -0.577350   -0.577350   \n",
              "1               -0.57735   -0.577350   -0.577350    1.732051   -0.577350   \n",
              "2               -0.57735   -0.577350   -0.577350   -0.577350    1.732051   \n",
              "3               -0.57735   -0.577350   -0.577350   -0.577350    1.732051   \n",
              "4               -0.57735   -0.577350    1.732051   -0.577350   -0.577350   \n",
              "\n",
              "   weight_100g  weight_200g  ...  fat_free_NO  fat_free_YES  sodium_free_NO  \\\n",
              "0     1.732051    -0.577350  ...          1.0          -1.0             1.0   \n",
              "1     1.732051    -0.577350  ...          1.0          -1.0            -1.0   \n",
              "2    -0.577350     1.732051  ...          1.0          -1.0             1.0   \n",
              "3    -0.577350    -0.577350  ...         -1.0           1.0            -1.0   \n",
              "4    -0.577350    -0.577350  ...          1.0          -1.0             1.0   \n",
              "\n",
              "   sodium_free_YES  gluten_free_NO  gluten_free_YES  organic_NO  organic_YES  \\\n",
              "0             -1.0             1.0             -1.0         1.0         -1.0   \n",
              "1              1.0            -1.0              1.0        -1.0          1.0   \n",
              "2             -1.0             1.0             -1.0        -1.0          1.0   \n",
              "3              1.0             1.0             -1.0         1.0         -1.0   \n",
              "4             -1.0             1.0             -1.0        -1.0          1.0   \n",
              "\n",
              "   multipack_NO  multipack_YES  \n",
              "0           1.0           -1.0  \n",
              "1           1.0           -1.0  \n",
              "2           1.0           -1.0  \n",
              "3           1.0           -1.0  \n",
              "4          -1.0            1.0  \n",
              "\n",
              "[5 rows x 22 columns]"
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              "      <th></th>\n",
              "      <th>flavor_cheese_onion</th>\n",
              "      <th>flavor_chicken_flavored</th>\n",
              "      <th>flavor_salt_vinegar</th>\n",
              "      <th>flavor_tomato_ketchup</th>\n",
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              "      <th>...</th>\n",
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              "      <th>0</th>\n",
              "      <td>1.732051</td>\n",
              "      <td>-0.577350</td>\n",
              "      <td>-0.577350</td>\n",
              "      <td>-0.57735</td>\n",
              "      <td>1.732051</td>\n",
              "      <td>-0.577350</td>\n",
              "      <td>-0.577350</td>\n",
              "      <td>-0.577350</td>\n",
              "      <td>1.732051</td>\n",
              "      <td>-0.577350</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.577350</td>\n",
              "      <td>-0.577350</td>\n",
              "      <td>1.732051</td>\n",
              "      <td>-0.57735</td>\n",
              "      <td>-0.577350</td>\n",
              "      <td>-0.577350</td>\n",
              "      <td>1.732051</td>\n",
              "      <td>-0.577350</td>\n",
              "      <td>1.732051</td>\n",
              "      <td>-0.577350</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
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              "      <td>-1.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.577350</td>\n",
