medical_cost_analysis_TeamExplorer.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/muhammedbalogun/Global_AI_TeamExplorer/blob/main/medical_cost_analysis_TeamExplorer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7VtAA4jTfYRQ"
      },
      "source": [
        "# Medical Cost Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CCH8o-NFk2Ls"
      },
      "source": [
        "## Introduction"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dataset used for this project was obtained on kaggle (https://www.kaggle.com/datasets/mirichoi0218/insurance). It contains 1338 observation and 7 columns."
      ],
      "metadata": {
        "id": "CJNbR2AIOWM9"
      }
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    {
      "cell_type": "markdown",
      "source": [
        "### Features\n",
        "\n",
        "- age: age of primary beneficiary\n",
        "\n",
        "- sex: insurance contractor gender, female, male\n",
        "\n",
        "- bmi: Body mass index, providing an understanding of body, weights that are relatively high or low relative to height,\n",
        "objective index of body weight (kg / m ^ 2) using the ratio of height to weight, ideally 18.5 to 24.9\n",
        "\n",
        "- children: Number of children covered by health insurance / Number of dependents\n",
        "\n",
        "- smoker: Smoking\n",
        "\n",
        "- region: the beneficiary's residential area in the US, northeast, southeast, southwest, northwest.\n",
        "\n",
        "- charges: Individual medical costs billed by health insurance"
      ],
      "metadata": {
        "id": "kYHuBi4IPZ-q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Research Question\n",
        " > The aim of the project is to estimate the approximate cost of a person's\n",
        "health insurance based on the given variables"
      ],
      "metadata": {
        "id": "aaL2jyUKQtzo"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KBIH3qNfk2Lt"
      },
      "source": [
        "## Data Wrangling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "metadata": {
        "id": "q-TGp5Nwk2Lu"
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      "source": [
        "# import required packages\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.express as px\n",
        "%matplotlib inline\n",
        "\n",
        "# import warning and ignore future warnings\n",
        "import warnings\n",
        "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n",
        "\n",
        "# machine learning packages\n",
        "# scaler, metrics and encoder\n",
        "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.metrics import mean_absolute_error, r2_score\n",
        "from sklearn import preprocessing\n",
        "\n",
        "# models slection\n",
        "from sklearn.model_selection import GridSearchCV, StratifiedKFold, learning_curve\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from xgboost.sklearn import XGBRegressor\n",
        "\n",
        "# other regression models\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.neural_network import MLPRegressor\n",
        "from sklearn.ensemble import ExtraTreesRegressor, VotingRegressor, AdaBoostRegressor\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "from sklearn.linear_model import ElasticNet\n",
        "from sklearn.linear_model import SGDRegressor\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.linear_model import BayesianRidge\n",
        "from sklearn.kernel_ridge import KernelRidge\n",
        "from lightgbm import LGBMRegressor"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PT7sEVR5k2Lu"
      },
      "source": [
        "### Gathering Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "KBbox_c6k2Lv"
      },
      "outputs": [],
      "source": [
        "# load dataset\n",
        "df = pd.read_csv(\"/content/insurance.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p-j6UMznk2Lv"
      },
      "source": [
        "### Assessing Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "hhCrLIUmk2Lv"
      },
      "outputs": [],
      "source": [
        "# preliminary assessment function\n",
        "\n",
        "def assess_data(df):\n",
        "    \n",
        "    # check header\n",
        "    print(\"The First Five Observations in the DataFrame \\n\")\n",
        "    display(df.head())\n",
        "    print('\\n')\n",
        "\n",
        "    # check tail\n",
        "    print(\"The Last Five Observations in the DataFrame \\n\")\n",
        "    display(df.tail())\n",
        "    print('\\n')\n",
        "    \n",
        "    # check shape of df\n",
        "    print(\"The Shape of the DataFrame\")\n",
        "    print(df.shape)\n",
        "    print('\\n')\n",
        "    \n",
        "    # check info of df\n",
        "    print(\"Basic Information of the DataFrame \\n\")\n",
        "    display(df.info())\n",
        "    print('\\n')\n",
        "    \n",
        "    # check number of unique values in df\n",
        "    print(\"Number of Unique Values in the DataFrame \\n\")\n",
        "    print(df.nunique())\n",
        "    print('\\n')\n",
        "    \n",
        "    # check number of missing values in df\n",
        "    print(\"Number of Missing Values in the DataFrame \\n\")\n",
        "    print(df.isnull().sum())\n",
        "    print('\\n')\n",
        "    \n",
        "    # check number duplicates in df\n",
        "    print(\"Number of Duplicates in DataFrame\")\n",
        "    print(\"Number of duplicates: \", df.duplicated().sum())\n",
        "    print('\\n')\n",
        "\n",
        "    # check summary statistics of numeric data in df\n",
        "    print(\"Summary Statistics\")\n",
        "    print(df.describe())"
      ]
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            "The First Five Observations in the DataFrame \n",
            "\n"
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              "   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"
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              "      age     sex    bmi  children smoker     region     charges\n",
              "1333   50    male  30.97         3     no  northwest  10600.5483\n",
              "1334   18  female  31.92         0     no  northeast   2205.9808\n",
              "1335   18  female  36.85         0     no  southeast   1629.8335\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1bb916eb-748c-4528-82ef-ea56490faf00');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "The Shape of the DataFrame\n",
