insurance_model.ipynb

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  "metadata": {
    "colab": {
      "name": "insurance-model",
      "provenance": [],
      "collapsed_sections": [
        "uAqK-kIKnbj-",
        "Rww8LiXBtOrk",
        "wsjAX9LB8nks"
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      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/GarlicDebug/insurance-machine-learning-model/blob/master/insurance_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VyP6N_jOv2xs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# To support both python 2 and python 3\n",
        "from __future__ import division, print_function, unicode_literals\n",
        "\n",
        "# Common imports\n",
        "import numpy as np\n",
        "import os\n",
        "\n",
        "# to make this notebook's output stable across runs\n",
        "np.random.seed(42)\n",
        "\n",
        "# To plot pretty figures\n",
        "%matplotlib inline\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "plt.rcParams['axes.labelsize'] = 14\n",
        "plt.rcParams['xtick.labelsize'] = 12\n",
        "plt.rcParams['ytick.labelsize'] = 12\n",
        "\n",
        "# Where to save the figures\n",
        "PROJECT_ROOT_DIR = \".\"\n",
        "\n",
        "# Ignore useless warnings (see SciPy issue #5998)\n",
        "import warnings\n",
        "warnings.filterwarnings(action=\"ignore\", module=\"scipy\", message=\"^internal gelsd\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GF_3B4n-w3mt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "from six.moves import urllib\n",
        "\n",
        "# URL for data file\n",
        "DOWNLOAD_URL = \"https://raw.githubusercontent.com/rlsummerscales/CPTR435/master/data/insurance.csv\"\n",
        "# local path where data file will be stored on computer (or in virtual environment)\n",
        "INSURANCE_PATH = os.path.join(\"datasets\", \"insurance\")\n",
        "\n",
        "def fetch_insurance_data(insurance_url=DOWNLOAD_URL, insurance_path=INSURANCE_PATH):\n",
        "    # create local directories for storing data files (if necessary)\n",
        "    # NOTE: if running this in Colaboratory, these directories will not be\n",
        "    # created on your computer, but in the virtual environment for the notebook\n",
        "    # in colaboratory. It will only be available to this notebook, not others.\n",
        "    if not os.path.isdir(insurance_path):\n",
        "        os.makedirs(insurance_path)\n",
        "\n",
        "    # build local path for data file\n",
        "    csv_path = os.path.join(insurance_path, \"insurance.csv\")\n",
        "    # download datafile if not already downloaded\n",
        "    urllib.request.urlretrieve(insurance_url, csv_path)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dq8dK-oFt2kr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fetch_insurance_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OZRHNm2Mx20t",
        "colab_type": "code",
        "outputId": "c2e4352d-b71f-4e19-9559-4c2b2fcd9c23",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "! ls datasets/insurance"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "insurance.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bZLYIiVEyAjc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "def load_insurance_data(insurance_path=INSURANCE_PATH):\n",
        "    csv_path = os.path.join(insurance_path, \"insurance.csv\")\n",
        "    return pd.read_csv(csv_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rafn058yyAuK",
        "colab_type": "code",
        "outputId": "c56db938-6392-4658-92ab-132c32727a5f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "insurance = load_insurance_data()\n",
        "\n",
        "insurance.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>smoker</th>\n",
              "      <th>region</th>\n",
              "      <th>charges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>female</td>\n",
              "      <td>27.900</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>southwest</td>\n",
              "      <td>16884.92400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>male</td>\n",
              "      <td>33.770</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>1725.55230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>male</td>\n",
              "      <td>33.000</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>4449.46200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>male</td>\n",
              "      <td>22.705</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>21984.47061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>male</td>\n",
              "      <td>28.880</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>3866.85520</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age     sex     bmi  children smoker     region      charges\n",
              "0   19  female  27.900         0    yes  southwest  16884.92400\n",
              "1   18    male  33.770         1     no  southeast   1725.55230\n",
              "2   28    male  33.000         3     no  southeast   4449.46200\n",
              "3   33    male  22.705         0     no  northwest  21984.47061\n",
              "4   32    male  28.880         0     no  northwest   3866.85520"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FCEK65AHyWpn",
        "colab_type": "code",
        "outputId": "706d8bdb-fed6-4864-cbe5-c620e35f9fac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "insurance.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1338 entries, 0 to 1337\n",
            "Data columns (total 7 columns):\n",
            "age         1338 non-null int64\n",
            "sex         1338 non-null object\n",
            "bmi         1338 non-null float64\n",
            "children    1338 non-null int64\n",
            "smoker      1338 non-null object\n",
            "region      1338 non-null object\n",
            "charges     1338 non-null float64\n",
            "dtypes: float64(2), int64(2), object(3)\n",