              "      <td>1.732051</td>\n",
              "      <td>-0.577350</td>\n",
              "      <td>-0.57735</td>\n",
              "      <td>-0.577350</td>\n",
              "      <td>-0.577350</td>\n",
              "      <td>-0.577350</td>\n",
              "      <td>1.732051</td>\n",
              "      <td>-0.577350</td>\n",
              "      <td>1.732051</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
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              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.577350</td>\n",
              "      <td>-0.577350</td>\n",
              "      <td>1.732051</td>\n",
              "      <td>-0.57735</td>\n",
              "      <td>-0.577350</td>\n",
              "      <td>-0.577350</td>\n",
              "      <td>-0.577350</td>\n",
              "      <td>1.732051</td>\n",
              "      <td>-0.577350</td>\n",
              "      <td>-0.577350</td>\n",
              "      <td>...</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.577350</td>\n",
              "      <td>-0.577350</td>\n",
              "      <td>1.732051</td>\n",
              "      <td>-0.57735</td>\n",
              "      <td>-0.577350</td>\n",
              "      <td>1.732051</td>\n",
              "      <td>-0.577350</td>\n",
              "      <td>-0.577350</td>\n",
              "      <td>-0.577350</td>\n",
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              "<p>5 rows × 22 columns</p>\n",
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              "\n",
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              "        buttonEl.style.display =\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Logistic Regression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn import metrics\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=101)\n",
        "\n",
        "# Running logistic regression model\n",
        "model = LogisticRegression()\n",
        "result = model.fit(X_train, y_train)\n",
        "\n",
        "prediction_test = model.predict(X_test)\n",
        "# Print the prediction accuracy\n",
        "print(metrics.mean_squared_error(y_test, prediction_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kfYQdlQk28Y7",
        "outputId": "40a2966e-17fd-43be-96a8-dd42f4374fa7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10.125\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# To get the weights of all the variables\n",
        "weights = pd.Series(model.coef_[0],\n",
        "                 index=X.columns.values)\n",
        "print(weights.sort_values(ascending = False)[:10].plot(kind='bar'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "G22BDHad2-pL",
        "outputId": "b2a236cc-9ced-4b74-e1d1-3924a60595cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AxesSubplot(0.125,0.125;0.775x0.755)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(weights.sort_values(ascending = False)[-10:].plot(kind='bar'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "rjyIhaWO2_C_",
