Medical_Insurance_Cost_Prediction.ipynb

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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "URmAxWVIRiR7"
      },
      "source": [
        "Importing the Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dCei9HxoNdG5"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn import metrics"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k4ydeF1FSK2n"
      },
      "source": [
        "Data Collection & Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HvkkGhfRSGdn"
      },
      "source": [
        "# loading the data from csv file to a Pandas DataFrame\n",
        "insurance_dataset = pd.read_csv('/content/insurance.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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          "height": 196
        },
        "id": "jKHJM3wUSfhe",
        "outputId": "33b466ad-af74-491e-b3f9-4dadff8cca4a"
      },
      "source": [
        "# first 5 rows of the dataframe\n",
        "insurance_dataset.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>smoker</th>\n",
              "      <th>region</th>\n",
              "      <th>charges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>female</td>\n",
              "      <td>27.900</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>southwest</td>\n",
              "      <td>16884.92400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>male</td>\n",
              "      <td>33.770</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>1725.55230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>male</td>\n",
              "      <td>33.000</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>4449.46200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>male</td>\n",
              "      <td>22.705</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>21984.47061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>male</td>\n",
              "      <td>28.880</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>3866.85520</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age     sex     bmi  children smoker     region      charges\n",
              "0   19  female  27.900         0    yes  southwest  16884.92400\n",
              "1   18    male  33.770         1     no  southeast   1725.55230\n",
              "2   28    male  33.000         3     no  southeast   4449.46200\n",
              "3   33    male  22.705         0     no  northwest  21984.47061\n",
              "4   32    male  28.880         0     no  northwest   3866.85520"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "qB730MywSmwM",
        "outputId": "70945582-388b-4ac7-b743-fac830f1ec4f"
      },
      "source": [
        "# number of rows and columns\n",
        "insurance_dataset.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1338, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "zbBK33o-S_Q7",
        "outputId": "a0248809-e6b1-42a9-d2b3-dc67a10a4460"
      },
      "source": [
        "# getting some informations about the dataset\n",
        "insurance_dataset.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yjwwR0fnTg2H"
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      "source": [
        "Categorical Features:\n",
        "- Sex\n",
        "- Smoker\n",
        "- Region"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        },
        "id": "-DsX_XZUTOw8",
        "outputId": "83883e4f-cb77-40b7-8d76-f3d8de97b0a0"
      },
      "source": [
        "# checking for missing values\n",
        "insurance_dataset.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         0\n",
              "sex         0\n",
              "bmi         0\n",
              "children    0\n",
              "smoker      0\n",
              "region      0\n",
              "charges     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "39CD23eNUBpW"
      },
      "source": [
        "Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "H3JJHLVgT4kV",
        "outputId": "847ad66b-06c6-4b4e-bb4c-2ffba55977a2"
      },
      "source": [
        "# statistical Measures of the dataset\n",
        "insurance_dataset.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>charges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1338.000000</td>\n",
              "      <td>1338.000000</td>\n",
              "      <td>1338.000000</td>\n",
              "      <td>1338.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>39.207025</td>\n",
              "      <td>30.663397</td>\n",
              "      <td>1.094918</td>\n",
              "      <td>13270.422265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>14.049960</td>\n",
              "      <td>6.098187</td>\n",
              "      <td>1.205493</td>\n",