            "(1338, 7)\n",
            "\n",
            "\n",
            "Basic Information of the DataFrame \n",
            "\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1338 entries, 0 to 1337\n",
            "Data columns (total 7 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       1338 non-null   int64  \n",
            " 1   sex       1338 non-null   object \n",
            " 2   bmi       1338 non-null   float64\n",
            " 3   children  1338 non-null   int64  \n",
            " 4   smoker    1338 non-null   object \n",
            " 5   region    1338 non-null   object \n",
            " 6   charges   1338 non-null   float64\n",
            "dtypes: float64(2), int64(2), object(3)\n",
            "memory usage: 73.3+ KB\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Number of Unique Values in the DataFrame \n",
            "\n",
            "age           47\n",
            "sex            2\n",
            "bmi          548\n",
            "children       6\n",
            "smoker         2\n",
            "region         4\n",
            "charges     1337\n",
            "dtype: int64\n",
            "\n",
            "\n",
            "Number of Missing Values in the DataFrame \n",
            "\n",
            "age         0\n",
            "sex         0\n",
            "bmi         0\n",
            "children    0\n",
            "smoker      0\n",
            "region      0\n",
            "charges     0\n",
            "dtype: int64\n",
            "\n",
            "\n",
            "Number of Duplicates in DataFrame\n",
            "Number of duplicates:  1\n",
            "\n",
            "\n",
            "Summary Statistics\n",
            "               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\n"
          ]
        }
      ],
      "source": [
        "# explore data set\n",
        "assess_data(df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I_WUfp2c3d1V"
      },
      "source": [
        "### Summary of Assessment\n",
        "\n",
        "- There are 1338 number of observations and 7 columns in the dataset\n",
        "- There are six features (age, sex, bmi, children, smoker and region) and one target (charges) variable in the dataset\n",
        "- No missing values in the dataset\n",
        "- There is one duplicate row in the dataset\n",
        "- sex, smoker and region contains categorical variables\n",
        "- The age range of correspondence in the dataset is from 18 - 64 years\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eOJRb6SK274K"
      },
      "source": [
        "### Cleaning Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "OiRYz8CM3D3Q"
      },
      "outputs": [],
      "source": [
        "# copy data for cleaning\n",
        "df_clean = df.copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "n42Q6bk7VgzU",
        "outputId": "793b238a-6693-4c2f-9385-e688aaf6f8b4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age   sex    bmi  children smoker     region    charges\n",
              "581   19  male  30.59         0     no  northwest  1639.5631"
            ],
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              "      <th>581</th>\n",
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              "      <td>30.59</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>1639.5631</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e3fcead1-e126-4f18-8d69-011069cfa085')\"\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e3fcead1-e126-4f18-8d69-011069cfa085');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# check duplicate row\n",
        "df_clean[df_clean.duplicated()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "LemwYsTaV-gn",
        "outputId": "dc2aae81-9717-4d07-b479-e9f95c48d275"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [age, sex, bmi, children, smoker, region, charges]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d5aa9e73-6878-4082-b5b8-581e04956add\">\n",
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              "      <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",
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              "  <tbody>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d5aa9e73-6878-4082-b5b8-581e04956add button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d5aa9e73-6878-4082-b5b8-581e04956add');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# drop duplicates\n",
        "df_clean.drop_duplicates(inplace=True)\n",
        "\n",
        "# confirm drop\n",
        "df_clean[df_clean.duplicated()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gc-nDk1NWZTG",
        "outputId": "e197b5db-6bf0-4da6-eb7e-8aa680f66c57"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age            int64\n",
              "sex         category\n",
              "bmi          float64\n",
              "children       int64\n",
              "smoker      category\n",
              "region      category\n",
              "charges      float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# change data type of categorical variables from object to category\n",
        "cat_columns = ['sex', 'smoker', 'region']\n",
        "\n",
        "for col in cat_columns:\n",
        "  df_clean[col] = df_clean[col].astype('category')\n",
        "\n",
        "# confirm changes\n",
        "df_clean.dtypes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dJb76mbIk2Lx"
      },
      "source": [
        "## Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qUqOmiY9cQcM"
      },
      "source": [
        "### What is the distribution of \"bmi\" (Body Mass Index)?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "id": "ZoYUNiP-k2Lx",