        "outputId": "0f860337-cc1b-479c-f587-0e47e3ba12e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AxesSubplot(0.125,0.125;0.775x0.755)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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BYECzZs3k1z4+PsjPz1d0jLy8PPj6+sqvfX19kZeXV+c2IiJyjjqH6oaHh8NgMFjctnfvXrsHcpQmTdxtPoaXl4cdkthOLTmsUUtGNeRQQwZAHTnUkAFQRw41ZABuPUedxSM5Odnqdl9fX+Tm5sLT0xNA1ZVC586dFYXw8fExK1AGgwE+Pj51blOiqKgURqOk+PtM1PJ8hYgc9viltjWjvf6w1JBDDRnUkkMNGdSSQw0ZrOXQajVWP3Tb3G0VEhKCxMREAEB2djYyMjLQo0cPRcfo168fdu7ciQsXLsBoNGLTpk3o379/nduIiMg5bH7CPDY2FvHx8QgODoZWq0VCQgLc3auq1ZIlS9C0aVNERUWhsrISvXv3xtWrV1FaWoqePXtCr9djwoQJ8PPzw7hx4zB8+HAAQPfu3TFkyBAAsLqNiIicw+bi4erqiqVLl1rcFhcXJ3/t4uJicRSWyYgRIzBixAjF24iISDw+YU5ERIqxeBARkWIsHkREpBiLBxERKcbiQUREirF4EBGRYiweRESkGJehpRrUsJ47EakbiwfVoIb13IlI3Vg8SJVsvfoxHYOIHIPFg1SprqsfgFdARM7EG+ZERKQYiwcRESnG4kFERIqxeBARkWIsHkREpBiLBxERKcbiQUREitmleJSXl2PixIkIDg5GSEgIdu3aZXG/goICPPXUUwgKCkJERITZtp07dyIiIgKDBg3CwIEDsXbtWnlbUlISOnbsiNDQUISGhmL8+PH2iE1ERLfILg8JrlmzBu7u7tixYweys7MRHR2NtLQ0uLm5me3n6uqKuLg4lJaW1lj33MvLCytXroS3tzdKSkoQERGBtm3bomPHjgCAbt261bpWOhERiWWXK4/t27cjMjISAODv74/WrVsjPT29xn4eHh7o2LEjGjVqVGNbu3bt4O3tLe8XEBCA3Nxce8QjIiI7s8uVh8FgQLNmzeTXPj4+yM/Pv+XjnThxAocOHcLcuXPl9/bv34/Q0FC4u7tj7NixePzxxxUds0kT91vOY+Ll5WHzMexBDTnUkAFQTw5r1JJRDTnUkAFQRw41ZABuPcdNFY/w8HAYDAaL2/bu3XtLDdemsLAQ48aNw+zZs+UrkccffxwDBgxAw4YN8dtvv2Hs2LFYt24dAgICbvq4RUWlMBqlW86llnmU1JBDDRlE5bDHH7itGe11klFDDjVkUEsONWSwlkOr1Vj90H1TxSM5Odnqdl9fX+Tm5sLT0xMAkJeXh86dO9/Moc0UFRUhJiYGY8aMQf/+/eX3TccFgIcffhiPPPIIDh8+rKh4EBGR/djlnkdISAgSExMBANnZ2cjIyECPHj0UHaO4uBgxMTGIjo6GXq8321ZQUCB/nZubi0OHDqFly5a2Bycioltil3sesbGxiI+PR3BwMLRaLRISEuDuXnW5s2TJEjRt2hRRUVGorKxE7969cfXqVZSWlqJnz57Q6/WYMGECPvzwQ2RnZyMxMVEuRKNGjcLQoUOxfv16fPPNN3BxcQEATJ48GQ8//LA9ohMR0S2wS/FwdXWtdRhtXFyc/LWLi4vFUVgAMG3aNEybNs3itsmTJ2Py5Mm2ByUiIrvgE+ZERKQYiwcRESnG4kFERIqxeBARkWIsHkREpBiLBxERKcbiQUREirF4EBGRYnZ5SJDon+pKxXVsXRxq0/cT/ROxeBBZUfJnOazNfaqWGYaJRGO3FRERKcbiQUREirF4EBGRYiweRESkGIsHEREpxuJBRESKcagukcrZ+qyJ6RhE9mRz8SgvL8f06dORmZkJFxcXTJs2Db17966xX0FBAaZMmYLffvsNzZs3R1JSkrxt3759ePbZZ+Hv7w8AaNCgATZt2iRvX758OZKTkwEA4eHhGD9+vK2xif42+KwJqZHNxWPNmjVwd3fHjh07kJ2djejoaKSlpcHNzc1sP1dXV8TFxaG0tNTikrUBAQFmBcXk559/xtdff40vv/wSAKDX69GpUyc8+uijtkYnIqJbZPM9j+3btyMyMhIA4O/vj9atW1tcp9zDwwMdO3ZEo0aNFB1/27ZtCAsLQ8OGDdGwYUOEhYVh27ZttsYmIiIb2Fw8DAYDmjVrJr/28fFBfn6+4uNkZ2cjPDwcer1e7qICgLy8PPj6+podPy8vz7bQRERkkzq7rcLDw2EwGCxu27t3r11CtGrVCnv27IGHhwdycnIQExMDb29vdOvWzS7HB4AmTdxtPoaXl4cdkthODTnUkAFQRw5muHlqyamGHGrIANx6jjqLR/WrAEt8fX2Rm5sLT09PAFVXCp07d1YUwt39rxO7n58fdDodDh48iG7dusHHx8eseOXl5cHHx0fR8QGgqKgURqOk+PtM1HJTUg051JBBLTlupwz2ONnZmtNeJ1w15FBDBms5tFqN1Q/dNt8wDwkJQWJiItq0aYPs7GxkZGRg8eLFio5RWFgILy8vaDQaXLx4ET/88APi4uLk48+fPx/R0dEAgJSUFLz22mu2xiYihTg9PVVnc/GIjY1FfHw8goODodVqkZCQIF9JLFmyBE2bNkVUVBQqKyvRu3dvXL16FaWlpejZsyf0ej0mTJiAtLQ0bNiwAfXq1UNlZSXCwsKg0+kAAJ07d0bfvn0xcOBAAEBYWBg6depka2wiUohDhqk6m4uHq6urxaG3AOSrBwBwcXGxOAoLAEaOHImRI0fW2saECRMwYcIE24ISEZHdcHoSIiJSjMWDiIgUY/EgIiLFODEiEf1tqGWSSI48Y/Egor8RtYz4UksOZ2K3FRERKcbiQUREirF4EBGRYiweRESkGIsHEREpxuJBRESKsXgQEZFiLB5ERKQYiwcRESnG4kFERIqxeBARkWIsHkREpBiLBxERKWbzrLrl5eWYPn06MjMz4eLigmnTpqF379419isoKMCUKVPw22+/oXnz5khKSpK3rVu3Dps3b5Zf5+TkQK/XY/r06di3bx+effZZ+Pv7AwAaNGiATZs22RqbiIhsYHPxWLNmDdzd3bFjxw5kZ2cjOjoaaWlpcHNzM9vP1dUVcXFxKC0trbHm+ahRozBq1CgAwLVr19CzZ08MGjRI3h4QEGBWbIiIyLls7rbavn07IiMjAQD+/v5o3bo10tPTa+zn4eGBjh07olGjRlaPt2vXLnh5eaFNmza2RiMiIgexuXgYDAY0a9ZMfu3j44P8/PxbPt7mzZsRERFh9l52djbCw8Oh1+uRnJx8y8cmIiL7qLPbKjw8HAaDweK2vXv32jVMYWEhfvrpJyxYsEB+r1WrVtizZw88PDyQk5ODmJgYeHt7o1u3boqO3aSJu835vLw8bD6GPaghhxoyAOrIwQx/UUMONWQA1JOjLreas87iUdcnfV9fX+Tm5sLT0xMAkJeXh86dO99SmJSUFPTq1Us+FgC4u/910vfz84NOp8PBgwcVF4+iolIYjdIt5QLUs6ykGnKoIYNacjCDunKoIYOoHPYqTrXl1Go1Vj9029xtFRISgsTERABV3UsZGRno0aPHLR1r8+bNGDp0qNl7hYWFkKSqk/7Fixfxww8/IDAw0LbQRERkE5tHW8XGxiI+Ph7BwcHQarVISEiQrxaWLFmCpk2bIioqCpWVlejduzeuXr2K0tJS9OzZE3q9HhMmTAAA/PLLL7h8+TIee+wxs+OnpaVhw4YNqFevHiorKxEWFgadTmdrbCIisoFGMn2s/4djt9U/K4NacjCDunKoIYOoHF5eHhj88hc2HWPr4lDndVsREdHth8WDiIgUY/EgIiLFWDyIiEgxFg8iIlKMxYOIiBRj8SAiIsVYPIiISDEWDyIiUozFg4iIFGPxICIixVg8iIhIMRYPIiJSjMWDiIgUs3k9DyIiEu9KxXVsXRxq8zFuFYsHEdHfUMmf5bC2Yoij1xRhtxURESnG4kFERIrZXDzKy8sxceJEBAcHIyQkBLt27bK4386dOxEREYFBgwZh4MCBWLt2rdn25cuXQ6fTQafTYfny5Te9jYiIxLP5nseaNWvg7u6OHTt2IDs7G9HR0UhLS4Obm5vZfl5eXli5ciW8vb1RUlKCiIgItG3bFh07dsTPP/+Mr7/+Gl9++SUAQK/Xo1OnTnj00UetbiMiIuew+cpj+/btiIyMBAD4+/ujdevWSE9Pr7Ffu3bt4O3tDQDw8PBAQEAAcnNzAQDbtm1DWFgYGjZsiIYNGyIsLAzbtm2rcxsRETmHzcXDYDCgWbNm8msfHx/k5+db/Z4TJ07g0KFD6NKlCwAgLy8Pvr6+ZsfIy8urcxsRETlHnd1W4eHhMBgMFrft3btXcYOFhYUYN24cZs+eLV+JiNCkibvNx/Dy8rBDEtupIYcaMgDqyMEMf1FDDjVkANSRw5EZ6iweycnJVrf7+voiNzcXnp6eAKquFDp37mxx36KiIsTExGDMmDHo37+//L6Pj49ZgcrLy4OPj0+d25QoKiqF0Sgp/j4TR4+Z/jvlUEMGteRgBnXlUEMGteSwNYNWq7H6odvmG+YhISFITExEmzZtkJ2djYyMDCxevLjGfsXFxYiJiUF0dDT0en2NY8yfPx/R0dEAgJSUFLz22mt1blNCq9Uo/h5HHMMe1JBDDRkAdeRghr+oIYcaMgDqyGFLhrq+VyNJ0q1/HAdw+fJlxMfH4+jRo9BqtZg6dSp0Oh0AYMmSJWjatCmioqKwcOFCrF+/Hvfff7/8vaNGjcLQoUMBAO+//z5SUlIAAGFhYZgwYYK8n7VtREQkns3Fg4iIbj98wpyIiBRj8SAiIsVYPIiISDEWDyIiUozFg4iIFGPxICIixVg8iIhIMRYPIiJSjMWDiIgUs3luq3+qyspKfPXVV8jKygIAtGzZEoMGDYKLi4vQDMuXL8dLL70krM26lJWVAUCNxb7o9nTt2jWcPn0aAHDfffehQYMGTsvy559/Yv/+/bj33nsRGBgotG01nC9E4/QkFuTn5+OZZ56Bu7s72rVrBwA4fPgwSkpKsGbNmlua1fdWDRs2DJ9//rmw9mpz4sQJvPLKK/j999+h0WjQokULLFy4EAEBAcIyDB06FBqN+WRtHh4eaN++PcaMGSOkoB04cADbt2+X15Tx8fFB//790bFjR4e3Xd0XX3xRI0dISAhCQ0Nr/IwcZeXKlVi1ahXq1asHjUaD69ev49lnn8ULL7wgpP0pU6ZgzJgxCAwMxMWLFxEaGgp3d3cUFxdj0qRJNSZgdRQ1nC+OHz8OT09PeXbzbdu24csvv8S9996Ll156Ce7uti9JcSMWDwvi4uLQoUMHPP3002bv/+c//8HBgwexdOlSYVmWLVuGRo0aISwsDK6urvL7jRo1EpYBAIYPH44nn3wSoaGhAIAtW7Zg/fr12Lhxo7AMb7/9Nk6fPo2wsDAAVSfQpk2b4vLly7h27RoWLVrk0PZXrFiBr7/+GmFhYfIJIS8vDykpKejXrx/Gjx/v0PZN5s+fj1OnTmH48OFmOTZu3IjmzZtj1qxZDs+watUqfP/995gzZ478AeLEiROYM2cOunfvjueff97hGQYMGCCvKvrJJ59gz549WLt2LfLz8/Hcc8/hiy++cHgGQB3ni2HDhmHFihVo2rQpMjIyMHr0aLzwwgs4duwY6tWrhzfffNP+jUpUg06nu6VtjtCyZUv5v8DAQPl/RRs4cGCN9wYNGiQ0w/Dhw81eV1ZWSnq9XjIajVJISIjD2w8ODpauXLlS4/3y8nKhvxfBwcEW36+srKx1m73169dP+vPPP2u8f/HiRalv375CMoSFhclfT5gwQVq/fr38OjQ0VEgGSVLH+aL63+KiRYukhIQESZIk6fr16xb/du2B9zwsEHXZfzNMfajO1qpVKxw4cEDunvnll1/QunVroRmKi4tRUVGBO+64AwBw9epVXLp0CRqNBg0bNnR4+5IkWfzd0Gg0kARfwF+8eBF33nmn2XuXLl0SlsPFxQUeHjVXqWvcuDHq1RN3WikoKEDjxo2xf/9+s3uDFRUVwjKo4Xyh1f419unXX3+V1z9ycXFx2H0XFg8L7rvvPqSlpaFv375m76empuK+++5zUirnysrKwlNPPSX//8/JyUGLFi0wbNgwABByX6Z///6IjIyUV6FMTU1Fv379UFZWhmbNmjm8/bCwMOj1eg+CI0kAACAASURBVISFhcHX1xcAYDAYkJKSIneliTBmzBiEhoYiODhYzpGbm4tvvvkG48aNE5JBo9GgoKCgxlLS+fn5QtoHgGeffRZhYWGoX78+goKC8O9//xsAcOjQIfnnIoIazhf33HMP1q9fD29vb2RmZqJr164Aqj5gXbt2zSFt8p6HBb///jueeeYZdO7cWb4BdujQIezfvx9r165FixYthGXJysrC7NmzkZWVhatXr8rvHz16VFgGANi/f7/V7Z06dRKS49tvv5WzdOrUCX369BHSrsnPP/+M7du3y0sj+/r6IiQkRNj/f5OcnBykpaWZ3TDv27cv/Pz8hLS/ceNGfPrpp4iPjzf7G1m4cCFGjhyJyMhIITnOnTuH8+fPIzAwUL4CKCgoQGVlpbACoobzhcFgwNy5c1FQUIDY2FgMHjwYALBnzx7s27cPr7zyit3bZPGoxaVLl/DZZ5/h2LFjAKqG3o0cORKNGzcWmmPEiBGIi4vDggULsHr1aqxfvx5ubm549tlnheYgulFKSgqWLVuGs2fPQqPR4N5778X48eOFXYUZDIZaC0RmZiZatWolJAegnvOFSCweKhcREYGkpCQMHjwYW7duBVA1ZHXz5s1Cc1gaJguI6a6qqKjA6tWrsX37drlbxDQ0NTY2Vsj9DhMO1a3JWc/+hIeHIzk5GUDNIe3Vt90OPvvsM4wcORIAkJ6ejp49e8rb3n//fYcs3c17Hha89dZbtW7TaDSYOnWqsCymm12NGzdGVlYWvL29UVxcLKx9k2nTpslfV1RU4KuvvkLTpk2FtD19+nS4urrizTffNLvX8L///Q/x8fF47733hOSoPlTXVCzy8vKQkJCgmqG6hw8fFjJU9/jx41a3m+4/OFL1z73Xr1+vdZujqeF8sXnzZrl4vPvuu2bF49tvv2XxEKX68xQm169fx9atW5Gfny+0eAwYMADFxcV49tlnERUVBaPR6JQnzm/s03/ssccQFRUlpO3MzEykpqaavefp6Yn58+ejX79+QjIAVd00W7dulUd7mTz55JMYPHiwsOKRnp6OtLQ0s/fatm2L4OBghISECMlgqdtUo9GgrKwMly5dEnJPrvoV1o1XWyKvvtRwvqheLG8snI4qpCweFrz44otmr7dv344lS5bgwQcfxIoVK4RmiYmJAQD07NkT+/fvR0VFhUOeFlWqtLQU58+fF9KWVqtFTk5OjZvBZ86cEXqS4FDdv3z77bdmry9fvoyPP/4Y//3vf2s8LOcoFRUVOHHiBCRJMvvatE0UNZwvnFFIWTys+OGHH7B48WI0atQIr7/+OoKCgoRn2LNnT4333