2.2.Regression.ipynb

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    "colab": {
      "name": "2.2.Regression.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H0_rANGC9I7F",
        "colab_type": "text"
      },
      "source": [
        "## Import Libraries and Load the Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DCM__jlt6Lny",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import sklearn\n",
        "import sklearn.linear_model\n",
        "import warnings"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z32XP2088jcy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from scipy.stats import skew\n",
        "from scipy.stats.stats import pearsonr\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('ggplot')\n",
        "plt.rcParams['figure.figsize'] = (12, 8)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VVvdG_yl6PKe",
        "colab_type": "code",
        "outputId": "052bdace-44ee-4fca-fc70-59535b266216",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "facebook = pd.read_csv('./facebook_train.csv')\n",
        "facebook.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>total_followers</th>\n",
              "      <th>type</th>\n",
              "      <th>category</th>\n",
              "      <th>month</th>\n",
              "      <th>weekday</th>\n",
              "      <th>hour</th>\n",
              "      <th>paid</th>\n",
              "      <th>comment</th>\n",
              "      <th>like</th>\n",
              "      <th>share</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>124050</td>\n",
              "      <td>Photo</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>77</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>132556</td>\n",
              "      <td>Photo</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>24</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>313</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>92786</td>\n",
              "      <td>Photo</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>145</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>135632</td>\n",
              "      <td>Photo</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>299</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>139540</td>\n",
              "      <td>Link</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>229</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   total_followers   type  category  month  ...  paid  comment  like  share\n",
              "0           124050  Photo         3      6  ...     1        3    77     16\n",
              "1           132556  Photo         2      8  ...     0       11   313     50\n",
              "2            92786  Photo         2      2  ...     1        2   145     39\n",
              "3           135632  Photo         1      9  ...     0        2   299     49\n",
              "4           139540   Link         1     12  ...     1        7   229     21\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UditYvP19sM2",
        "colab_type": "code",
        "outputId": "e1daa292-bdee-4eb7-aae8-1146e27fea7e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "facebook.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 397 entries, 0 to 396\n",
            "Data columns (total 10 columns):\n",
            "total_followers    397 non-null int64\n",
            "type               397 non-null object\n",
            "category           397 non-null int64\n",
            "month              397 non-null int64\n",
            "weekday            397 non-null int64\n",
            "hour               397 non-null int64\n",
            "paid               397 non-null int64\n",
            "comment            397 non-null int64\n",
            "like               397 non-null int64\n",
            "share              397 non-null int64\n",
            "dtypes: int64(9), object(1)\n",
            "memory usage: 31.1+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cw80_jGI9TQR",
        "colab_type": "text"
      },
      "source": [
        "## Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uvcI_X5_6PQT",
        "colab_type": "code",
        "outputId": "728a24c8-0b5d-45f8-bc57-447e87f0e763",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        }
      },
      "source": [
        "#np.log1p() returns the natural logarithm of one plus the input array, element-wise\n",
        "like = pd.DataFrame({\"like\":facebook[\"like\"], \"log(like + 1)\":np.log1p(facebook[\"like\"])})\n",
        "like.hist();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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QkgEAAAADJRkAAAAwUJIBAAAAAyUZAAAAMFCSAQAAAAMlGQAAADBQkgEAAAADJRkAAAAw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\nm+iZAAAAmg8zyQAAAIDBYzP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            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TyUuAySI6PS9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#log transform the target\n",