        "outputId": "71847b5f-181b-4a16-b4a7-829610870766"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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pxtVtDTyepuX+qcANSTahaeGcuH3IbQBV9akku9LcC/DtNFdPS/c10/cX8eVrtl/ce0+midedNONDzqUZcL0bsNYk655Oz73KgHVpbn75NZovsN/RXMxwFs3VXuv1rb8RzS0altG0GNznfmnt9Okrif1oJr+P29E0yeLJNF/kd9B8UT5/km1t0h7vDW0dXEhzteHC/rh61nk7ze0t7qTvnlf9ddNTvhbNTVCXtDHd0db1GwfV88rqYNDxT+Hn/RSa1sblNF/ov2x/Bg8ZsOwurN593Hpfd9EkUycDz51kvQ1ovuQvpBkvdhvNl/zekywfmsdnTfwMrqFprdqYVd8jcM82rnNW4/dm2RSXnew+bpvSXCyxtP1d+TlNq/aG/fG35+c/0iQzV7fHe217nu0NpGfZl9IkMpfRdAPf2tbpocCCKcY8cd4v7Stf6TkxWb3TJJdHtefdb2nuxfaalW2PppXuXe3vx+3ter+gubnw/sBG7XIT961724BtbExz8cfvgR2n87P2tWa/UjXdcdSSpLnStgYeAryuqmare1rSmDNxk6Qx1160chlNC/E2tZKb5EpasznGTZLGVJI9aG5B8lc048neadImzW8mbpI0vv4a2IfmPm7/THNvM0nzmF2lkiRJHeF93CRJkjpijeoq3WyzzWrhwoWjDkOSJGmVzj333F9V1YJh1lmjEreFCxeyZMmSUYchSZK0SkmuHHYdu0olSZI6wsRNkiSpI0zcJEmSOsLETZIkqSNM3CRJkjrCxE2SJKkjTNwkSZI6wsRNkiSpI0zcJEmSOsLETZIkqSNM3CRJkjrCxE2SJKkjTNwkSZI6Yp1RByBpfCw8aPGc7m/pYXvM6f4kqetscZMkSeoIW9ykMTfXrWCSpPFli5skSVJHmLhJkiR1hImbJElSR5i4SZIkdYSJmyRJUkfMWeKW5KgkNyS5sK/875NcmuSiJB/oKT84yeVJfpbk+XMVpyRJ0riay9uBHA18DPjsREGSZwF7Ak+oqjuTPLgt3wHYC/hT4CHAt5I8sqrunsN4JUmSxsqctbhV1ZnAjX3FbwAOq6o722VuaMv3BI6vqjur6hfA5cCOcxWrJEnSOBr1DXgfCTwzyaHA74B3VtU5wFbAWT3LLWvLVpBkf2B/gG233XZ2o5U0o3zEliQNZ9QXJ6wDbArsBPwDcEKSDLOBqjq8qhZV1aIFCxbMRoySJEljYdSJ2zLgpGr8CLgH2Ay4GtimZ7mt2zJJkqR5a9SJ25eBZwEkeSSwHvAr4BRgryTrJ9kO2B740ciilCRJGgNzNsYtyXHALsBmSZYBhwBHAUe1twj5PbBPVRVwUZITgIuBu4A3ekWpJEma7+YscauqvSeZ9cpJlj8UOHT2IpIkSeqWUXeVSpIkaYpM3CRJkjrCxE2SJKkjTNwkSZI6wsRNkiSpI0zcJEmSOsLETZIkqSNM3CRJkjrCxE2SJKkjTNwkSZI6wsRNkiSpI0zcJEmSOsLETZIkqSNM3CRJkjrCxE2SJKkj1hl1AFLXLDxo8ahDkCTNU7a4SZIkdYSJmyRJUkeYuEmSJHWEiZskSVJHmLhJkiR1hImbJElSR5i4SZIkdYSJmyRJUkeYuEmSJHWEiZskSVJHmLhJkiR1hImbJElSR8xZ4pbkqCQ3JLlwwLx3JKkkm7XTSfLRJJcn+WmSJ81VnJIkSeNqLlvcjgZ26y9Msg3wPOCXPcW7A9u3r/2BT85BfJIkSWNtzhK3qjoTuHHArA8BBwDVU7Yn8NlqnAVskmTLOQhTkiRpbI10jFuSPYGrq+onfbO2Aq7qmV7WlkmSJM1b64xqx0k2BN5F0026OtvZn6Y7lW233XYGIpMkSRpPo2xxeziwHfCTJEuBrYHzkmwBXA1s07Ps1m3ZCqrq8KpaVFWLFixYMMshS5Ikjc7IErequqCqHlxVC6tqIU136JOq6jrgFODV7dWlOwG3VNW1o4pVkiRpHMzl7UCOA34IPCrJsiT7rWTxrwJXAJcDRwB/NwchSpIkjbU5G+NWVXuvYv7Cns8FvHG2Y5IkSeoSn5wgSZLUESZukiRJHWHiJkmS1BEmbpIkSR1h4iZJktQRJm6SJEkdYeImSZLUESZukiRJHWHiJkmS1BEmbpIkSR1h4iZJktQRJm6SJEkdYeImSZLUESZukiRJHWHiJkmS1BHrjDoAaXUtPGjxqEOQJGlO2OImSZLUESZukiRJHWHiJkmS1BEmbpIkSR1h4iZJktQRJm6SJEkdYeImSZLUESZukiRJHWHiJkmS1BEmbpIkSR1h4iZJktQRJm6SJEkdYeImSZLUEXOWuCU5KskNSS7sKfuXJJcm+WmSLyXZpGfewUkuT/KzJM+fqzglSZLG1TpzuK+jgY8Bn+0pOxU4uKruSvJ+4GDgwCQ7AHsBfwo8BPhWkkdW1d1zGK+kNczCgxbP6f6WHrbHnO5P0ppvzlrcqupM4Ma+sm9W1V3t5FnA1u3nPYHjq+rOqvoFcDmw41zFKkmSNI7GaYzba4GvtZ+3Aq7qmbesLVtBkv2TLEmyZPny5bMcoiRJ0uiMReKW5N3AXcCxw65bVYdX1aKqWrRgwYKZD06SJGlMzOUYt4GSvAZ4AbBrVVVbfDWwTc9iW7dlkiRJ89ZIW9yS7AYcALywqu7omXUKsFeS9ZNsB2wP/GgUMUqSJI2LOWtxS3IcsAuwWZJlwCE0V5GuD5yaBOCsqvrbqrooyQnAxTRdqG/0ilJJkjTfzVniVlV7Dyj+zEqWPxQ4dPYikiRJ6paxuDhBkiRJq2biJkmS1BEmbpIkSR1h4iZJktQRJm6SJEkdYeImSZLUESZukiRJHWHiJkmS1BEmbpIkSR1h4iZJktQRJm6SJEkdYeImSZLUESZukiRJHWHiJkmS1BEmbpIkSR1h4iZJktQRJm6SJEkdYeImSZLUESZukiRJHWHiJkmS1BEmbpIkSR1h4iZJktQRJm6SJEkdMVTiluT8JG9K8sDZCkiSJEmDDdvithg4ALgmyXFJdp2FmCRJkjTAUIlbVb0beCjwYmBtYHGSXyT5xyTbzkaAkiRJagw9xq0aX6uqlwIPAQ4H3gVckeQbSXab6SAlSZK0GhcnJNkJOAw4CLgGeC/wc+DEJB+emfAkSZI0YdiLEx6c5J1JLgJOBzYBXlJVD6uqf6qqvwOeC7xuwLpHJbkhyYU9ZZsmOTXJZe37A9vyJPloksuT/DTJk1bnICVJktYEw7a4LQNeCxwFbF1VL62qU/uWuQg4Z8C6RwP93agHAadV1fbAae00wO7A9u1rf+CTQ8YpSZK0xllnyOV3rarvrmyBqroVeNaA8jOTLOwr3hPYpf18DE0r3oFt+WerqoCzkmySZMuqunbIeCVJktYYw7a43Zjk8f2FSR6fZIdp7H/znmTsOmDz9vNWwFU9yy1ryyRJkuatYRO3w4HHDijfoZ03bW3rWg27XpL9kyxJsmT58uWrE4IkSdJYGzZxezzwowHl5wCPm8b+r0+yJUD7fkNbfjWwTc9yW7dlK6iqw6tqUVUtWrBgwTRCkCRJ6oZhE7e7gY0HlD8QyDT2fwqwT/t5H+DknvJXt1eX7gTc4vg2SZI03w2buJ0BvDvJ2hMFSdYB3g2cubIVkxwH/BB4VJJlSfajuQ/cc5NcBjynnQb4KnAFcDlwBPB3Q8YpSZK0xhn2qtIDgO8Blyf5Xlv2DOD+wM4rW7Gq9p5k1grPO23Hu71xyNgkSZLWaEMlblX1s/aq0jcBT2yLjwU+UVXXzHRwktRlCw9aPKf7W3rYHnO6P0lzb9gWN9qxZu+ehVgkSZK0EkMnbkk2pGltezB9Y+Sq6qQZikuSJEl9hkrckjwHOA540IDZBaw9oFySJEkzYNirSj8CLKZ5TulafS+TNkmSpFk0bFfpQuCFXoggSZI094Ztcfs+8KjZCESSJEkrN2yL26eAf03yEOAC4A+9M6vqvJkKTJIkSfc1bOJ2Yvs+6IHyXpwgSZI0i4ZN3LablSgkSZK0SsM+OeHK2QpEa465vlu8JEnzxbAXJ5Bk9yRfSXJxkm3astclWeGZo5IkSZo5QyVuSV4BnABcRtNtum47a22aB9BLkiRplgzb4nYA8DdV9Tbgrp7ys7j3ofOSJEmaBcMmbtsDPxxQfjvwgNUPR5IkSZMZNnG7BnjkgPKdgZ+vfjiSJEmazLCJ2+HAR5M8vZ3eJsk+wAeAT85oZJIkSbqPYW8H8oEkGwOnAhsA3wHuBP61qj4+C/FJkiSpNewNeKmqdyc5FNiBpsXu4qq6fcYjkyRJ0n0MnbgBVNUdwJIZjkWSJEkrMVTiluSUlc2vqheuXjiSJEmazLAtbr/um14XeAKwDXDSjEQkSZKkgYa9OGHfQeVJPgjcOiMRSZIkaaChn1U6iU8Db5yhbUmSJGmAmUrcHjVD25EkSdIkhr044aP9RcCWwO7AUTMVlCRJklY07MUJj+ubvgdYDrwNEzdJkqRZNezFCc+arUAkSZK0cjM1xk2SJEmzbNgxbt8BairLVtWzh9ju24DXtdu+ANiXZuzc8cCDgHOBV1XV74eJV5IkaU0ybIvbJcCTga2AZe3rIcCTgEuBi3peU5JkK+DNwKKqeiywNrAX8H7gQ1X1COAmYL8hY5UkSVqjDHtxwp3AMcBbquqPLW9JPgykqt6yGnHcL8kfgA2Ba4FnAy9v5x8DvAf45DS3L0mS1HnDtri9GvhYb9LW+gTwqukEUFVXA/8K/JImYbuFpmv05qq6q11sGU0r3wqS7J9kSZIly5cvn04IkiRJnTBs4hZWvCUIk5RNbYPJA4E9ge1oul03Anab6vpVdXhVLaqqRQsWLJhuGJIkSWNv2K7So4Ajk2wPnNWW7QQcAPzHNGN4DvCLqloOkOQk4OnAJknWaVvdtgaunub2JUmS1gjDJm4HADcAbwH+X1t2LXAY8MFpxvBLYKckGwK/BXYFlgDfAV5Cc2XpPsDJ09y+JEnSGmHYG/DeA3wA+ECSB7Rlt65OAFV1dpITgfOAu4AfA4cDi4Hjk7yvLfvM6uxHkiSp64ZtcQMgySLg4cBX2umNgDt7LiYYSlUdAhzSV3wFsON0tidJkrQmGvYGvJvTdFnuSHOz3O1pEqx/A35H04UqSZKkWTDsVaUfAq6neZrBHT3lXwCeN1NBSZIkaUXDdpXuCuxaVTcl6S3/ObDtjEUlSZKkFQzb4nY/YNDzQhfQdJVKkiRplgybuJ0JvKZnupKsDRwInDZTQUmSJGlF07mP2xlJngKsT3Pvtj8FNqa5aa4kSZJmyVAtblV1Mc3jrX4AfBPYgObChD+rqp/PfHiSJEmaMOUWtyTrAt8DXt3ed02SJElzaMotblX1B5oHwdfshSNJkqTJDHtxwjHA38xGIJIkSVq5YS9O2Ah4RZLnAucCv+mdWVVvnqnAJEmSdF9TStySPB64CHgMzcPgAR7Wt5hdqJI0QgsPWjyn+1t62B5zuj9JU29x+zGwZVU9CyDJYuB1VXXtrEUmSZKk+5jqGLf0TT+T5ikKkiRJmiPDXpwwoT+RkyRJ0iybauJWrDiGzTFtkiRJc2iqY9wC/FeSO9vpDYAjktzRu1BVvXAmg5MkSdK9ppq4HdM3/V8zHYgkSZJWbkqJW1XtO9uBSJIkaeWme3GCJEmS5piJmyRJUkeYuEmSJHWEiZskSVJHmLhJkiR1hImbJElSR5i4SZIkdYSJmyRJUkeYuEmSJHXEWCRuSTZJcmKSS5NckuSpSTZNcmqSy9r3B446TkmSpFEai8QN+Ajw9ap6NPAE4BLgIOC0qtoeOK2dliRJmrdGnrgl2RjYGfgMQFX9vqpuBvbk3ofbHwO8aDQRSpIkjYeRJ27AdsBy4D+S/DjJkUk2AjavqmvbZa4DNh9ZhJIkSWNgHBK3dYAnAZ+sqj8DfkNft2hVFVCDVk6yf5IlSZYsX7581oOVJEkalXFI3JYBy6rq7Hb6RJpE7vokWwK07zcMWrmqDq+qRVW1aMGCBXMSsCRJ0iiMPHGrquuAq5I8qi3aFbgYOAXYpy3bBzh5BOFJkiSNjXVGHUDr74Fjk6wHXAHsS5NUnpBkP+BK4KUjjE+SJGnkxiJxq6rzgUUDZu0617FIkiSNq8saYkQAABAwSURBVJF3lUqSJGlqTNwkSZI6wsRNkiSpI0zcJEmSOmIsLk7Q7Fp40OJRhyBJkmaALW6SJEkdYeImSZLUESZukiRJHWHiJkmS1BEmbpIkSR1h4iZJktQRJm6SJEkdYeImSZLUESZukiRJHWHiJkmS1BEmbpIkSR1h4iZJktQRJm6SJEkdYeImSZLUESZukiRJHWHiJkmS1BEmbpIkSR1h4iZJktQRJm6SJEkdYeImSZLUESZukiRJHWHiJkmS1BEmbpIkSR2xzqgDkCR108KDFs/p/pYetsec7k8aR2PT4pZk7SQ/TvKVdnq7JGcnuTzJ55OsN+oYJUmSRmlsEjfgLcAlPdPvBz5UVY8AbgL2G0lUkiRJY2IsErckWwN7AEe20wGeDZzYLnIM8KLRRCdJkjQexiJxAz4MHADc004/CLi5qu5qp5cBWw1aMcn+SZYkWbJ8+fLZj1SSJGlERp64JXkBcENVnTud9avq8KpaVFWLFixYMMPRSZIkjY9xuKr06cALk/wlsAHwAOAjwCZJ1mlb3bYGrh5hjJIkSSM38ha3qjq4qrauqoXAXsC3q+oVwHeAl7SL7QOcPKIQJUmSxsLIE7eVOBB4e5LLaca8fWbE8UiSJI3UOHSV/lFVnQ6c3n6+AthxlPFIkiSNk3FucZMkSVIPEzdJkqSOMHGTJEnqCBM3SZKkjjBxkyRJ6ggTN0mSpI4wcZMkSeoIEzdJkqSOMHGTJEnqCBM3SZKkjjBxkyRJ6ggTN0mSpI4wcZMkSeoIEzdJkqSOMHGTJEnqCBM3SZKkjjBxkyRJ6ggTN0mSpI4wcZMkSeoIEzdJkqSOWGfUAcxHCw9aPOoQJElSB9niJkmS1BEmbpIkSR1h4iZJktQRJm6SJEkdYeImSZLUESZukiRJHWHiJkmS1BEjv49bkm2AzwKbAwUcXlUfSbIp8HlgIbAUeGlV3TSqOCVJozXX98Bcetgec7o/aSrGocXtLuAdVbUDsBPwxiQ7AAcBp1XV9sBp7bQkSdK8NfLEraqurarz2s+3AZcAWwF7Ase0ix0DvGg0EUqSJI2HkSduvZIsBP4MOBvYvKqubWddR9OVOmid/ZMsSbJk+fLlcxKnJEnSKIxN4pbk/sAXgbdW1a2986qqaMa/raCqDq+qRVW1aMGCBXMQqSRJ0miMReKWZF2apO3YqjqpLb4+yZbt/C2BG0YVnyRJ0jgYeeKWJMBngEuq6t96Zp0C7NN+3gc4ea5jkyRJGicjvx0I8HTgVcAFSc5vy94FHAackGQ/4ErgpSOKT5IkaSyMPHGrqu8BmWT2rnMZiyRJ0jgbeVepJEmSpsbETZIkqSNM3CRJkjrCxE2SJKkjTNwkSZI6wsRNkiSpI0Z+O5BxsPCgxaMOQZIkaZVscZMkSeoIEzdJkqSOMHGTJEnqCBM3SZKkjvDiBEmSBljTL1xbetgeow5B02CLmyRJUkeYuEmSJHWEiZskSVJHmLhJkiR1hImbJElSR5i4SZIkdYSJmyRJUkeYuEmSJHWEiZskSVJHmLhJkiR1hImbJElSR5i4SZIkdYSJmyRJUkeYuEmSJHWEiZskSVJHmLhJkiR1xDqjDmBVkuwGfARYGziyqg4bcUiSJHXewoMWz9m+lh62x5ztC+b22GBuj2+sW9ySrA18HNgd2AHYO8kOo41KkiRpNMa9xW1H4PKqugIgyfHAnsDFgxa+4Opb5jzLliRJK+d388wZ6xY3YCvgqp7pZW2ZJEnSvDPuLW6rlGR/YP928s4r3/+CC0cZz5jaDPjVqIMYQ9bLiqyTwayXwayXwayXFa3RdZL3T3vVRw27wrgnblcD2/RMb92W/VFVHQ4cDpBkSVUtmrvwusF6Gcx6WZF1Mpj1Mpj1Mpj1siLrZLAkS4ZdZ9y7Ss8Btk+yXZL1gL2AU0YckyRJ0kiMdYtbVd2V5E3AN2huB3JUVV004rAkSZJGYqwTN4Cq+irw1SkufvhsxtJh1stg1suKrJPBrJfBrJfBrJcVWSeDDV0vqarZCESSJEkzbNzHuEmSJKnV2cQtyVFJbkhyYU/Ze5JcneT89vWXo4xxriXZJsl3klyc5KIkb2nLN01yapLL2vcHjjrWubSSepnv58sGSX6U5Cdtvby3Ld8uydlJLk/y+fbCoHljJfVydJJf9JwvTxx1rHMtydpJfpzkK+30vD5XJgyoF8+VZGmSC9rjX9KWzevvIpi0Xob6Lups4gYcDew2oPxDVfXE9jXVsXFriruAd1TVDsBOwBvbR4QdBJxWVdsDp7XT88lk9QLz+3y5E3h2VT0BeCKwW5KdgPfT1MsjgJuA/UYY4yhMVi8A/9Bzvpw/uhBH5i3AJT3T8/1cmdBfL+C5AvCs9vgnbgMy37+LJvTXCwzxXdTZxK2qzgRuHHUc46Sqrq2q89rPt9H8IdmK5jFhx7SLHQO8aDQRjsZK6mVeq8bt7eS67auAZwMntuXz8XyZrF7mtSRbA3sAR7bTYZ6fK7BivWil5vV30UzpbOK2Em9K8tO2K3XeNcNOSLIQ+DPgbGDzqrq2nXUdsPmIwhq5vnqBeX6+tF085wM3AKcCPwdurqq72kXm5WPm+uulqibOl0Pb8+VDSdYfYYij8GHgAOCedvpBeK7AivUyYT6fK9D8s/PNJOe2TzgCv4tgcL3AEN9Fa1ri9kng4TTdG9cCHxxtOKOR5P7AF4G3VtWtvfOquYx4XrYeDKiXeX++VNXdVfVEmqeS7Ag8esQhjYX+eknyWOBgmvp5CrApcOAIQ5xTSV4A3FBV5446lnGyknqZt+dKj2dU1ZOA3WmGp+zcO3MefxcNqpehvovWqMStqq5v/+DeAxxB80U0ryRZlyY5ObaqTmqLr0+yZTt/S5pWhHllUL14vtyrqm4GvgM8FdgkycQ9Hld4zNx80lMvu7Vd7lVVdwL/wfw6X54OvDDJUuB4mi7Sj+C5skK9JPmveX6uAFBVV7fvNwBfoqmDef9dNKhehv0uWqMSt4kTovW/gHn1wPl2zMlngEuq6t96Zp0C7NN+3gc4ea5jG6XJ6sXzJQuSbNJ+vh/wXJrxf98BXtIuNh/Pl0H1cmnPF05oxubMm/Olqg6uqq2raiHNowe/XVWvYJ6fK5PUyyvn87kCkGSjJH8y8Rl4Hk0dzPfvooH1Mux30dg/OWEySY4DdgE2S7IMOATYpb3suoClwOtHFuBoPB14FXBBOz4H4F3AYcAJSfYDrgReOqL4RmWyetl7np8vWwLHJFmb5p+4E6rqK0kuBo5P8j7gxzRJ73wyWb18O8kCIMD5wN+OMsgxcSDz+1yZzLHz/FzZHPhSk7eyDvC5qvp6knOY399Fk9XLfw7zXeSTEyRJkjpijeoqlSRJWpOZuEmSJHWEiZskSVJHmLhJkiR1hImbJElSR5i4SRpLST6W5PRRx9EFSW5P8ppRxyFp9pm4SZq2JEcnqZ7Xr5J8JclYPDorydI2rlcNmHd2O++do4itjWHpKPcvqXtM3CStrm/R3LR2S5o7gd+P5lEu4+Iq4LW9Be2zRx8L/HokEUnSNJm4SVpdd1bVde3rPOBDwKPbx0UBkORxSb6V5LdJbmxb6jbumb92kn9NclP7+jCwds/8Vyf5dZL1e3ec5Ngkp6wivs8BT03ysJ6y/YATgdv7tvfKJOckuS3JDUm+kGSrnvnrJvlokmuS3JnkqiSH9cx/cZKf9hznGUk2n0oltusvTfJ/knw6ya1JliX5h75lHpHk9CS/S/KzNA8679/OVkmO76nPxUm2b+ctSHJtkkN6ln98u72/nmqskkbDxE3SjGmfw/cy4IKq+m1bthHwDZokaUeaZ/E9DTiqZ9V3AH9D86iXp9Ikba/omf8Fmr9Xe/bsa+N2W6t6zNKvgP8G9m3XWw945STrrUfz+LwnAC8ANgOO65n/5nafewHbt8f6s3a7W9A8aPwY4DHAzsB/riK2Qd4GXAA8CXg/8IEkT233sRZNa+ZaNPX0WuA9wB8T2iQb0jxD9HfAX7TLXQt8K8mGVbUceA3w7iRPbRPs44DjquoL04hX0hzq7LNKJY2N3ZJMtFxtRNM1+Zc981/elr+qqm4DSLI/8J0kj6iqy4G3Ah+oqhPa+W8Bnj+xgar6bZJjaRKVE3q2eyuweAoxHgV8um1leiFwc1Wd2T4z8I+qqjeZvCLJG4BLkmxdVcuAhwL/A3y3mucF/hL4Qbv8Q4B1gROr6sq2bDoPF/9mVX2s/fzvSd4M7Ar8EHgOsAOwXVX9EiDJW4Hv9qy/F80zMvdtYyTJ64EbaJLRE6rqG0k+ARwLnEGT+P39NGKVNMdscZO0us4Enti+dgROA76ZZJt2/mOAn04kba0fAPcAO7QtZ1vSJCYAVNU9wNl9+zkCeG6Srdvp1wLHVNVdU4jxGzTJzHNpukmPGrRQkiclOTnJlUluA5a0s7Zt349uj/N/knw8yR5tKxjAT2jG+12Y5ItJ3tA+aHxYP+2bvgZ4cPv5McDVE0lb62yaupzwZGA74Lb2atPbgVuABwIP71nuQOD3wKuBV1TVfbqNJY0nEzdJq+uOqrq8fZ0DvA54ALD/FNatqe6kqn4CnAe8pr24YBGTJGAD1r2HpgvzXcCz28/30dOlewfwKuApwG7t7PXa7ZwHLAQOpvn7eQxwapK1qupumosznkeTfO0HXJbkCVM9xtYf+sNnuL/VawHnc28yPfF6JPDpnuUWAtu0238YkjrBxE3STCuaFqAN2+lLgMe1498mPI3m788lVXULzRisnSZmpunD3HHAto+gGZ/1OuD7VfWzIeI6CngmcGpVXTNg/qNpxrS9q6rOrKpLubel696Dq7qtqk6sqjcAe9Akgo9o51VV/bCq3kuT+F1DMw5uplwCbNXTmglNPfX+LT+vjedXPQn1xOtGaC6yoLlo4xTgncAnkmyLpLFn4iZpda2fZIv29Rjg34H701wQAM04qjuAz7ZXl+5M0/JzUju+DeAjwAFJXpLkUcCHabpP+x0HbAG8gVVflHAfVXUFTWI22ZWTvwTuBN6U5GFJ9gD+qXeBJG9PsneSxyR5BPeOs1uWZKf2itCntEnQC2latC4eJs5V+BZwKU1dPrG9aOFDQG938bHA9cDJSf4iyXZJdk7ywYkrS9vjWkBTjx+h6W79bE+3r6Qx5S+ppNX1HJoWs2tpEoCnAH9dVacDVNUdNBcaPAD4EXAyzXi23nurfRD4D+DIdhtr0SQg99GOkzuBJsE6oX/+qlTVjRNXuw6YtxzYB3gRTbJ1CPD2vsVuA/6hPY7zaLogd2+P8Rbg6cBXgMvaY/qnqvqvYeNcSfz30FzVuhZtsgW8j6Y+Jpa5g+aK1itorsa9lKZL94HATUn+guYq3ldX1c3tBQyvobno4cCZilXS7Eh70ZEkdUKSrwHLqupvRh2LJM01bwciqROSPJBmjNrzaO6zJknzjombpK74MbApzcUD07k/miR1nl2lkiRJHeHFCZIkSR1h4iZJktQRJm6SJEkdYeImSZLUESZukiRJHWHiJkmS1BH/P0wgky3mxiDOAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# set binsize\n",
        "binsize = 2\n",
        "bins = np.arange(0, df_clean['bmi'].max()+binsize, binsize)\n",
        "\n",
        "# specify fig size\n",
        "plt.figure(figsize=[10, 6])\n",
        "\n",
        "# plot histogram\n",
        "plt.hist(data=df_clean, x='bmi', bins=bins)\n",
        "plt.xlim([15, 55])\n",
        "plt.xlabel('Body Mass Index', fontsize=14)\n",
        "plt.ylabel('Frequency', fontsize=14)\n",
        "plt.title('Distribution of Body Mass Index', fontsize=20);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JAGiqDLW5Rvb"
      },
      "source": [
        "> From the histogram above, it can be observed that the \"bmi\" is normally distributed"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T7vncJULe32h"
      },
      "source": [
        "### What is the relationship between \"smoker\" and \"charges\"?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "zVs4aeW65Rve",
        "outputId": "e8ee3604-0fe9-4c5e-879c-dae354183700",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# choose a base color\n",
        "base_color = sns.color_palette()[0]\n",
        "\n",
        "# create violinplot to view relationship\n",
        "plt.figure(figsize=[10, 5])\n",
        "sns.violinplot(data=df_clean, x='charges', y='smoker', color=base_color)\n",
        "plt.xlabel('Charges', fontsize=14)\n",
        "plt.ylabel('Smoker', fontsize=14)\n",
        "plt.title('Relationship between Smoker and Charges', fontsize=16);"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> This is a violin plot \n",
        "the white dot represents the median\n",
        "the thick gray bar in the center represents the interquartile range\n",
        "the thin gray line represents the rest of the distribution, except for points that are determined to be “outliers” using a method that is a function of the interquartile range\n",
        "\n",
        "> The violin plot above shows that the median charges of the smoker is graeter than the non-smoker. This indicate that the smokers pays higher medical charges than those who do not smoke."