            "memory usage: 73.2+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k3yooKs5QK1p",
        "colab_type": "text"
      },
      "source": [
        "**Categorical Data Value Counts:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z85nmXC_ypvd",
        "colab_type": "code",
        "outputId": "613c772c-f7ea-472a-806b-abc04d7fc7a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "insurance['sex'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "male      676\n",
              "female    662\n",
              "Name: sex, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ud91dutzyu_1",
        "colab_type": "code",
        "outputId": "09b76b5b-3301-4845-b78c-01a027af86c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "insurance['smoker'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "no     1064\n",
              "yes     274\n",
              "Name: smoker, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c7uwOw6TyvGy",
        "colab_type": "code",
        "outputId": "3ef8b12a-d415-44ec-ea44-7b046f04b204",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "insurance['region'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "southeast    364\n",
              "northwest    325\n",
              "southwest    325\n",
              "northeast    324\n",
              "Name: region, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MoOjzmalQZU3",
        "colab_type": "text"
      },
      "source": [
        "There seems to be a pretty balanced distribution of samples based on region and sex, but not between smokers and non-smokers. Stratified splitting will be needed."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aPhkvmVUzCqF",
        "colab_type": "code",
        "outputId": "666c6f5b-9cf6-44f4-d15e-31f86d6117a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "insurance.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>charges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1338.000000</td>\n",
              "      <td>1338.000000</td>\n",
              "      <td>1338.000000</td>\n",
              "      <td>1338.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>39.207025</td>\n",
              "      <td>30.663397</td>\n",
              "      <td>1.094918</td>\n",
              "      <td>13270.422265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>14.049960</td>\n",
              "      <td>6.098187</td>\n",
              "      <td>1.205493</td>\n",
              "      <td>12110.011237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>15.960000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1121.873900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>27.000000</td>\n",
              "      <td>26.296250</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4740.287150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>39.000000</td>\n",
              "      <td>30.400000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9382.033000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>51.000000</td>\n",
              "      <td>34.693750</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>16639.912515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>64.000000</td>\n",
              "      <td>53.130000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>63770.428010</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               age          bmi     children       charges\n",
              "count  1338.000000  1338.000000  1338.000000   1338.000000\n",
              "mean     39.207025    30.663397     1.094918  13270.422265\n",
              "std      14.049960     6.098187     1.205493  12110.011237\n",
              "min      18.000000    15.960000     0.000000   1121.873900\n",
              "25%      27.000000    26.296250     0.000000   4740.287150\n",
              "50%      39.000000    30.400000     1.000000   9382.033000\n",
              "75%      51.000000    34.693750     2.000000  16639.912515\n",
              "max      64.000000    53.130000     5.000000  63770.428010"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ksuBUbywzIwv",
        "colab_type": "code",
        "outputId": "6bbbaa20-913f-4e51-9003-f2b32d952376",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 902
        }
      },
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "insurance.hist(bins=50, figsize=(20,15))\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lzX0+y7HNKTam+h6vrkPNS7pV7D59vnV66aveeMJt9u/d/HF658/v8fY0prom46vvRgwx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\nAKBLeAQAAABAl/AIAAAAgC7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V1cYkNya5u7V2vLV2IsndSW6sqo1V9RPpfcvu7DkvAYB1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\nCAAAAIBO/z/1W3MWQPvduAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x1080 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8r9P442eTnm-",
        "colab_type": "text"
      },
      "source": [
        "There seems to be a large cluster of people whose age is less than 20. This may be unnatural, and all ages under 18 may be listed as 18. There seem to be some strange gaps in the samples around multiples of 10\n",
        "\n",
        "The BMI mean is around 30. A normal BMI is between 18.5 and 24.9, so the majority of the people in our sample are overweight or obese.\n",
        "\n",
        "Most charges are under 15,000$, with min and max being 1121$ and 63770$.\n",
        "\n",
        "Most samples have no children, and no sample has more than 5 children.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KF9kPEFZXVRI",
        "colab_type": "text"
      },
      "source": [
        "# Creating a Test Set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yBiDIbMAt2lJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q091EKfqt2lL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eFMmfaUBfH3A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "smoker_cat = insurance[['smoker']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vE_Cd5kDXU6j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import StratifiedShuffleSplit\n",