N3d0aJFC4vj7B2l+j0Po9GIs2fPyoXN0aZOnYqoqCi0bt1aHpKbm5uLI0eOICEhQUgGgEN1Lbl+/To2bNiAjz76CL169UJSUlKN4buOcuXKFYwdO1Z+Xf1rZzx74czzRUFBgdx9Vv1rSZJQWFjokDZ5w9yCw4cP45133sHFixcxadIk9OrVy2lZIiMjceTIEXm43++//46WLVuioKAA8+fPR+/evYXkqD5U18XFBX5+fsLueQBVn2zT09PNbhD36NFD+E3aAwcOYNu2bU4fqnv27FmkpqY6bagu8Ndoq9atWyMuLg7333+/sLbVRA3ni2XLllndfuPVkT2weFgQGBiIZs2aoW/fvhY/wThizHRtpk6ditGjR8tPc2dmZuLjjz/GCy+8gMmTJwubv4eousGDB+Py5cuYMGGCxZkGRNwwnzx5MubMmYN//etfDm/LGjWcL86dOwcvLy+Ht1Mdu60sGD9+vCqmHACqHhKs/sfZqlUr/P777wgICBDaz37w4EEsWrQIOTk5qKyslPv/f/zxR4e3bRqq+/XXX9cYmip6qG5tdu3aJewq0JrExEQhD+iZhucuXbq0xj0fjUaDb775xuEZAgICEBYWhtdee82pP3s1nC+GDh2KV155BYMGDRLXqENmzLpNbNq0yeFt6PV6aevWrfLrrVu3Snq9XpIkSRoyZIjD2zcJCQmRtmzZIp05c0Y6e/as/J8IkyZNkmbMmCFlZGRIRUVFUlFRkZSRkSHNmDFDiouLE5KhLjNmzHB2BEmSJGnJkiXOjmCmqKjIocfPysqSIiIipPj4eCkrK0v6448/5P/UxpHni5MnT0pRUVHSiy++6PCfuQm7rWwg4kGkEydOYOrUqfjjjz+g0Wjw73//GwsXLkSzZs3wf//3f+jevbtD2zdx5kNX/fr1qzFU92a2kfOJ+L35448/EBUVBQ8PD/kKQNTVjxKO/llIkoRPPvkE69atQ9++fc0mS3RE1xm7rWwgou4GBAQgKSkJpaWlAGA2TFdU4QCqhgrv2bPHKTcD1TJU15pLly4JnYqipKQERqMRjRs3xqVLl7B//34EBATggQceEJbhZjj6b2Tt2rVYt24dEhISMGDAAIe2ZStH/yyMRqN8nmjUqJHDVzFk8bCBiBOXJEn4/PPPcfr0aUyZMgVnz55FYWEhHnnkEYe3DQBdunSR+7RXrVoFNzc3NGjQQOg9D7UM1T1y5AgmTZqEgoICPP7445gzZ468ctvTTz8t7Mps27ZteO2116DVapGQkICVK1eiadOmyMrKwsyZM4U9KHgzHPk3MmLECDRp0gSbN29GkyZNHNaOvTjyZ3Hs2DHEx8fjwQcfREpKipBBBCweKrdgwQIUFRUhMzMTU6ZMgZubG9544w1hS9OKnkPLkj59+qBLly5mQ3UfffRRLF68WOhQ3TfeeAMzZsxA+/bt8dlnnyE6Ohpr166Fj4+P0MELH330EbZv346ysjJERERg48aNePDBB5GTk4OJEyeqqng40ogRI27q+Zob53r6J3ruuecwc+ZM6HQ6YW2yeNhAxAlj3759SElJQXh4OADgrrvuEvr0rOmT/oULF+Du7o4GDRoAqFonwHSJLIKrq6vTT4qXL1/G448/DqBq3Pz999+P0aNHY82aNcK7z0zP2Nxzzz148MEHAQB+fn6orKwUmqMujvwbudkHM2+c68lZHPmzSE5Oxl133VXnfnPmzMGcOXPs0qa27l1ub6WlpcjMzLS4zSHrAt/gjjvuMDsxGY1Gh7dpyXPPPWd2Yrp+/bqQdaqBqkK1cuVKvPbaa9i9e7fZtnnz5gnJAFQNGa7+Mxg4cCCmTp2Kp59+GhcuXBCWo3qG0aNH17pNBEsfIEwPUAJAcHCwyDgWibwqdNb54mYKB1C1yqC9sHhYsWfPHgwcOFCekTIjI8PshBkYGOjwDC1atMCWLVsgSRLOnj2LOXPmOGWalKtXr6JRo0bya1dXV2FXQHPmzMHvv/+OBx54AG+//TZef/11edvBgweFZACArl274vvvvzd7Lzg4GDNnzjRbqMvRBgwYIJ+0R4wYIb9/4sQJBAQECMsBABMmTDCb0bawsNBsmhBRk0VaI+qqUA3nC5FYPKxYunQpPv/8c/nmU5s2bXDmzBmhGeLj47F//36cO3cOw4cPh9FoFDqrb3XVP10XFRUJuwrKyMjAu+++i5iYGHz++efIzc3Fq6++CkmShH6qnDVrlsXRZr179zYbOJCenu7QHM8//7zFyTEDAgLMpqd3dA4A6NGjB6ZPnw6g6vdjzJgxiIuLc3i7aqSG84VIvOdRhxsf+Tf1+Yvi7u6O+fPnC23TkqeeegpRUVEIDQ0FULUYkajVDKt3xTRs2BDvv/8+pkyZgqlTpzqtG88atfSxi8jxzDPPYP78+Xj99dfxyy+/YOzYsTXW8nY2kR8wnH2+EInFwwo3NzecP39evuzdt2+f0JlsTX788UecOXPGrHsgOjpaaIZhw4bBz89PnuV33rx5wiYDvPvuu5GVlSVf9ru4uGDx4sWYNm0a/vjjDyEZlFDLc7eOzFF9MSi9Xo9XX30VnTt3xkMPPYTjx48LmdvqRhcuXJCHTlc3adIkIe2r5XxhTfUHB23FJ8ytOHz4MGbPno2zZ88iMDAQ2dnZWLlypcWJ4BwlPj4eR44cwcMPP2z20M+CBQuEZQCqrjRMVx3W3nOE7Oxs1K9fXx75ZSJJEtLT050667ElalkC1ZE5+vTpU+s20U93//rrr5g4cSKMRiP27NmDjIwMbNy4UehgCkAd54u9e/eiTZs2ctH6888/kZmZia5du9q/MSGToPyN/fnnn9Lu3bul3bt3S5cuXRLeft++faWrV68Kb/dGYWFhN/WeMw0dOtTZESRJUs/PRS05HC0yMlL6448/pNDQUPm9AQMGOCWLs88XoaGhktFolF9XVlY67PeA3VZ1OH/+PK5duwadToeysjKLK7g50j333COsLUsyMjJw+PBhFBcXY/369fL7paWluHbtmhOT1XTjOtbOIqnkYl5EDqGfdGtx7dq1Gt1k9evXF9Z+dc4+X0g3rHap1WodNnybo62sSE5OxgsvvCB3ERUUFGDixIlCM/j7++Ppp5/Gxx9/jPXr18v/iVJQUIAjR46gvLwcR44ckf87d+6c8K6zuoh+UK+2ZztE9bGrIcdbb71lNvLL3d1dXsVOlAYNGqCsrEz+9z9+/HiNdeZFUMP5ws3NzexZjl9//dXiGuv2wCsPKz755BNs3rxZvjn9wAMPCFu32+Tq1au477778Pvvvwtt10Sn00Gn0+H777/HY4895pQMalNXH7uoezBqyCHyk25tnn/+ecTGxqKwsBDx8fH47rvvsGjRIqEZAHWcL6ZOnYrx48fLV2LHjx+vc5XBW8XiYUX9+vVrzJ3k6Jkqb6SWT/dBQUF47733kJOTg8WLF+PEiRM4deqU0Ll06iKqu2jBggX46KOPMGXKFABV4/nj4+OFtK22HKZPuu3atQPg2E+6tenVqxceeOABfPfdd5AkCS+88AKaN28uNAOgjvNFhw4d8NVXX+HQoUMAgPbt2ztstmcWDyvuvPNOnDp1Sv5k9