      ],
      "metadata": {
        "id": "Hy3NXSd5EROX"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YCGNHReIeXkU"
      },
      "source": [
        "### What is the relationship between \"smoker\" and \"region\"?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "MWoz_9ZFeq-P",
        "outputId": "40f0cb8a-27ce-480d-fd90-aa8b284a9143",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# plot bar chart\n",
        "plt.figure(figsize=[10, 5])\n",
        "sns.countplot(data=df_clean, x='region', hue='smoker')\n",
        "plt.xlabel('Region')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Relationship between Smoker and Region');"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> Southeast and northeast region have the largest number of smokers from the four regions"
      ],
      "metadata": {
        "id": "6TbsQR29A_1k"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KFWRH7F1e_be"
      },
      "source": [
        "### What is the relationship between \"bmi\" and \"sex\"?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "ubN4POKc5Rvg",
        "outputId": "1ec188d6-8c31-434f-90fe-df2c09b807dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# choose a base color\n",
        "base_color = sns.color_palette()[0]\n",
        "\n",
        "# create boxplot to view relationship\n",
        "plt.figure(figsize=[10, 5])\n",
        "sns.boxplot(data=df_clean, x='bmi', y='sex', color=base_color)\n",
        "plt.xlabel('Body Mass Index', fontsize=14)\n",
        "plt.ylabel('Sex', fontsize=14)\n",
        "plt.title('Relationship between BMI and Sex', fontsize=16);"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> From the box plot above showing the relationship between 'bmi' and 'sex', it can be observed that the median of male bmi is higher than that of the female gender. This shows that male have higher bmi than the female gender."
      ],
      "metadata": {
        "id": "hZfvZheEXtoz"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wtIhToc6fASV"
      },
      "source": [
        "### What is the relationship between \"bmi\" and \"children\"?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "PDjAfoZB5Rvg",
        "outputId": "e35049f8-c2b0-4a4b-e27f-10d5fa8a1694",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# choose a base color\n",
        "base_color = sns.color_palette()[0]\n",
        "\n",
        "# create boxplot to view relationship\n",
        "plt.figure(figsize=[10, 6])\n",
        "sns.boxplot(data=df_clean, x='children', y='bmi', color=base_color)\n",
        "plt.xlabel('Number of Children', fontsize=14)\n",
        "plt.ylabel('Body Mass Index', fontsize=14)\n",
        "plt.title('Relationship between BMI and Number of Children', fontsize=16);"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> From the box plot, the beneficiaries with two (2) children/dependants seem to have higher bmi than the other groups."
      ],
      "metadata": {
        "id": "LMn7O7PAZfEB"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rsUMYmayfAsI"
      },
      "source": [
        "### What is the relationship between \"age\" and \"bmi\"?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "bO8_CHPc5Rvj",
        "outputId": "68713e8f-7b0d-4449-e322-27866dff25bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# scatter plot\n",
        "plt.figure(figsize=[10, 6])\n",
        "sns.scatterplot(data=df_clean, x='age', y='bmi', color=base_color, alpha=0.5);\n",
        "plt.xlabel('Age (year)', fontsize=14)\n",
        "plt.ylabel('Body Mass Index', fontsize=14)\n",
        "plt.title('Relationship between BMI and Age', fontsize=16);"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> The scatter plot shows that there no strong relationship between bmi and age of beneficiaries."
      ],
      "metadata": {
        "id": "xWeqy7jsacY4"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HnhmbEP65Rvj"
      },
      "source": [
        "### What is the relationship between \"charges\" and \"bmi\"?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "j_0f70Py5Rvk",
        "outputId": "202e24c3-89af-4d64-c355-7ca328cc08dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# line plot\n",
        "plt.figure(figsize=[10, 6])\n",
        "sns.scatterplot(data=df_clean, x='bmi', y='charges', color=base_color, alpha=0.5);\n",
        "plt.xlabel('Body Mass Index', fontsize=14)\n",
        "plt.ylabel('Charges', fontsize=14)\n",
        "plt.title('Relationship between BMI and Charges', fontsize=16);"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> From the scatter plot, medical charges tends to increase with increase in the body mass index of patient."
      ],
      "metadata": {
        "id": "CuR6zYxpad7O"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K0vG6xxl5Rvk"
      },
      "source": [
        "### What is the relationship between \"age\" and \"charges\"?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "ZTYHutYR5Rvl",
        "outputId": "9a08199e-23ca-4b53-9e51-283c1eaaedae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# line plot\n",
        "plt.figure(figsize=[10, 6])\n",
        "sns.lineplot(data=df_clean, x='age', y='charges', color=base_color);\n",
        "plt.xlabel('Age (year)', fontsize=14)\n",
        "plt.ylabel('Charges', fontsize=14)\n",
        "plt.title('Relationship between Charges and Age', fontsize=16);"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> From the line plot above, it can be seen that the medical charges tend to increase with increase in age."
      ],
      "metadata": {
        "id": "GUbB8XDjahSy"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Z75tgZdgBnU"
      },
      "source": [
        "### Which \"region\" have the highest number of \"children\"?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "yrPcez_o5Rvn",
        "outputId": "a3dff716-ecbf-4b24-bc8f-b02170d880d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 776
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# plot bar chart\n",
        "\n",
        "g = sns.catplot(\n",
        "    data=df_clean, \n",
        "    x='region', \n",
        "    col='children', \n",
        "    col_wrap=3, \n",
        "    kind='count', \n",
        "    color=base_color\n",
        ");\n",
        "g.set(xlabel='Region', ylabel='Frequency')\n",
        "g.set_titles(\"Number of Children = {col_name}\", fontsize=14)\n",
        "g.fig.suptitle('Relationship between Region and Children', fontsize=20, y=1.05);"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> From the bar plot above, the following conclution can be drawn;\n",
        "\n",
        "\n",
        "1.   Southeast has the largest number of beneficiaries with no child\n",
        "2.   Southeast has the largest number of beneficiaries with 1 child\n",
        "3.   Northwest and Southeast have the largest number of beneficiaries with 2 children\n",
        "4.   Northwest has the largest number of beneficiaries with 3 children\n",
        "5.   Southwest has the largest number of beneficiaries with more than 3 children\n",
        "\n"
      ],
      "metadata": {
        "id": "6JQjtzR_PJJh"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rw2ZGXhy5Rvr"
      },
      "source": [
        "### What is relationship between “region”, “smoker” and “bmi”?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "0qtzIQ565Rvs",
        "outputId": "b01c7aa0-9b84-483c-97b3-7663bff25f84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# plot bar chart\n",
        "g = sns.catplot(data=df_clean, x='region', y='bmi', col='smoker', ci=None, kind='bar', color=base_color)\n",
        "g.set(xlabel='Region', ylabel='BMI')\n",
        "g.set_titles(\"Smoker = {col_name}\", fontsize=14)\n",
        "g.fig.suptitle('Relationship between Smoker, Region and BMI', fontsize=20, y=1.05);"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> There seems to be no significant difference in the bmi of smokers and non-smokers across the four regions. "
      ],
      "metadata": {
        "id": "i_h-Pile3Fj8"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sei4Ewfnq7OP"
      },
      "source": [
        "### Are there outliers in \"bmi\"?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "Vn0DyNjEgtUI",
        "outputId": "53d619e5-718d-4ea2-bbda-1d689138ae00"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# check for outliers in bmi\n",
        "plt.figure(figsize=[8, 4])\n",
        "sns.boxplot(data=df_clean, x='bmi')\n",
        "plt.xlabel('Body Mass Index', fontsize=14)\n",
        "plt.title('Box Plot of Body Mass Index', fontsize=20);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NqOLADWJvfxi"
      },
      "source": [
        "The bmi value from the dataset, ranges from 15.96 to 53.13 with view values outside the spread of the boxplot. There tends to be some outlier in the dataset. To understand what outlier means in this domain, we search for the range of bmi value for adults between the age of 18 and 64."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fzgk4p4NrcOZ"
      },
      "source": [
        "> According to the National Health Service (NHS) in UK, the ideal BMI range for most adults is from 18.5 to 24.9. \n",
        "The NHS gave the following classification of BMI;\n",
        "> - below 18.5 – underweight range\n",
        "> - between 18.5 and 24.9 – healthy weight range\n",
        "> - between 25 and 29.9 – overweight range\n",
        "> - between 30 and 39.9 – obese range\n",
        "\n",
        "> (source: https://www.nhs.uk/common-health-questions/lifestyle/what-is-the-body-mass-index-bmi/)\n",
        "\n",
        "> Also for pregnant women, bmi could range from 37 - 54 \n",
        "(source: https://www.cdc.gov/reproductivehealth/maternalinfanthealth/pregnancy-weight-gain.htm) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
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          "height": 143
        },
        "id": "HXS2tz40nPgs",
        "outputId": "a9042416-5d7e-49c9-8f11-b6386dd4b611"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age   sex    bmi  children smoker     region     charges\n",
              "847    23  male  50.38         1     no  southeast   2438.0552\n",
              "1047   22  male  52.58         1    yes  southeast  44501.3982\n",
              "1317   18  male  53.13         0     no  southeast   1163.4627"
            ],
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              "      <td>53.13</td>\n",
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              "      <td>no</td>\n",
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            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "# check all the greater than 50\n",
        "df_clean[df_clean['bmi'] > 50]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> If we assume that some beneficiaries are suffering from severe obesity condition and some female in the dataset might be pregnant, male beneficiaries with bmi greater than 50 will be treated as outliers and will be dropped from the dataset."