        "split = StratifiedShuffleSplit(n_splits=10, test_size=0.2, random_state=42)\n",
        "\n",
        "for train_index, test_index in split.split(insurance, smoker_cat):\n",
        "    strat_train_set = insurance.loc[train_index]\n",
        "    strat_test_set = insurance.loc[test_index]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "67vMDSlAW59B",
        "colab_type": "code",
        "outputId": "6715d289-fa00-4867-e17a-c6fce7913bc4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "strat_train_set['smoker'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "no     851\n",
              "yes    219\n",
              "Name: smoker, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WVqvyiGmfyEO",
        "colab_type": "code",
        "outputId": "6ab4a34c-9745-4db9-b98f-b2f02111972c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "strat_test_set['smoker'].value_counts() "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "no     213\n",
              "yes     55\n",
              "Name: smoker, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e3Q2JjzBiaAo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "def income_cat_proportions(data):\n",
        "    return data[\"smoker\"].value_counts() / len(data)\n",
        "\n",
        "# regular, non-stratified sampling split\n",
        "train_set, test_set = train_test_split(insurance, test_size=0.2, random_state=42)\n",
        "\n",
        "# create a data frame to show the probability of each strata \n",
        "compare_props = pd.DataFrame({\n",
        "    \"Overall\": income_cat_proportions(insurance),\n",
        "    \"Stratified\": income_cat_proportions(strat_test_set),\n",
        "    \"Random\": income_cat_proportions(test_set),\n",
        "}).sort_index()\n",
        "\n",
        "# compute percentage each strata is over/under represented in the test sets\n",
        "compare_props[\"Rand. %error\"] = 100 * compare_props[\"Random\"] / compare_props[\"Overall\"] - 100\n",
        "compare_props[\"Strat. %error\"] = 100 * compare_props[\"Stratified\"] / compare_props[\"Overall\"] - 100"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ftvVn9KbjLq-",
        "colab_type": "code",
        "outputId": "7580d20e-ebb9-49df-c7d5-f51c62590661",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        }
      },
      "source": [
        "compare_props"
      ],
      "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>Overall</th>\n",
              "      <th>Stratified</th>\n",
              "      <th>Random</th>\n",
              "      <th>Rand. %error</th>\n",
              "      <th>Strat. %error</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>no</th>\n",
              "      <td>0.795217</td>\n",
              "      <td>0.794776</td>\n",
              "      <td>0.798507</td>\n",
              "      <td>0.413814</td>\n",
              "      <td>-0.055409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yes</th>\n",
              "      <td>0.204783</td>\n",
              "      <td>0.205224</td>\n",
              "      <td>0.201493</td>\n",
              "      <td>-1.606929</td>\n",
              "      <td>0.215165</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Overall  Stratified    Random  Rand. %error  Strat. %error\n",
              "no   0.795217    0.794776  0.798507      0.413814      -0.055409\n",
              "yes  0.204783    0.205224  0.201493     -1.606929       0.215165"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vaWWxXjBjoup",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "insurance = strat_train_set.copy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jC8Nq8iIjy5g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corr_matrix = insurance.corr()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a4KhMPFHjy2T",
        "colab_type": "code",
        "outputId": "8c040dcf-545a-435a-cf93-522133d88b45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "corr_matrix[\"charges\"].sort_values(ascending = False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "charges     1.000000\n",
              "age         0.312333\n",
              "bmi         0.230924\n",
              "children    0.092615\n",
              "Name: charges, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jjtQhdaGmNdI",
        "colab_type": "text"
      },
      "source": [
        "There doesn't seem to be any strong correlations between the other quantitative attributes and charges, so perhaps these attributes aren't good predictors of charges. We should investigate other attributes to use."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w2aMc_pWnN9B",
        "colab_type": "text"
      },
      "source": [
        "#Preparing the data\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uAqK-kIKnbj-",
        "colab_type": "text"
      },
      "source": [
        "##Removing the Output Labels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YG-aGCJdqsBX",
        "colab_type": "code",
        "outputId": "645a96a6-0571-4a0f-9174-6f5681b2c9d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "strat_train_set.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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>smoker</th>\n",
              "      <th>region</th>\n",
              "      <th>charges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>37</td>\n",
              "      <td>male</td>\n",
              "      <td>29.83</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "      <td>northeast</td>\n",
              "      <td>6406.4107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>362</th>\n",
              "      <td>19</td>\n",
              "      <td>female</td>\n",
              "      <td>21.70</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>southwest</td>\n",
              "      <td>13844.5060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1069</th>\n",
              "      <td>54</td>\n",