        "facebook[\"like\"] = np.log1p(facebook[\"like\"])\n",
        "\n",
        "#find numeric features\n",
        "numeric_features = facebook.dtypes[facebook.dtypes != \"object\"].index\n",
        "\n",
        "#compute skew\n",
        "skewed_features = facebook[numeric_features].apply(lambda x: skew(x.dropna()))\n",
        "skewed_features = skewed_features[skewed_features > 0.75]\n",
        "skewed_features = skewed_features.index\n",
        "\n",
        "#log transform skewed numeric features\n",
        "facebook[skewed_features] = np.log1p(facebook[skewed_features])\n",
        "\n",
        "#create dummies for categorical features\n",
        "facebook = pd.get_dummies(facebook)\n",
        "facebook = pd.get_dummies(facebook, columns=[\"category\", \"month\", \"weekday\", \"hour\"])\n",
        "\n",
        "#replace NANs with the mean of the column\n",
        "facebook = facebook.fillna(facebook.mean())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CTB029Tv6Pj9",
        "colab_type": "code",
        "outputId": "273fc27e-5a42-4f5c-e5fe-c7c33884c828",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "#standardize total_followers by removing the mean and scaling to unit variance\n",
        "scaler = sklearn.preprocessing.StandardScaler()\n",
        "trans_total_followers = scaler.fit_transform(np.array(facebook.total_followers).reshape(-1,1))\n",
        "facebook['trans_total_followers'] = trans_total_followers\n",
        "facebook.head() #debug check"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>total_followers</th>\n",
              "      <th>paid</th>\n",
              "      <th>comment</th>\n",
              "      <th>like</th>\n",
              "      <th>share</th>\n",
              "      <th>type_Link</th>\n",
              "      <th>type_Photo</th>\n",
              "      <th>type_Status</th>\n",
              "      <th>type_Video</th>\n",
              "      <th>category_1</th>\n",
              "      <th>category_2</th>\n",
              "      <th>category_3</th>\n",
              "      <th>month_1</th>\n",
              "      <th>month_2</th>\n",
              "      <th>month_3</th>\n",
              "      <th>month_4</th>\n",
              "      <th>month_5</th>\n",
              "      <th>month_6</th>\n",
              "      <th>month_7</th>\n",
              "      <th>month_8</th>\n",
              "      <th>month_9</th>\n",
              "      <th>month_10</th>\n",
              "      <th>month_11</th>\n",
              "      <th>month_12</th>\n",
              "      <th>weekday_1</th>\n",
              "      <th>weekday_2</th>\n",
              "      <th>weekday_3</th>\n",
              "      <th>weekday_4</th>\n",
              "      <th>weekday_5</th>\n",
              "      <th>weekday_6</th>\n",
              "      <th>weekday_7</th>\n",
              "      <th>hour_1</th>\n",
              "      <th>hour_2</th>\n",
              "      <th>hour_3</th>\n",
              "      <th>hour_4</th>\n",
              "      <th>hour_5</th>\n",
              "      <th>hour_6</th>\n",
              "      <th>hour_7</th>\n",
              "      <th>hour_8</th>\n",
              "      <th>hour_9</th>\n",
              "      <th>hour_12</th>\n",
              "      <th>hour_15</th>\n",
              "      <th>hour_16</th>\n",
              "      <th>hour_17</th>\n",
              "      <th>hour_18</th>\n",
              "      <th>hour_19</th>\n",
              "      <th>hour_20</th>\n",
              "      <th>hour_21</th>\n",
              "      <th>hour_22</th>\n",
              "      <th>hour_23</th>\n",
              "      <th>hour_24</th>\n",
              "      <th>trans_total_followers</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>124050</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>1.386294</td>\n",
              "      <td>4.356709</td>\n",
              "      <td>2.833213</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.036279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>132556</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.484907</td>\n",
              "      <td>5.749393</td>\n",
              "      <td>3.931826</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.555526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>92786</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>1.098612</td>\n",
              "      <td>4.983607</td>\n",
              "      <td>3.688879</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1.872224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>135632</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.098612</td>\n",