cUXXwi7B/HLL78gKChIHhp7I9EjjObMmQMvLy9kZWUBqLoX8/LLLwstHqWlpTUejqv+nukE5mhq6WNXQw6Rn3Stueuuu9CuXTu0atVKeNsmzjxfVNe4cWMh5wcWDyteffVVvPzyyzh16hT69OmDhg0b4oMPPhDSdnJyMoKCgrB69eoa2zQajfDicezYMSxcuFCensPNzU34A3pPPfVUjaGn1d+bO3eukBxq6WNXQw6Rn3Rrs2fPHsyaNQsuLi749ttvkZGRgeXLlwv7WzVx5vli9OjR+OSTT+QlFEwkBy6dwOJRC6PRiDNnzmDTpk3Izs6GJEm4//77hV2Gmp4q//TTT4W0V5cbn5StqKgQ1k10/fp1XLt2DUajEVeuXJHbLSkpQXl5uZAM1amlj10tOUR90q2NaVoQ05xazpgWxNnnC9O/u8glFFg8aqHVavHee++hV69ewiebq2706NEYOnQo+vbti4YNGzotR8eOHfHBBx/g6tWr2LdvHz7++GOrD4rZ0wcffIBly5ZBo9Ggffv28vvu7u6IiYkRkqE6tfSxqyFHVlYWZs+ejaysLLPJIY8ePSo0h7OnBXH2+cI0Rf+ND9I6EouHFYGBgTh8+DDatm3rtAwxMTFISkrCggUL8MQTTyAiIkLYKoLVTZo0CatXr4abmxsWLVqEPn364LnnnhPS9osvvogXX3wRCQkJmDVrlpA2rblw4QK8vb3x5JNPAqgaEVfb1Bj/9Bxz5szBxIkTsWDBAqxevRrr168XukAXoJ5pQdRwvjh48CAWLVqEnJwcVFZWOrTbitOTWDFkyBCcOHECzZs3NxtBImoVv+qKi4vx1VdfITk5GWVlZfj666+Ftn/gwAF07NjR7L2UlJSbXpDHnq5evWo2HLT6VPEi6PV6rFu3Tm738uXLePrpp7Fx48bbLkdERASSkpIwePBgbN26FQAwdOhQod0napgWBFDH+aJ///4YN24c2rdvbzaPlSOuSHjlYcXMmTOdHUFm+kWQBE9DbpKQkID33nsPDzzwAABg+/bt+M9//iO0eOzYsQPz5s1DYWGhvK66RqMR3kXizLVN1JbD1KffuHFjZGVlwdvbG8XFxcLaNxqNaNCgAdatWyev7dKhQwcha3jfSA3ni4YNG2Lw4MFiGnPIpCd/c9HR0ZIkSdJbb73l5CSS9M0330gvvvii1LVrV2nmzJnSgQMHnJIjKytLGjx4sFRYWCh988030sCBA6Vz584JzaDT6aRffvlFqqysFNrujYYMGSIVFRXJr8+fPy8NGjTotsyxdu1a6cKFC9KePXuk9u3bS23btpVWr14tNIMzfvbVqel88c4770i7d+8W0havPCwoKipCcXExvv/+e0yYMKHGJ32R3SSffvopwsPDsWjRIqfeMG/ZsiWmT5+OZ555BkajEWvXrsXdd98tNEPjxo2dcr/nRs5c20RtOUwDFnr27In9+/ejoqLC4kJVjtS8eXOcPXsW9957r9B2TdR0vkhMTMSqVavg5uaGBg0a8J6HaO+++y7+85//4OrVq/IvgjO7SawZN24cVqxY4bDj3zhP0ffffw9/f3/5D/WVV15xWNsmpuG469atg4eHBwYMGGD2PIPoex5A1U1Z0wOcjz/+uLC1TdSYY8+ePfjpp58AAF26dBE+bDcmJga//vorgoKCzO41LFmyREj7ajpf5ObmWnzfEfc8WDysiI6OFjoJ4a0ICwtDSkqKw45f19PCL774osPaNgkMDJT/GE3UWsxvN++++y6+/fZbDBw4EEDVvbDevXsLnRCwtjVLwsPDhWUA/h7nC3ti8bCBoz/13wy1LDy0YsUKjBs3ztkxHGrRokWYOnUqXnrpJYsz+Ir6pKuWHADQr18/JCcny5/4L1++jPDwcKSmpjq87cmTJ+Odd97BJ598gtGjRzu8PVuJOF/k5eVh0aJFyMrKMhs84YjFuXjPwwYGg8HZEVRjx44dDi8elp4mF9llFRQUBADo3bu3sDbVnAMA/vWvf5n9G9xxxx3CRjqZliBOSUn5WxQPEeeLV199FQMGDMDRo0fx9ttvY8OGDbjvvvsc0haLhw1Erx+hZiIuYDt06FDjZ16vXj20bdsW8+bNk4cRO0qfPn1QWVmJnJwcvPTSSw5t6++QA6j6NxkzZozcRbRlyxazCT0def+jdevWCAoKQkVFhdniU468SWwLEeeL4uJi+fmfDh06oF27doiMjHRI9zKLx9+cs1caNBHxhzFp0iTccccdGDZsGCRJQnJyMoqLi+Hn54fZs2cLmQfMxcUF6enpTj9pqyWH6X5TYmKi/F5mZiYyMzMdPoHnggUL8PLLL2P06NH48MMPHdbO34lpVmVXV1cYDAbcfffdtS4WZjMhA4L/oUSsET1ixIibes/ZRPwswsPDa31P5Fj/999/X1q9erV0/vx56fLly/J/oqklh7OVlpZa3T579mwxQeog4m/kzTfflIqLi6Xk5GSpc+fOUo8ePaTXX3/dIW3xysMGIj71X7lyxey10WjEpUuXHN6uUpKAbqvy8nLk5OTAz88PAJCTk4PLly8DELvojmkEWvUZbJ0x6suZOUz/DsePH7e4/cZ1Rhyprrm0qi/L6kwizhfTpk0DUDUKs1OnTigtLUWLFi0c0hZHW9XhzJkzOHPmjNlcSiLGsa9evRqrV69GaWmp2SRvV65cweDBg5GQkODwDDcqLi6W/xDbtWuHu+66S94mYkK+1NRUzJo1S56zKDMzE3PnzsVjjz2GTz/9FM8//7xD26e/PPfcc1i1apXFmZU1Go1DRvfcKpEjEp11vjCxVMw9PDzg7e1t97ZYPKxYvHgxNm3ahICAAHluKY1Gg3Xr1jm87ZKSEly6dAnz5s0zm0nW3d1d+GI7APDdd99h6tSpePjhhyFJEo4dO4ZFixahe/fuQnOcP38ehw8fBlBVwJo0aSK0fZMLFy7IhbR9+/ZmhfR2zKFmoorHW2+9hZSUFNx///3Czxcmffr0QV5envyBs6SkBE2aNEGDBg3wzjvvmC1pYDOHdIb9Q+h0OqmkpMTZMVQhPDxcOn78uPz6+PHjFu9B3A5SU1OlTp06STExMVJMTIzUpUsXaceOHbdljpdeeumm3nMmEfcaJEmSgoODnX7Paf78+Wa/Azt27JDmzZsn/fTTT9KwYcPs2hbveVjh5eUlfJ4ek6FDh1odwSR6Wvjr16+bLXITEBCA69evC2nbGUtsWvPuu+/if//7H+6//34AQHZ2Nl544QWh67mrJYelFftOnjwprP2bUX1qckfy8fFxylr21e3fvx8zZsyQX+t0Orz//vuYOXNmjfuntmLxsKJ9+/aYPHkyQkJCzOZSEtGHabrxpRaenp5ISkpCREQEgKopIUQtOuSMJTatueOOO+QTNgD4+/s7ZdJKZ+bYuHEjEhMTkZ2djWHDhsnvl5SUmGUSIS4ursZT9dXfE/V7Ex8fj+effx7du3c3W8kwOjpaSPtA1YCagwcPyhOI/t///R+MRiMA+xdRFg8rMjIyAJivI+7osesmzpporzZz587F1KlTMWfOHADAQw89JGy97OpLbJaWluL06dNo1aqVkLYteeKJJ7By5