              "      <td>female</td>\n",
              "      <td>31.90</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>10928.8490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>53</td>\n",
              "      <td>female</td>\n",
              "      <td>35.90</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "      <td>southwest</td>\n",
              "      <td>11163.5680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>213</th>\n",
              "      <td>34</td>\n",
              "      <td>female</td>\n",
              "      <td>26.73</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>5002.7827</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      age     sex    bmi  children smoker     region     charges\n",
              "8      37    male  29.83         2     no  northeast   6406.4107\n",
              "362    19  female  21.70         0    yes  southwest  13844.5060\n",
              "1069   54  female  31.90         1     no  southeast  10928.8490\n",
              "132    53  female  35.90         2     no  southwest  11163.5680\n",
              "213    34  female  26.73         1     no  southeast   5002.7827"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PMfdL1lUrH6Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#drops labels for training set\n",
        "insurance = strat_train_set.drop(\"charges\", axis=1)\n",
        "insurance_labels = strat_train_set[['charges']].copy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tju2tR2BrzVI",
        "colab_type": "code",
        "outputId": "ab4c3ae3-2f9c-4bbb-f982-a44320c6abda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "insurance_labels.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1070 entries, 8 to 264\n",
            "Data columns (total 1 columns):\n",
            "charges    1070 non-null float64\n",
            "dtypes: float64(1)\n",
            "memory usage: 16.7 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uzt7LScAsoF3",
        "colab_type": "code",
        "outputId": "3645530c-4f19-46da-daba-d602c0ba33b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "insurance.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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>smoker</th>\n",
              "      <th>region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>37</td>\n",
              "      <td>male</td>\n",
              "      <td>29.83</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "      <td>northeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>362</th>\n",
              "      <td>19</td>\n",
              "      <td>female</td>\n",
              "      <td>21.70</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>southwest</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1069</th>\n",
              "      <td>54</td>\n",
              "      <td>female</td>\n",
              "      <td>31.90</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>53</td>\n",
              "      <td>female</td>\n",
              "      <td>35.90</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "      <td>southwest</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>213</th>\n",
              "      <td>34</td>\n",
              "      <td>female</td>\n",
              "      <td>26.73</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      age     sex    bmi  children smoker     region\n",
              "8      37    male  29.83         2     no  northeast\n",
              "362    19  female  21.70         0    yes  southwest\n",
              "1069   54  female  31.90         1     no  southeast\n",
              "132    53  female  35.90         2     no  southwest\n",
              "213    34  female  26.73         1     no  southeast"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rww8LiXBtOrk",
        "colab_type": "text"
      },
      "source": [
        "##Cleaning missing attribute values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VB6UuhrOs1-y",
        "colab_type": "code",
        "outputId": "6a96a487-1ed7-481a-891d-cf9df4da1a25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        }
      },
      "source": [
        "sample_incomplete_rows = insurance[insurance.isnull().any(axis=1)].head()\n",
        "sample_incomplete_rows"
      ],
      "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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>smoker</th>\n",
              "      <th>region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [age, sex, bmi, children, smoker, region]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dhT5Uzzt8izV",
        "colab_type": "text"
      },
      "source": [
        "No attributes appear to be missing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wsjAX9LB8nks",
        "colab_type": "text"
      },
      "source": [
        "##One Hot Encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Imi7QQpD8u_M",
        "colab_type": "code",
        "outputId": "dd5488a8-51f3-4a6b-e2f2-aa271e652cf1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        }
      },
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "cat_encoder = OneHotEncoder()\n",
        "insurance_cat = insurance[['region', 'smoker', 'sex']]\n",
        "insurance_cat_1hot = cat_encoder.fit_transform(insurance_cat)\n",
        "\n",