              "      <td>5.703782</td>\n",
              "      <td>3.912023</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.743300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>139540</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>2.079442</td>\n",
              "      <td>5.438079</td>\n",
              "      <td>3.091042</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.981863</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   total_followers      paid   comment  ...  hour_23  hour_24  trans_total_followers\n",
              "0           124050  0.693147  1.386294  ...        0        0               0.036279\n",
              "1           132556  0.000000  2.484907  ...        0        1               0.555526\n",
              "2            92786  0.693147  1.098612  ...        0        0              -1.872224\n",
              "3           135632  0.000000  1.098612  ...        0        0               0.743300\n",
              "4           139540  0.693147  2.079442  ...        0        0               0.981863\n",
              "\n",
              "[5 rows x 52 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zHbOtZw3I-9u",
        "colab_type": "code",
        "outputId": "164a00b3-21a4-448b-de7d-706e573c11e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "facebook.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['total_followers', 'paid', 'comment', 'like', 'share', 'type_Link',\n",
              "       'type_Photo', 'type_Status', 'type_Video', 'category_1', 'category_2',\n",
              "       'category_3', 'month_1', 'month_2', 'month_3', 'month_4', 'month_5',\n",
              "       'month_6', 'month_7', 'month_8', 'month_9', 'month_10', 'month_11',\n",
              "       'month_12', 'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4',\n",
              "       'weekday_5', 'weekday_6', 'weekday_7', 'hour_1', 'hour_2', 'hour_3',\n",
              "       'hour_4', 'hour_5', 'hour_6', 'hour_7', 'hour_8', 'hour_9', 'hour_12',\n",
              "       'hour_15', 'hour_16', 'hour_17', 'hour_18', 'hour_19', 'hour_20',\n",
              "       'hour_21', 'hour_22', 'hour_23', 'hour_24', 'trans_total_followers'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OW1bfV75Zg7c",
        "colab_type": "text"
      },
      "source": [
        "## Create Train/Validation Splits"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xdG8HxLA6PmY",
        "colab_type": "code",
        "outputId": "44481d88-e4ec-4cee-87f9-1e35ec63e3c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X = facebook[['type_Link', 'type_Photo', 'type_Status',\n",
        "             'type_Video', 'category_1', 'category_2',\n",
        "             'category_3', 'month_1', 'month_2', 'month_3',\n",
        "             'month_4', 'month_5', 'month_6', 'month_7',\n",
        "             'month_8', 'month_9', 'month_10', 'month_11',\n",
        "             'month_12', 'weekday_1', 'weekday_2', 'weekday_3',\n",
        "             'weekday_4', 'weekday_5', 'weekday_6', 'weekday_7',\n",
        "             'trans_total_followers', 'hour_1', 'hour_2', 'hour_3',\n",
        "       'hour_4', 'hour_5', 'hour_6', 'hour_7', 'hour_8', 'hour_9', 'hour_12',\n",
        "       'hour_15', 'hour_16', 'hour_17', 'hour_18', 'hour_19', 'hour_20',\n",
        "       'hour_21', 'hour_22', 'hour_23', 'hour_24', 'paid']]\n",
        "y = facebook.like\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, test_size=0.2)\n",
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((317, 48), (80, 48), (317,), (80,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GlIPc48aaDkx",
        "colab_type": "text"
      },
      "source": [
        "## Ridge Regression Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kDjSHewD6PrE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import Ridge, RidgeCV\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "def mse_cv(model):\n",
        "    mse = -cross_val_score(model, X_train, y_train, scoring=\"neg_mean_squared_error\", cv = 5)\n",
        "    return mse"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t5WpLPCT6Pxk",
        "colab_type": "code",
        "outputId": "98d79cf7-7c45-4995-a89f-67470ace7268",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        }
      },
      "source": [
        "#model 1: Ridge Regression Estimator\n",
        "model_ridge = Ridge()\n",
        "alphas = [0.05, 0.1, 0.3, 1, 3, 5, 10, 15, 20, 25, 30, 40, 50, 60, 75]\n",
        "\n",
        "#perform 5-fold CV to find the alpha value that minimizes MSE\n",
        "cv_ridge = [mse_cv(Ridge(alpha = alpha)).mean() for alpha in alphas]\n",
        "cv_ridge = pd.Series(cv_ridge, index = alphas)\n",
        "cv_ridge.plot(title = \"Validation\")\n",
        "plt.xlabel(\"alpha\")\n",
        "plt.ylabel(\"MSE\");\n",