Ur5eZOkpCQ88cQT8vrqop56d2aO7t27o3nz5pg3b57Z5Jju7u5o2bKlw9q1RC1XPx9++CHOnTuHo0ePCh39V93s2bMxadIk+UPElStXsHjxYpSVleHpp5+2a1u8Ya5yJSUl+Oijj3D06FGzuWpE3oSrrqysDEDdwyMdYc+ePZg1axZcXFzw7bffIiMjA8uXL8cHH3wgNEdgYGCt20QO2VVLDmcxXf2cPHnSrEvVdPUj+veib9++SE1NddrME0ajEd999x26du2KU6dOAQDuv/9+h63nzisPKyRJwueff47Tp09jypQpOHv2LAoLC4WuKfHqq68iICAA2dnZiIuLw+bNm53yqTsqKgobNmwwKxqm90RZunQpPv/8c4wdOxYA0KZNG4ufOh0tKytLeJuWODNHbZMymoiYnFFNVz9AVbfh5cuXnfLBCqjqlnrvvffQq1cvIf//WTysWLBgAYqKipCZmYkpU6bAzc0Nb7zxhtCb1adPn8b777+Pb775BoMGDULfvn0xatQoYe2b3HizrbKy0ikPK3p5eZm9dtSnKrJODZMyNmvWDM2aNcOXX37p7CgAqopWREQEevToYfZ7KWLNG5PAwEAcPnwYbdu2dXhbLB5W7Nu3DykpKfKkb3fddZfwNaJNv4T169fHxYsX0bhxY8fNVWNB9YcVq08+Z3pYUSQ3NzecP39e/sS7b98+swcoSRzRa2VYo5au3QceeMDhk3PWJTMzE1FRUWjevDlcXV3lEYmO+MDL4mHFHXfcYXZpbhq1IJK/vz8uXryIwYMHIzIyEh4eHkK7rSIjIxESEqKKhxWnTJmCsWPH4uzZs3jqqaeQnZ2NlStXCs1A5tSwpohaunZFLIxWl5kzZwKoujep0WjMVla0NxYPK1q0aIEtW7ZAkiScPXsWH374obyWgihvv/02gKqlNtu0aYOSkhL06NFDWPseHh7w8PDAqlWrhLVZm2PHjuH111/HuXPnAFRNBy5q7QiyrHr3VUVFBVJTU81uXouglq7dG5dsNhHZbXXPPfdgypQp8mCJVq1aOWxUJIuHFfHx8XjzzTdx7tw5DB8+HH369BH+/EVpaSlcXV2h1Wrxr3/9CwUFBU65AhK5QlltfvvtN6xZswbXrl1Dly5dUFJSgi5duuDuu+8WloHM3dh9FRERgdjYWKEZnN21a1L9U35FRQV2794tz8MmyuzZszF8+HAMHToUAJCUlIRZs2bh448/tntbLB51mD9/vtnr0tJSoe2PGjUKn332GcrKyhAbG4sWLVrgu+++w5tvvik0h8gVymoze/ZsAEB+fj52796Nd955B3l5ef/4Ial/JxqNBgUFBULays7Ohr+/v9O7dk1u7LZ67rnnEBcXJzTDhQsXzB7aHDp0qMPu/bB4WPHUU0/VmFDN0nuOJEkSXF1d8dVXX2H48OGYMGGC8BvVgNgVympz5MgR/Pjjj/jxxx9x7tw5PPbYY2Y38Um86vc8pP8/YWa3bt2EtD158mQkJSXh3LlzuPPOO53WtVsbNzc34UtVa7VanDx5Ur5xf+rUKYc9sMjiYcH169dx7do1GI1G+WldoGpUh6V1tB2poqICV69exQ8//ICRI0cCEDdXT3VCVyirxbBhw9ChQwdMnjwZjz76qNC2ybLq9zxcXFwQGxuLdu3aCWn7ypUrSE1NhcFgkJe9Bar+Pn744QehU6ED5vc8JEnCkSNHhN//mTRpEqKjo/HQQw8BqHoWqLZ7MbZi8bDggw8+wLJly6DRaMymMHZ3d0dMTIzQLAMGDJAfhnrkkUdw7tw5s3m2ROnYsSMuXryIqKgoREREoEGDBujXr5/QDBs3bsRPP/2EFStWoKioCI888gi6deuGvn37Cs1Bf3HmkN3JkycjMTER58+fx+rVq822iZpGqLrq9zxcXFwQFRWF4OBgoRl69uyJr776ymzdHUfNQcfpSaxISEgwG57qLJcuXYKHhwe0Wi3KyspQWloqL+6Snp6Onj17Cs1jMBgcukJZXQoKCrBr1y58+OGHvOfhZCdPnsQHH3yAM2fOmM2yLPJB2gULFmD69OnC2qMqLB5W1NZFJWriu5shcpW08vJy5Ofnm62SJnK50YSEBPz000+4cuUKunTpIv9nmjiRxBs8eDBCQ0PRqlUrs751tU3s6UiffPIJRo8erYqhuiKx28qKDh06QKPRyE9pmqjpk66o2r9u3Tq8++67uPPOO+WfhejlRlu2bImnn35a+Cgvql29evUwZswYZ8dwKlM3siMfyFMjXnncpIqKCmzduhXFxcXyxHxqIOrK44knnsB///tfh6yFTH9f77zzDoKCgoTfXyDn45XHTbrjjjswbNgwDB06VFXFQ5R77rmHhYNq6Nq1K8aNGwetVosGDRo4bXVHZ6prNBO7rW5D1e95GI1GrY5fMAAACN5JREFUZGRkoKSkxImJahJ14ThhwgTMmDEDvXr1Er6qIqnXrFmzsGDBArRq1copQ8jVwNRddebMGfz888/yCKudO3f+o4eUs3hYUf2eh4uLC5o3b262PrCjVVZWYvbs2TWecq9u0qRJQrLs2rULu3btQnZ2tnyScMZwSFKXxo0bIyQkxNkxnMr0oOyoUaOQlJSEu+66CwDwwgsvCH/CXCQWDyucveiPi4sLjh07ZnUfUSfvHTt24Ntvv3XKWt2kXjqdDhs2bED//v3NrkjVNCJRlPPnz8uFA6hawuH8+fNOTORYvGFuQV1PkYv8wzCtPxwWFmY2mkPkEFkAGD16NNasWYN69fh5g/5SfSnc6iMT1TQiUZSXXnoJHh4e8txSSUlJuHTpEpYuXerkZI7B4mFBYGCg/IdwI9F/GH369LGYQeQQWaCqb/vEiRPQ6XRmq6RFR0cLzUGkVqWlpVi2bBn2798PAOjcuTPGjx8Pd3d3JydzDBYPKy5fvlzjvUaNGjltgXtnqu0J3gULFghOQkRqwOJhhekKpLp69eqhbdu2mDdvnrAlJ48fP459+/YBALp06SJ8sjUiqlt5eTlWrFiBvXv3AgAee+wxPP/88//Y+z8sHlasWrVKfr5DkiQkJyejuLgYfn5+SE5OxqeffurwDCkpKVi8eLF8Yzw9PR1TpkzBkCFDHN52dZIkITEx0ewPQ6/X35ZXYUSWvPrqq6isrMTw4cMB/DW/1z/16px3P61ITU1FUlKS/HrUqFGIiIhAUlKSQ1bmsmTt2rVISkqCl5cXAODcuXOIjY0VXjzeeustHD16FBEREQCqilp2dvY/9gEoIqUyMjKwdetW+fUjjzwi/O9UJBYPK8rLy5GTkwM/Pz8AQE5OjnwfxFELrFhiKhw3fi3S999/j+TkZHm0Vf/+/REREcHiQVTN5cuX5VGRlu6Z/pOweFgxceJEDBs2TF6HODMzE3PnzkVZWZmwB6Puu+8+LF26FJGRkQCATZs2ycVMtOpdVOyuIjJnWgZ34MCBkCQJ27dv/0dfefCeRx2KiorMFlZp0qSJ8Pbnz5+PvXv3QqPRoFu3bpgxY4bwHAsXLsSxY8fkxX9SUlLQokULTJs2TWgOIjVLT0+X5/Vq1aoVBg0a5OREjsPiQTfFaDQiMTFR/sPo2rUrRowYwSsQov9v0qRJSEhIQP369REaGori4mI899xziI2NdXY0h7g9ZzL7G9HpdFi5ciXy8/OdmmPfvn2IiorC0qVLsXTpUkRFReGnn35yaiYiNTl16hQ8PDywe/dudOnSBenp6UhJSXF2LIdh8VC5lStX4s8//4Rer0dMTAy2bt2KiooK4TksTTtd11TURLcT0zK8P//8M3r27ImGDRv+o2ca5g1zlXvwwQcxbdo0TJkyBenp6di0aRPmzZsnT4HgaKdPn0Z2djZKS0uxZ88e+f2SkpI65wAjup0EBARgzJgxOHnyJF5++WVcuXLF2ZEcisXjb+LkyZPYv38/MjIy0KpVK2HtHjx4EElJSTh//jxWr14tv+/u7o74+HhhOYjUbuHChfj+++/RsmVLuLq6oqCgAC+//LKzYzkMb5ir3Lp165CSkoKysjKEh4cjNDQUPj4+wnMkJSXJDwhakp6ejp49ewpMRETOxOKhcjNnzkR4eDiCgoKcHcUqUWupE5E6sNtK5aytIqgm/AxCdHth8VC5vLw8LFq0CFlZWWajrESv51EXPu9BdHv5544j+4d49dVX0bVrVwDA22+/jaCgIPkpbyIiZ2HxULni4mLo9Xq4uLigQ4cOePPNN82GzKoFu62Ibi8sHipXv359AICrqysMBgOuX7+OCxcuCM1QWVmJmTNnWt1n0qRJgtIQkRrwnofKdezYERcvXkRUVBQiIiLQoEEDYTP6mri4uODYsWNW9zEtVkVEtwcO1f0bMRgMKC0tRYsWLYS3vXjxYpSVlSEsLExerwAA/v3vfwvPQkTOx+KhUnVN/SF6XeQ+ffrUeE+j0ahu1BcRicHioVKBgYHy8Ncb/4k0Gg2OHj3qjFhERAB4z0O1HnroISQnJ2PUqFFYt26ds+MAAI4fP459+/YBALp06YKAgAAnJyIiZ2HxUKmKigqkpqYiLy/P4tBc0TeoU1JSsHjxYrndVatWYcqUKf/oZTaJqHbstlKpnTt3IjExEQcOHJDXUDfRaDTCr0aGDBmCNWvWwMvLCwBw7tw5xMbGYsuWLUJzEJE68MpDpXQ6HXQ6HRYsWIDp06c7Ow4AyIXjxq+J6PbDhwRVTi2F47777sPSpUtRUFCAgoICLFu2DH5+fs6ORUROwm4ruilFRUWYP38+9u7dC41Gg27dumHGjBlo0qSJs6MRkROweBARkWLstqKbotPpsHLlSuTn5zs7ChGpAIsH3ZSVK1fizz//hF6vR0xMDLZu3Wq2vggR3V7YbUWKVFZWIj09HZs2bcKBAwewf/9+Z0ciIifglQcpcvLkSezfvx8ZGRlo1aqVs+MQkZPwyoNuyrp165CSkoKysjKEh4cjNDQUPj4+zo5FRE7ChwTppvz++++YMWMGgoKCnB2FiFSAVx5ERKQYrzzopuTl5WHRokXIysoyG2XF9TyIbk+8YU435dVXX0XXrl0BAG+//TaCgoIQHh7u5FRE5CwsHnRTiouLodfr4eLigg4dOuDNN9+0OFU8Ed0eWDzoptSvXx8A4OrqCoPBgOvXr+PChQtOTkVEzsJ7HnRTOnbsiIsXLyIqKgoRERFo0KABQkJCnB2LiJyEo61IMYPBgNLSUrRo0cLZUYjISVg8yKry8nKr2xs1aiQoCRGpCYsHWRUYGAiNRgMAuPFXRaPR4OjRo86IRUROxnseZNVDDz2E5ORkjBo1Svi66USkXiweZFVFRQVSU1ORl5dncWhur169nJCKiJyN3VZk1c6dO5GYmIgDBw6gdevWZts0Gg2vRohuUywedFMWLFiA6dOnOzsGEakEiwcRESnGJ8yJiEgxFg8iIlKMxYOIiBRj8SAiIsVYPIiISLH/B7ld08Tm79dpAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "model_rf = RandomForestClassifier(n_estimators=1000 , oob_score = True, n_jobs = -1,\n",
        "                                  random_state =50, max_features = \"auto\",\n",
        "                                  max_leaf_nodes = 30)\n",
        "model_rf.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions\n",
        "prediction_test = model_rf.predict(X_test)\n",
        "print(metrics.mean_squared_error(y_test, prediction_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nRG0SiZS4aHL",
        "outputId": "f0af8921-3bac-4cba-cb10-4bcd61d06001"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10.125\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "importances = model_rf.feature_importances_\n",
        "weights = pd.Series(importances,\n",
        "                 index=X.columns.values)\n",
        "weights.sort_values()[-10:].plot(kind = 'barh')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "gEbR0tO9-A6B",
        "outputId": "b8e7b88a-b44f-49d3-f160-d3333dc36793"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbf7accd650>"
            ]
          },
          "metadata": {},
          "execution_count": 138
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBClassifier\n",
        "\n",
        "model = XGBClassifier()\n",
        "model.fit(X_train, y_train)\n",
        "preds = model.predict(X_test)\n",
        "metrics.mean_squared_error(y_test, preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WLxKRi8Z-Lzh",
        "outputId": "8c95c07b-f407-4f97-8bad-76cc62843772"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17.5"
            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    }
  ]
}