        "print(insurance_cat_1hot)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2656\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2657\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2658\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: ('region', 'smoker', 'sex')",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-35-926a01c1bbfa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mcat_encoder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mOneHotEncoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0minsurance_cat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minsurance\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'region'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'smoker'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sex'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0minsurance_cat_1hot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcat_encoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minsurance_cat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2925\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2926\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2927\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2928\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2929\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2657\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2658\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2659\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2660\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2661\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: ('region', 'smoker', 'sex')"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E0c33tmGA7ec",
        "colab_type": "code",
        "outputId": "63c4da2a-f7fa-4987-e774-00000394fb89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "cat_encoder.categories_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array(['northeast', 'northwest', 'southeast', 'southwest'], dtype=object),\n",
              " array(['no', 'yes'], dtype=object),\n",
              " array(['female', 'male'], dtype=object)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4p_989fiBBWL",
        "colab_type": "text"
      },
      "source": [
        "##Creating a Pipeline"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "saoj4HG0BFFX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.base import BaseEstimator, TransformerMixin\n",
        "from sklearn.pipeline import FeatureUnion\n",
        "\n",
        "# Create a class to select numerical or categorical columns \n",
        "# since Scikit-Learn doesn't handle DataFrames yet\n",
        "class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, attribute_names):\n",
        "        self.attribute_names = attribute_names\n",
        "    def fit(self, X, y=None):\n",
        "        return self\n",
        "    def transform(self, X):\n",
        "        return X[self.attribute_names].values\n",
        "\n",
        "insurance_num = insurance.select_dtypes(include=[np.number])\n",
        "num_attribs = insurance_num\n",
        "cat_attribs = insurance_cat\n",
        "\n",
        "\n",
        "num_pipeline = Pipeline([\n",
        "                        ('selector', DataFrameSelector(num_attribs)),\n",
        "                        ('std_scaler', StandardScaler())\n",
        "])\n",
        "\n",
        "cat_pipeline = Pipeline([\n",
        "                         ('selector', DataFrameSelector(cat_attribs)),\n",
        "                         ('cat_encoder', OneHotEncoder(sparse=False))\n",
        "])\n",
        "\n",
        "\n",
        "full_pipeline = FeatureUnion(transformer_list=[\n",
        "                                                ('num_pipeline', num_pipeline),\n",
        "                                                ('cat_pipeline', cat_pipeline)\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6wF-FsW5kffQ",
        "colab_type": "code",
        "outputId": "55d20da4-d4ba-4fc1-efb4-32f8334b63cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "insurance_prepared = full_pipeline.fit_transform(insurance)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-39-355ca5159ce1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0minsurance_prepared\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfull_pipeline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minsurance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    910\u001b[0m             \u001b[0msum\u001b[0m \u001b[0mof\u001b[0m \u001b[0mn_components\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m \u001b[0mdimension\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0mover\u001b[0m \u001b[0mtransformers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    911\u001b[0m         \"\"\"\n\u001b[0;32m--> 912\u001b[0;31m         \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parallel_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_fit_transform_one\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    913\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m             \u001b[0;31m# All transformers are None\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36m_parallel_func\u001b[0;34m(self, X, y, fit_params, func)\u001b[0m\n\u001b[1;32m    940\u001b[0m             \u001b[0mmessage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_log_message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransformers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    941\u001b[0m             \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransformer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 942\u001b[0;31m                                     weight) in enumerate(transformers, 1))\n\u001b[0m\u001b[1;32m    943\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    944\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1001\u001b[0m             \u001b[0;31m# remaining jobs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1002\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1003\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1004\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1005\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    832\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    833\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 834\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    835\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    836\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    751\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    752\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 753\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    