        "print(\"Lowest MSE = \", cv_ridge.min())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Lowest MSE =  1.358878883047091\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vvvji8OuCggKVlJRo/PjxmjBhgiSpf//+Sk9PV0VFhbKysjq9VllZmezhBklS9Z4y1ZSV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\ngg==\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "065cF9YtNzuT",
        "colab_type": "code",
        "outputId": "2c04b133-0ec4-4294-a74a-d97a7d0cb81a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "warnings.simplefilter(\"ignore\")\n",
        "model_ridge = Ridge(alpha=30).fit(X_train, y_train) #use the alpha from the above cell\n",
        "mse_cv(model_ridge).mean()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.358878883047091"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TlwNOLgeOsU6",
        "colab_type": "code",
        "outputId": "355e5da9-0ea0-4d5a-93ff-bae5ba76e9e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        }
      },
      "source": [
        "coef = pd.Series(model_ridge.coef_, index = X_train.columns)\n",
        "imp_coef = pd.concat([coef.sort_values().head(10), coef.sort_values().tail(10)])\n",
        "imp_coef.plot(kind = \"barh\")\n",
        "plt.title(\"Coefficients of the Ridge Model\");"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zc/XOO+9o6tSp1dp/xowZOnHihCTp6NGj6tChgyZPnlxv8QIAAAB1zTKF+6pV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\nPQMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NrQToZTqncD4",
        "colab_type": "text"
      },
      "source": [
        "## Model Evaluation Metrics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VxXYLycYnbKq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = model_ridge.predict(X_test) #predict on the validation set"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "17gWkcOUnuDT",
        "colab_type": "code",
        "outputId": "3228d8a9-330d-4181-b076-566881b384c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "from sklearn.metrics import mean_absolute_error, median_absolute_error, mean_squared_error, r2_score\n",
        "\n",
        "print(\"Mean Absolute Error: \", mean_absolute_error(y_test, y_pred))\n",
        "print(\"Median Absolute Error: \", median_absolute_error(y_test, y_pred))\n",
        "print(\"Mean Squared Error: \", mean_squared_error(y_test, y_pred))\n",
        "print(\"R^2: \", r2_score(y_test, y_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error:  0.8207093361858682\n",
            "Median Absolute Error:  0.6366326990548452\n",
            "Mean Squared Error:  1.1238466770466922\n",
            "R^2:  0.23003813413546192\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PWM7FP2inuGP",
        "colab_type": "code",
        "outputId": "6d91b99f-7b79-4380-e47e-cea5081ca515",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        }
      },
      "source": [
        "from yellowbrick.regressor import ResidualsPlot\n",
        "\n",
        "visualizer = ResidualsPlot(model_ridge, hist=False)\n",
        "visualizer.fit(X_train, y_train)\n",
        "visualizer.score(X_test, y_test)\n",
        "visualizer.poof()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iIg0YOBMRERERacDAmYiIiIhIAwbOREREREQaMHAmIiIiItKAgTMRERERkQYM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\nDPgv8k2IpMY/CBIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FG9gFGnanuJP",
        "colab_type": "code",
        "outputId": "d7b24544-cf78-4673-9a33-16475c3699df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        }
      },
      "source": [
        "from yellowbrick.regressor import PredictionError\n",
        "\n",
        "visualizer = PredictionError(model_ridge)\n",
        "\n",
        "visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer\n",
        "visualizer.score(X_test, y_test)  # Evaluate the model on the test data\n",
        "visualizer.poof() "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ta2IJRf/GHjIRUT9km8CVmpqKIUOGyN0k6gZ7yERE/YxjNnVWVpbcTSI3MCAT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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KXPxI9awnuMD",
        "colab_type": "code",
        "outputId": "ca403a8a-b89c-4384-a900-9b345bf78062",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        }
      },
      "source": [
        "from yellowbrick.model_selection import LearningCurve\n",
        "\n",
        "visualizer = LearningCurve(RidgeCV(alphas=alphas), scoring='r2')\n",
        "\n",
        "visualizer.fit(X, y)\n",
        "visualizer.poof()  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Xryc+Pr70AtVFxufD5uViszZiWrWt6eGIiIhIBR26MXL2YauUDK/iKiWO4/B/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\nHClwi4iIiIjEkQK3iIiIiEgc/QKVyhRn+a4OXQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7l5LrXpGZ731",