      ],
      "metadata": {
        "id": "SsbYHS8XhnFL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# drop bmi for male greater than 50\n",
        "df_clean = df_clean[df_clean['bmi'] < 50]\n",
        "\n",
        "# check to confirm drop\n",
        "df_clean[df_clean['bmi'] > 50]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "jHDn7hWEmmMN",
        "outputId": "43a4026a-7a76-4184-db3c-960651015224"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [age, sex, bmi, children, smoker, region, charges]\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### What is the correlation between numeric variables?"
      ],
      "metadata": {
        "id": "mM34x0bhjWVR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# compute correlation\n",
        "corr = df_clean.corr()\n",
        "\n",
        "# plot correlation heatmap\n",
        "plt.figure(figsize=[10,4])\n",
        "sns.heatmap(corr, annot=True);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "-QvVWBGYjkUc",
        "outputId": "57940656-b7a4-47aa-826c-95e9a5848d1e"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> There is no much linear correlation between the numeric variables. Nevertheless, bmi and age seems to have low positive linear correlation with charges. There is no problem of multi-collinearity within the numeric features."
      ],
      "metadata": {
        "id": "c_eKoRMT4KUA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model building"
      ],
      "metadata": {
        "id": "GTi3Ycek5HnZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We start building of the model using linear regression and gradually increase the model complexity to observe how each model perform. We start without scalling the data and later scale the data to observe the effects of scalling the performance of the dataset "
      ],
      "metadata": {
        "id": "x51mzJ_GeskG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Split Data"
      ],
      "metadata": {
        "id": "nCdl-Fma8a6J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "features = df_clean.drop(\"charges\", axis=1)\n",
        "target = df_clean[\"charges\"]\n",
        "X = features\n",
        "y = target\n",
        "\n",
        "display(X.head())\n",
        "display(y.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "rWcxDnrABJnk",
        "outputId": "7d072d1f-59a6-4b62-ec6b-4d060849046f"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   age     sex     bmi  children smoker     region\n",
              "0   19  female  27.900         0    yes  southwest\n",
              "1   18    male  33.770         1     no  southeast\n",
              "2   28    male  33.000         3     no  southeast\n",
              "3   33    male  22.705         0     no  northwest\n",
              "4   32    male  28.880         0     no  northwest"
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bmi</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a7d5c130-9c73-4f9e-bbed-34d6d2308f3f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "          document.querySelector('#df-a7d5c130-9c73-4f9e-bbed-34d6d2308f3f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a7d5c130-9c73-4f9e-bbed-34d6d2308f3f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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          },
          "metadata": {}
        },
        {
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          "data": {
            "text/plain": [
              "0    16884.92400\n",
              "1     1725.55230\n",
              "2     4449.46200\n",
              "3    21984.47061\n",
              "4     3866.85520\n",
              "Name: charges, dtype: float64"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# encode categorical variables\n",
        "le = LabelEncoder()\n",
        "X[\"sex\"]  = le.fit_transform(X[\"sex\"])\n",
        "X[\"smoker\"]  = le.fit_transform(X[\"smoker\"])\n",
        "X[\"region\"]  = le.fit_transform(X[\"region\"])"
      ],
      "metadata": {
        "id": "weVjj-z4Ih7D"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Kgf0incnI7cz",
        "outputId": "04720d2c-d180-4608-c5a2-24a8c0e29c5f"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  sex     bmi  children  smoker  region\n",
              "0   19    0  27.900         0       1       3\n",
              "1   18    1  33.770         1       0       2\n",
              "2   28    1  33.000         3       0       2\n",
              "3   33    1  22.705         0       0       1\n",
              "4   32    1  28.880         0       0       1"
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</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>0</th>\n",
              "      <td>19</td>\n",
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              "      <td>27.900</td>\n",
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              "      <td>1</td>\n",
              "      <td>3</td>\n",
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              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>33.770</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
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              "      <td>33.000</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
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              "      <td>22.705</td>\n",
              "      <td>0</td>\n",
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              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "      <td>28.880</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-04975a22-95a0-496b-aaf3-079aa7917f6f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-04975a22-95a0-496b-aaf3-079aa7917f6f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-04975a22-95a0-496b-aaf3-079aa7917f6f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# split training and testing data with ratio 80 to 20\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=65)"
      ],
      "metadata": {
        "id": "auLMjc1FDIT5"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape)\n",
        "print(X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LIXDTymnEDkd",
        "outputId": "cd790d6e-3228-4517-d989-67dee26e0d41"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1067, 6)\n",
            "(267, 6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Baseline Model"
      ],
      "metadata": {
        "id": "5acVVqZ1E9cZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_mean = y_train.mean()\n",
        "y_pred_baseline = [y_mean] * len(y_train)\n",
        "print(\"Mean Charges: \", round(y_mean, 2))\n",
        "print(\"Baseline MAE: \", round(mean_absolute_error(y_train, y_pred_baseline),2))\n",
        "print(\"Baseline R2 Score: \", round(r2_score(y_train, y_pred_baseline),2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nNTzdf7sFA11",
        "outputId": "261ad5d6-9d9a-48e1-aa88-6ed4f5f4887c"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Charges:  12843.65\n",
            "Baseline MAE:  8675.59\n",
            "Baseline R2 Score:  0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> We set a baseline model which predict the mean of the target variable for all predictions. Our model should perform better than the baseline model"
      ],
      "metadata": {
        "id": "2Hc-4zp5_cBw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Linear Regression Model"
      ],
      "metadata": {
        "id": "pez5FqEyFfKq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "linreg_model = LinearRegression()\n",
        "linreg_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4HDGBj0bFeBd",
        "outputId": "fd787d4d-c9bd-4419-8d4d-b6e89946fa8d"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_training = linreg_model.predict(X_train)\n",
        "y_pred_test = linreg_model.predict(X_test)\n",
        "print(\"Training MAE:\", round(mean_absolute_error(y_train, y_pred_training),2))\n",
        "print(\"Test MAE:\", round(mean_absolute_error(y_test, y_pred_test),2))\n",
        "print(\"\\nTraining R2 Score: \", round(r2_score(y_train, y_pred_training),2))\n",
        "print(\"Test R2 Score: \", round(r2_score(y_test, y_pred_test),2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iTRwnRKCGpWE",
        "outputId": "f36c2642-5711-4349-afd7-6cea634ad199"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training MAE: 4131.34\n",
            "Test MAE: 4308.41\n",
            "\n",
            "Training R2 Score:  0.73\n",
            "Test R2 Score:  0.79\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> The linear regression model performs better than the baseline model. It also performs better on both the training and test data. Since the difference between the mean absolute error of the training and test data is small, the model does not over fit the training data.\n",
        "\n",
        "> We will explore other models to see if we can get a better performing model. "
      ],
      "metadata": {
        "id": "V5UCgRHECQdL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Random Forest Regressor Model"
      ],
      "metadata": {
        "id": "R04C5JflMilB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf_model1 = RandomForestRegressor()\n",
        "rf_model1.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "ewuWXGQiPhIG",
        "outputId": "1012a543-dd4a-43d2-bc42-45721939498c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor()"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_training = rf_model1.predict(X_train)\n",
        "y_pred_test = rf_model1.predict(X_test)\n",
        "print(\"Training MAE:\", round(mean_absolute_error(y_train, y_pred_training),2))\n",
        "print(\"Test MAE:\", round(mean_absolute_error(y_test, y_pred_test),2))\n",
        "print(\"\\nTraining R2 Score: \", round(r2_score(y_train, y_pred_training),2))\n",
        "print(\"Test R2 Score: \", round(r2_score(y_test, y_pred_test),2))"
      ],
      "metadata": {
        "id": "foAAduLVPpax",
        "outputId": "d5d0d4bc-faf3-4ea6-9d64-31601f3c9c60",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training MAE: 1134.59\n",
            "Test MAE: 2777.67\n",
            "\n",
            "Training R2 Score:  0.97\n",
            "Test R2 Score:  0.86\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> The random forest regressor performs very well on the training data and poorly on the test data, that is, it over fit. We try to change the input parameters to improve the performance of the model "
      ],
      "metadata": {
        "id": "ghqCPllsY7wW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf_model2 = RandomForestRegressor(n_estimators=10, max_depth=5)\n",
        "rf_model2.fit(X_train, y_train)\n",
        "\n",
        "y_pred_training = rf_model2.predict(X_train)\n",
        "y_pred_test = rf_model2.predict(X_test)\n",
        "print(\"Training MAE:\", round(mean_absolute_error(y_train, y_pred_training),2))\n",
        "print(\"Test MAE:\", round(mean_absolute_error(y_test, y_pred_test),2))\n",
        "print(\"\\nTraining R2 Score: \", round(r2_score(y_train, y_pred_training),2))\n",
        "print(\"Test R2 Score: \", round(r2_score(y_test, y_pred_test),2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b5WFw6H9YcEH",
        "outputId": "f6b803f5-041d-4e20-d3b8-292a4ed0f762"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training MAE: 2257.18\n",
            "Test MAE: 2519.37\n",
            "\n",
            "Training R2 Score:  0.88\n",
            "Test R2 Score:  0.88\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> After several trial of changing the maximum depth and number of estimator, we got a stable performance at n_estimator=10 and max_depth=5"
      ],
      "metadata": {
        "id": "CTzG50KAZyIM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Xgboost Model"
      ],
      "metadata": {