754\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    755\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    199\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 201\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    202\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    580\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    581\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 582\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    583\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    584\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 256\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    257\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    258\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 256\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    257\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    258\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36m_fit_transform_one\u001b[0;34m(transformer, X, y, weight, message_clsname, message, **fit_params)\u001b[0m\n\u001b[1;32m    714\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0m_print_elapsed_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_clsname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    715\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransformer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fit_transform'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 716\u001b[0;31m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransformer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    717\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    718\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransformer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    385\u001b[0m         \"\"\"\n\u001b[1;32m    386\u001b[0m         \u001b[0mlast_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_final_estimator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 387\u001b[0;31m         \u001b[0mXt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    388\u001b[0m         with _print_elapsed_time('Pipeline',\n\u001b[1;32m    389\u001b[0m                                  self._log_message(len(self.steps) - 1)):\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    315\u001b[0m                 \u001b[0mmessage_clsname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Pipeline'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    316\u001b[0m                 \u001b[0mmessage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_log_message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 317\u001b[0;31m                 **fit_params_steps[name])\n\u001b[0m\u001b[1;32m    318\u001b[0m             \u001b[0;31m# Replace the transformer of the step with the fitted\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    319\u001b[0m             \u001b[0;31m# transformer. This is necessary when loading the transformer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/joblib/memory.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    353\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    354\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 355\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    356\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    357\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcall_and_shelve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36m_fit_transform_one\u001b[0;34m(transformer, X, y, weight, message_clsname, message, **fit_params)\u001b[0m\n\u001b[1;32m    714\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0m_print_elapsed_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_clsname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    715\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransformer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fit_transform'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 716\u001b[0;31m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransformer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    717\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    718\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransformer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    551\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    552\u001b[0m             \u001b[0;31m# fit method of arity 1 (unsupervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 553\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    554\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    555\u001b[0m             \u001b[0;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-38-bf2383d0bfe6>\u001b[0m in \u001b[0;36mtransform\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattribute_names\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0minsurance_num\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minsurance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect_dtypes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2912\u001b[0m         \u001b[0;31m# Do we have a (boolean) DataFrame?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2913\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2914\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2915\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2916\u001b[0m         \u001b[0;31m# Do we have a (boolean) 1d indexer?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_frame\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3007\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3008\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_bool_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3009\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Must pass DataFrame with boolean values only'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3010\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3011\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Must pass DataFrame with boolean values only"
          ]
        }
      ]
    }
  ]
}