        "colab_type": "text"
      },
      "source": [
        "## XGBoostRegressor Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y5_ERhzL6P9E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import xgboost as xgb\n",
        "\n",
        "dtrain = xgb.DMatrix(X_train, label = y_train)\n",
        "dtest = xgb.DMatrix(X_test, label = y_test)\n",
        "\n",
        "params = {\"max_depth\":1, \"eta\":0.1}\n",
        "model = xgb.cv(params, dtrain,  num_boost_round=500, early_stopping_rounds=100)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5SUTdfxg6QAM",
        "colab_type": "code",
        "outputId": "71f079e6-6a5e-4ce4-c920-ba504545ecbd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        }
      },
      "source": [
        "model.loc[:,[\"test-rmse-mean\", \"train-rmse-mean\"]].plot();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AABAm7C/BsQ5JUlnZAZuTAAAAIFzYX4JT4iRJZVV1NicBAABAuLC/BGelSZLK6tttTgIA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\nBBMRERFRw2EIJiIiIqKG8//vYDPJzIsexgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2DO0N1tZ6QDE",
        "colab_type": "code",
        "outputId": "897f0da4-1939-4d29-d961-3733b027ac39",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "model_xgb = xgb.XGBRegressor(max_depth=1, learning_rate=0.1, objective='reg:squarederror')\n",
        "model_xgb.fit(X_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "             colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
              "             importance_type='gain', learning_rate=0.1, max_delta_step=0,\n",
              "             max_depth=1, min_child_weight=1, missing=None, n_estimators=100,\n",
              "             n_jobs=1, nthread=None, objective='reg:squarederror',\n",
              "             random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
              "             seed=None, silent=None, subsample=1, verbosity=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XEIQ-iO36QE1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "xgb_preds = model_xgb.predict(X_test)\n",
        "ridge_preds = model_ridge.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UQp8eLrx6QQ7",
        "colab_type": "code",
        "outputId": "f1706793-3aaa-4b44-8387-be75f840a553",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(\"XGB MSE on validation set: \", sklearn.metrics.mean_squared_error(y_test, xgb_preds))\n",
        "print(\"Ridge MSE on validation set: \", sklearn.metrics.mean_squared_error(y_test, ridge_preds))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "XGB MSE on validation set:  1.2612510276796738\n",
            "Ridge MSE on validation set:  1.1238466770466922\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DiulSe4Ltugk",
        "colab_type": "code",
        "outputId": "d04af135-7435-4c7b-ceb5-6f8d90f7741f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(\"XGB R^2: \", r2_score(y_test, xgb_preds))\n",
        "print(\"Ridge R^2: \", r2_score(y_test, ridge_preds))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "XGB R^2:  0.13590063980278944\n",
            "Ridge R^2:  0.23003813413546192\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XptnIFFv6QSq",
        "colab_type": "code",
        "outputId": "963b4755-2056-4e7c-eea1-e80c036a74ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        }
      },
      "source": [
        "predictions = pd.DataFrame({\"xgb\":xgb_preds, \"ridge\":ridge_preds})\n",
        "predictions.plot(x = \"xgb\", y = \"ridge\", kind = \"scatter\");"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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oAzg8FQvWl1xyyTuunX/++Tn//PPfcf2yyy6rREkAvAtH9AEcnoqvWAMwujmi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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uWSXgFwG6QWI",
        "colab_type": "code",
        "outputId": "cdcb348a-34f8-44c7-c753-a5ca0d9b2951",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "blend_preds = 0.8*ridge_preds + 0.2*xgb_preds\n",
        "print(\"Blended MSE on validation set: \", sklearn.metrics.mean_squared_error(y_test, blend_preds))\n",
        "print(\"MAE: \", mean_absolute_error(y_test, blend_preds))\n",
        "print(\"Median AE: \", median_absolute_error(y_test, blend_preds))\n",
        "print(\"Blended R^2 on validation set: \", r2_score(y_test, blend_preds))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Blended MSE on validation set:  1.144780021701526\n",
            "MAE:  0.8252929231758218\n",
            "Median AE:  0.6149217537958473\n",
            "Blended R^2 on validation set:  0.21569642948979184\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pi222f3wbDT7",
        "colab_type": "text"
      },
      "source": [
        "Note: Score has improved by taking the weighted average of uncorrelated results!"
      ]
    }
  ]
}