        "id": "9wQEniROPsJ2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xgb1_model = XGBRegressor()\n",
        "xgb1_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-R-ZSaUePxHG",
        "outputId": "3bf7206b-a605-42c4-fb1b-c3f4581d97c4"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[01:05:38] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor()"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_training = xgb1_model.predict(X_train)\n",
        "y_pred_test = xgb1_model.predict(X_test)\n",
        "print(\"Training MAE:\", round(mean_absolute_error(y_train, y_pred_training),2))\n",
        "print(\"Test MAE:\", round(mean_absolute_error(y_test, y_pred_test),2))\n",
        "print(\"\\nTraining R2 Score: \", round(r2_score(y_train, y_pred_training),2))\n",
        "print(\"Test R2 Score: \", round(r2_score(y_test, y_pred_test),2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2WOFXOjTQCjK",
        "outputId": "60d9f365-d7c6-442e-9bd2-0431fabb9d27"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training MAE: 2052.78\n",
            "Test MAE: 2513.85\n",
            "\n",
            "Training R2 Score:  0.89\n",
            "Test R2 Score:  0.88\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> The Xgboost model performs well on both training and test data. The R2 score is similar to what we got with the Random Foreest Regressor. We try to tune some parameter to improve the performance of the model"
      ],
      "metadata": {
        "id": "-omtFkgTtuha"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xgb2_model = XGBRegressor(n_estimators=100, max_depth=2)\n",
        "xgb2_model.fit(X_train, y_train)\n",
        "\n",
        "y_pred_training = xgb2_model.predict(X_train)\n",
        "y_pred_test = xgb2_model.predict(X_test)\n",
        "print(\"Training MAE:\", round(mean_absolute_error(y_train, y_pred_training),2))\n",
        "print(\"Test MAE:\", round(mean_absolute_error(y_test, y_pred_test),2))\n",
        "print(\"\\nTraining R2 Score: \", round(r2_score(y_train, y_pred_training),2))\n",
        "print(\"Test R2 Score: \", round(r2_score(y_test, y_pred_test),2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DfwE9HplbWCZ",
        "outputId": "d9302829-67b0-4fe3-ef12-f723c18607ac"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[01:20:10] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "Training MAE: 2012.0\n",
            "Test MAE: 3419.0\n",
            "\n",
            "Training R2 Score:  0.92\n",
            "Test R2 Score:  0.83\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> On several trials, the Xgboost model either over fit or under fit on the training data. We were unable to improve the performance of the model"
      ],
      "metadata": {
        "id": "_B3INfIdvIex"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Preproccessing"
      ],
      "metadata": {
        "id": "Sh-m0kJZf7o0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Xtrain = X_train.copy()\n",
        "Xtest = X_test.copy()\n",
        "\n",
        "#Standard Scaler\n",
        "scaler = StandardScaler()\n",
        "X_train_standard = scaler.fit_transform(X_train)\n",
        "X_test_standard = scaler.fit_transform(X_test)\n",
        "\n",
        "#Minmax Scaler\n",
        "scaler = MinMaxScaler()\n",
        "X_train_minmax = scaler.fit_transform(X_train)\n",
        "X_test_minmax = scaler.fit_transform(X_test)\n",
        "\n",
        "#Normalization\n",
        "X_train_normalize = preprocessing.normalize(X_train)\n",
        "X_test_normalize = preprocessing.normalize(X_test)\n",
        "\n",
        "train_list = [Xtrain,X_train_standard,X_train_minmax,X_train_normalize]\n",
        "scaler_list = [\"without_scaler\",\"standard_scaler\",\"minmax_scaler\",\"normalize\"]"
      ],
      "metadata": {
        "id": "sOtHljxCgLxM"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "> We used two different scaler and one normalizer and raw data to try out all types and decide which gives the best score"
      ],
      "metadata": {
        "id": "mAQpgV3nhIq6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Selection using Cross Validation"
      ],
      "metadata": {
        "id": "_gAyZB0AtR1d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kfold = StratifiedKFold(n_splits=10)\n",
        "random_state = 42\n",
        "z = 0\n",
        "\n",
        "for i in train_list:\n",
        "    linear_model = LinearRegression().fit(i,y_train)\n",
        "    knn_model = KNeighborsRegressor().fit(i, y_train)\n",
        "    decision_model = DecisionTreeRegressor().fit(i,y_train)\n",
        "    mlp_model = MLPRegressor().fit(i, y_train)\n",
        "    gaussian_model = BayesianRidge().fit(i, y_train)\n",
        "    linear_svm_model = SVR(kernel='linear').fit(i,y_train)\n",
        "    adaboost_model = AdaBoostRegressor(DecisionTreeRegressor(), learning_rate=0.1).fit(i,y_train)\n",
        "    randomforest_model = RandomForestRegressor().fit(i,y_train)\n",
        "    extra_model = ExtraTreesRegressor().fit(i,y_train)\n",
        "    gb_model = GradientBoostingRegressor().fit(i,y_train)\n",
        "    xgb_model = XGBRegressor().fit(i,y_train)\n",
        "    lgbm_model = LGBMRegressor().fit(i,y_train)\n",
        "    elastic_model = ElasticNet().fit(i,y_train)\n",
        "    sgd_model = SGDRegressor().fit(i,y_train)\n",
        "    kernel_model = KernelRidge().fit(i,y_train)\n",
        "    model_names = [\"Linear\",\"Knn\",\"DecisionTree\",\"MLP\",\"GaussianNB\",\"SupportVectorMachine\",\"AdaBoost\",\"RandomForest\",\"ExtraTrees\",\"GradientBoost\",\"Xgboost\",\"Lightgbm\", \"Elastic\", \"SGD\", \"Kernel Ridge\"]\n",
        "    model_list = [linear_model,knn_model,decision_model,mlp_model,gaussian_model,linear_svm_model,adaboost_model,randomforest_model,extra_model,gb_model,xgb_model,lgbm_model,elastic_model,sgd_model,kernel_model]\n",
        "    results = []\n",
        "    z +=1\n",
        "    if z ==1:\n",
        "        print(\"*\"*30, f\"{scaler_list[z-1]}\",\"*\"*30)\n",
        "    if z ==2:\n",
        "        print(\"*\"*30, f\"{scaler_list[z-1]}\",\"*\"*30)\n",
        "    if z ==3:\n",
        "        print(\"*\"*30, f\"{scaler_list[z-1]}\",\"*\"*30)\n",
        "    if z ==4:\n",
        "        print(\"*\"*30, f\"{scaler_list[z-1]}\",\"*\"*30)\n",
        "    for j in model_list:\n",
        "        result = cross_val_score(j, i, y_train, scoring = \"r2\", cv = 5, n_jobs=4)\n",
        "        results.append(result.mean())\n",
        "\n",
        "    acc_of_models = {\"Model\": model_names, \"R2\": results}    \n",
        "    acc_of_models = pd.DataFrame(acc_of_models)\n",
        "    display(acc_of_models)\n",
        "    print(np.max(acc_of_models[\"R2\"]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "CO1VSwxThBGL",
        "outputId": "72a3bef9-3caa-4299-f56b-0d14b9bf1426"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[02:44:58] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "****************************** without_scaler ******************************\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                   Model            R2\n",
              "0                 Linear  7.227982e-01\n",
              "1                    Knn  4.685128e-02\n",
              "2           DecisionTree  6.587124e-01\n",
              "3                    MLP -1.427832e-01\n",
              "4             GaussianNB  7.228145e-01\n",
              "5   SupportVectorMachine -1.219639e-01\n",
              "6               AdaBoost  7.834992e-01\n",
              "7           RandomForest  8.256897e-01\n",
              "8             ExtraTrees  8.037828e-01\n",
              "9          GradientBoost  8.397216e-01\n",
              "10               Xgboost  8.435270e-01\n",
              "11              Lightgbm  8.267576e-01\n",
              "12               Elastic  3.702524e-01\n",
              "13                   SGD -1.794256e+13\n",
              "14          Kernel Ridge  6.957435e-01"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5bf483a8-64a9-4542-a9e6-5d424598bb43\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "\n",
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              "</style>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>R2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Linear</td>\n",
              "      <td>7.227982e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Knn</td>\n",
              "      <td>4.685128e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>DecisionTree</td>\n",
              "      <td>6.587124e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>MLP</td>\n",
              "      <td>-1.427832e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GaussianNB</td>\n",
              "      <td>7.228145e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>SupportVectorMachine</td>\n",
              "      <td>-1.219639e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>AdaBoost</td>\n",
              "      <td>7.834992e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>RandomForest</td>\n",
              "      <td>8.256897e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>ExtraTrees</td>\n",
              "      <td>8.037828e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>GradientBoost</td>\n",
              "      <td>8.397216e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Xgboost</td>\n",
              "      <td>8.435270e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Lightgbm</td>\n",
              "      <td>8.267576e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Elastic</td>\n",
              "      <td>3.702524e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>SGD</td>\n",
              "      <td>-1.794256e+13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Kernel Ridge</td>\n",
              "      <td>6.957435e-01</td>\n",
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            "0.8435269993548269\n"
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          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[02:45:11] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "****************************** standard_scaler ******************************\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                   Model        R2\n",
              "0                 Linear  0.722798\n",
              "1                    Knn  0.788817\n",
              "2           DecisionTree  0.659552\n",
              "3                    MLP -1.144541\n",
              "4             GaussianNB  0.722823\n",
              "5   SupportVectorMachine -0.025136\n",
              "6               AdaBoost  0.795517\n",
              "7           RandomForest  0.825296\n",
              "8             ExtraTrees  0.803305\n",
              "9          GradientBoost  0.840040\n",
              "10               Xgboost  0.843504\n",
              "11              Lightgbm  0.826083\n",
              "12               Elastic  0.643478\n",
              "13                   SGD  0.722873\n",
              "14          Kernel Ridge -0.516167"
            ],
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              "      <th>Model</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>0.788817</td>\n",
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              "      <th>2</th>\n",
              "      <td>DecisionTree</td>\n",
              "      <td>0.659552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>MLP</td>\n",
              "      <td>-1.144541</td>\n",
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              "      <th>4</th>\n",
              "      <td>GaussianNB</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>AdaBoost</td>\n",
              "      <td>0.795517</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>RandomForest</td>\n",
              "      <td>0.825296</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>ExtraTrees</td>\n",
              "      <td>0.803305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>GradientBoost</td>\n",
              "      <td>0.840040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Xgboost</td>\n",
              "      <td>0.843504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Lightgbm</td>\n",
              "      <td>0.826083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Elastic</td>\n",
              "      <td>0.643478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>SGD</td>\n",
              "      <td>0.722873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Kernel Ridge</td>\n",
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              "          const element = document.querySelector('#df-3335c686-bab3-4da5-9fe6-29b0da7bcf8b');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
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          "metadata": {}
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        {
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          "name": "stdout",
          "text": [
            "0.8435037096690927\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[02:45:21] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "****************************** minmax_scaler ******************************\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                   Model        R2\n",
              "0                 Linear  0.722798\n",
              "1                    Knn  0.753904\n",
              "2           DecisionTree  0.675849\n",
              "3                    MLP -1.118080\n",
              "4             GaussianNB  0.722827\n",
              "5   SupportVectorMachine -0.087998\n",
              "6               AdaBoost  0.791661\n",
              "7           RandomForest  0.824695\n",
              "8             ExtraTrees  0.801883\n",
              "9          GradientBoost  0.839549\n",
              "10               Xgboost  0.843488\n",
              "11              Lightgbm  0.826317\n",
              "12               Elastic  0.284088\n",
              "13                   SGD  0.722336\n",
              "14          Kernel Ridge  0.721761"
            ],
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              "      <th>0</th>\n",
              "      <td>Linear</td>\n",
              "      <td>0.722798</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Knn</td>\n",
              "      <td>0.753904</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>DecisionTree</td>\n",
              "      <td>0.675849</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>MLP</td>\n",
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              "      <th>4</th>\n",
              "      <td>GaussianNB</td>\n",
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              "      <th>5</th>\n",
              "      <td>SupportVectorMachine</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>AdaBoost</td>\n",
              "      <td>0.791661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>RandomForest</td>\n",
              "      <td>0.824695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>ExtraTrees</td>\n",
              "      <td>0.801883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>GradientBoost</td>\n",
              "      <td>0.839549</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Xgboost</td>\n",
              "      <td>0.843488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Lightgbm</td>\n",
              "      <td>0.826317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Elastic</td>\n",
              "      <td>0.284088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>SGD</td>\n",
              "      <td>0.722336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Kernel Ridge</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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            "0.843488381827503\n"
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          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[02:45:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "****************************** normalize ******************************\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                   Model        R2\n",
              "0                 Linear  0.518230\n",
              "1                    Knn  0.269122\n",
              "2           DecisionTree  0.633352\n",
              "3                    MLP -1.177395\n",
              "4             GaussianNB  0.518279\n",
              "5   SupportVectorMachine -0.099805\n",
              "6               AdaBoost  0.775049\n",
              "7           RandomForest  0.807346\n",
              "8             ExtraTrees  0.800729\n",
              "9          GradientBoost  0.803752\n",
              "10               Xgboost  0.808315\n",
              "11              Lightgbm  0.789165\n",
              "12               Elastic  0.003000\n",
              "13                   SGD  0.046024\n",
              "14          Kernel Ridge  0.099130"
            ],
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              "      <td>0.269122</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>DecisionTree</td>\n",
              "      <td>0.633352</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>MLP</td>\n",
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              "      <th>4</th>\n",
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              "      <th>6</th>\n",
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              "      <th>7</th>\n",
              "      <td>RandomForest</td>\n",
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              "      <th>9</th>\n",
              "      <td>GradientBoost</td>\n",
              "      <td>0.803752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Xgboost</td>\n",
              "      <td>0.808315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Lightgbm</td>\n",
              "      <td>0.789165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Elastic</td>\n",
              "      <td>0.003000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>SGD</td>\n",
              "      <td>0.046024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Kernel Ridge</td>\n",
              "      <td>0.099130</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {}
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          "name": "stdout",
          "text": [
            "0.8083146539531253\n"
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> The best score was XGBRegrossor algorithm trained with min-max scaled data"
      ],
      "metadata": {
        "id": "A_hEEp2IyqUK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Xgboost Model with Scaled Data"
      ],
      "metadata": {
        "id": "YamTHZ5I2hcK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_model = XGBRegressor()\n",
        "xgb_model.fit(X_train_minmax, y_train)\n",
        "\n",
        "y_pred_training = xgb_model.predict(X_train_minmax)\n",
        "y_pred_test = xgb_model.predict(X_test_minmax)\n",
        "print(\"Training MAE:\", round(mean_absolute_error(y_train, y_pred_training),2))\n",
        "print(\"Test MAE:\", round(mean_absolute_error(y_test, y_pred_test),2))\n",
        "print(\"\\nTraining R2 Score: \", round(r2_score(y_train, y_pred_training),2))\n",
        "print(\"Test R2 Score: \", round(r2_score(y_test, y_pred_test),2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oTS8dSW021xe",
        "outputId": "963aaed6-bacc-4fb9-897d-17c98dfb944f"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[03:09:03] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "Training MAE: 2052.78\n",
            "Test MAE: 2632.8\n",
            "\n",
            "Training R2 Score:  0.89\n",
            "Test R2 Score:  0.86\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> The scaled dataset almost produced a similar result when compared with the non-scaled data. The performance of the non-scaled data is better, that is, scalling those not necessarily improved the performance of the model."
      ],
      "metadata": {
        "id": "3cDW7mY8I2dO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Hyperparameter Tuning Using Grid Search"
      ],
      "metadata": {
        "id": "SdQ3aHC53oog"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def algorithm_pipeline(X_train_data, X_test_data, y_train_data, y_test_data, \n",
        "                       model, param_grid, cv=10, scoring_fit='r2',\n",
        "                       do_probabilities = False):\n",
        "    gs = GridSearchCV(\n",
        "        estimator=model,\n",
        "        param_grid=param_grid, \n",
        "        cv=cv, \n",
        "        n_jobs=-1, \n",
        "        scoring=scoring_fit,\n",
        "        verbose=2\n",
        "    )\n",
        "    fitted_model = gs.fit(X_train_data, y_train_data)\n",
        "    \n",
        "    if do_probabilities:\n",
        "      pred = fitted_model.predict_proba(X_test_data)\n",
        "    else:\n",
        "      pred = fitted_model.predict(X_test_data)\n",
        "    \n",
        "    return fitted_model, pred"
      ],
      "metadata": {
        "id": "4ajaCUpZ3_-u"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = XGBRegressor()\n",
        "param_grid = {\n",
        "    'n_estimators': [100, 200, 300],\n",
        "    'colsample_bytree': [0.5, 0.6, 0.7],\n",
        "    'max_depth': [5, 10, 15],\n",
        "    'reg_alpha': [1.1, 1.2, 1.3],\n",
        "    'reg_lambda': [1.1, 1.2, 1.3],\n",
        "    'subsample': [0.5, 0.7, 0.8]\n",
        "}\n",
        "\n",
        "model, pred = algorithm_pipeline(X_train_minmax, \n",
        "                                 X_test_minmax,\n",
        "                                 y_train, y_test, \n",
        "                                 model, \n",
        "                                 param_grid, \n",
        "                                 cv=5)\n",
        "\n",
        "# Root Mean Squared Error\n",
        "print(model.best_score_)\n",
        "print(model.best_params_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yCZ6o29m4K1V",
        "outputId": "88e0c6b5-e23c-459a-bf3f-38096c59c258"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 729 candidates, totalling 3645 fits\n",
            "[03:55:58] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "0.826979434579499\n",
            "{'colsample_bytree': 0.7, 'max_depth': 5, 'n_estimators': 100, 'reg_alpha': 1.3, 'reg_lambda': 1.3, 'subsample': 0.8}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(round(r2_score(y_test, pred),2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fsy1aniwArbU",
        "outputId": "4499525a-ac40-4d9f-e09f-eb68a8b0e988"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.85\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The grid search hyperparameter tunning we carried did not result into a better performed model. Due to the computation expensivity of the grid search, we could not increase the tuning parameters."
      ],
      "metadata": {
        "id": "UIw_AjSkJnzs"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v5PO7KOak2Ly"
      },
      "source": [
        "## Conclusions"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "After model selection and hyperparameter tunning, the best performing models are Xgboost and Random Forest Regressor. They both performed well on the training and test data resulting to an R-squared of 88%."
      ],
      "metadata": {
        "id": "oCLk6QezK2_n"
      }
    }
  ],
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      "language": "python",
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