              "      <td>12110.011237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>15.960000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1121.873900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>27.000000</td>\n",
              "      <td>26.296250</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4740.287150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>39.000000</td>\n",
              "      <td>30.400000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9382.033000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>51.000000</td>\n",
              "      <td>34.693750</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>16639.912515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>64.000000</td>\n",
              "      <td>53.130000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>63770.428010</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               age          bmi     children       charges\n",
              "count  1338.000000  1338.000000  1338.000000   1338.000000\n",
              "mean     39.207025    30.663397     1.094918  13270.422265\n",
              "std      14.049960     6.098187     1.205493  12110.011237\n",
              "min      18.000000    15.960000     0.000000   1121.873900\n",
              "25%      27.000000    26.296250     0.000000   4740.287150\n",
              "50%      39.000000    30.400000     1.000000   9382.033000\n",
              "75%      51.000000    34.693750     2.000000  16639.912515\n",
              "max      64.000000    53.130000     5.000000  63770.428010"
            ]
          },
          "metadata": {
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          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "id": "thRxLvZwUJNW",
        "outputId": "052869c8-5a51-41d0-fca8-725c960a98c8"
      },
      "source": [
        "# distribution of age value\n",
        "sns.set()\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.distplot(insurance_dataset['age'])\n",
        "plt.title('Age Distribution')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
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          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "sJaFjdJJU644",
        "outputId": "f9181f98-6107-49ac-cbb8-8596d989a5c1"
      },
      "source": [
        "# Gender column\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.countplot(x='sex', data=insurance_dataset)\n",
        "plt.title('Sex Distribution')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AEYIBADBCMAAARggGAMAIwQAAGCEYAAAjBAMAYIRgAACMEAwAgBGCAQAwQjAAAEYIBgDACMEAABghGAAAIwQDAGCEYAAAjBAMAIARggEAMEIwAABGCAYAwAjBAAAYIRgAACMEAwBghGAAAIzYHoxFixapQ4cO+uqrryRJu3bt0sCBAxUXF6cRI0aoqKjId9uaxgAA9rI1GJ9//rl27dqlNm3aSJK8Xq8mTpyo5ORkZWVlKSYmRnPnzj3rGADAfrYFo7KyUtOnT9e0adN8y/bs2aOAgADFxMRIkoYNG6a33377rGMAAPv52bWh+fPna+DAgbr88st9y/Lz89W6dWvf78HBwfJ6vSopKalxLCgoyHi7ISGBtfMAgF8JDW1W31MAqlVX+6ctwcjJydGePXs0YcIEOzZ3mqKiMnm91m9alycFVOfo0dL6ngL7J6p1Pvun0+mo9g9tW4LxySefaN++ferdu7ck6bvvvtNDDz2ke++9V0eOHPHdrri4WE6nU0FBQXK73dWOAQDsZ8trGKNHj9YHH3yg7OxsZWdnKyIiQitXrtTIkSNVXl6unTt3SpLWrVunfv36SZI6duxY7RgAwH62vYZRFafTqbS0NKWkpKiiokJt2rRRenr6WccAAParl2BkZ2f7fu7SpYsyMjKqvF1NYwAAexmfklq5cmWVy1966aVamwwAoOEyDsbixYurXL506dJamwwAoOE66ymp7du3Szr1yeuPPvpIlvV/b1HNy8tT06ZN6252AIAG46zBmDx5siSpoqJCTz/9tG+5w+FQaGiopkyZUnezAwA0GGcNxi8vUE+aNElpaWl1PiEAQMNk/C6pX8fC6/WeNuZ0cpV0ALjYGQfj888/1/Tp0/Xll1+qoqJCkmRZlhwOh7744os6myAAoGEwDkZSUpJ69eql2bNnq1GjRnU5JwBAA2QcjMOHD+uJJ56Qw+Goy/kAABoo4xcf+vbtqw8++KAu5wIAaMCMjzAqKio0btw4de3aVa1atTptjHdPAcDFzzgYV155pa688sq6nAsAoAEzDsa4cePqch4AgAbOOBi/XCKkKjfeeGOtTAYA0HAZB+OXS4T84ocfftDPP/+s8PBwbd68udYnBgBoWIyD8evvsJAkj8ejpUuXcvFBALhE/OZrerhcLo0ZM0YvvPBCbc4HANBAnddFoD788EM+yAcAlwjjU1I9e/Y8LQ4nTpxQZWWlUlJS6mRiAICGxTgY6enpp/3euHFjtWvXToGBgbU+KQBAw2McjBtuuEHSqUubf//992rVqhWXNQeAS4jxM35ZWZkmTZqkTp066aabblKnTp301FNPqbS0tC7nBwBoIIyDMXPmTJ04cUIZGRnavXu3MjIydOLECc2cObMu5wcAaCCMT0lt3bpVmzZtUuPGjSVJ7dq105///Gf17du3ziYHAGg4jI8wAgICVFxcfNqyH374Qf7+/rU+KQBAw2N8hHHHHXdoxIgReuCBB9S6dWsdOXJEq1at0p133lmX8wMANBDGwRg7dqzCw8OVkZGhwsJChYWFaeTIkQQDAC4RxqekZs2apXbt2mnVqlV66623tGrVKkVGRmrWrFl1OT8AQANhHIzMzEx17NjxtGUdO3ZUZmZmrU8KANDwGAfD4XDI6/Wetszj8ZyxDABwcTIORkxMjObPn+8LhNfr1cKFCxUTE1NnkwMANBzn9AVKDz/8sLp3767WrVsrPz9foaGhWrZsWV3ODwDQQBgHIyIiQq+//rp2796t/Px8ud1uderUietJAcAlwjgYkuR0OtW5c2d17ty5ruYDAGigODwAABghGAAAIwQDAGDknF7DOB9/+tOflJeXJ6fTqSZNmmjq1KmKiorS/v37lZSUpJKSEgUFBSk1NVVt27aVpBrHAAD2su0IIzU1VW+88YY2bNigESNG6Omnn5YkpaSkKCEhQVlZWUpISFBycrJvnZrGAAD2si0YzZo18/1cVlYmh8OhoqIi5ebmKj4+XpIUHx+v3NxcFRcX1zgGALCfbaekpFMf/vvwww9lWZZeeOEF5efnKzw8XC6XS5LkcrkUFham/Px8WZZV7VhwcLDxNkNCAuvkseDSFhra7Ow3AupJXe2ftgbjlyvbbtiwQWlpaUpMTKzzbRYVlcnrtX7TujwpoDpHj9b/d9mzf6I657N/Op2Oav/Qrpd3SQ0ePFg7duxQRESECgoK5PF4JJ26mGFhYaHcbrfcbne1YwAA+9kSjOPHjys/P9/3e3Z2tlq0aKGQkBBFRUX5LpGemZmpqKgoBQcH1zgGALCfLaekTpw4ocTERJ04cUJOp1MtWrTQsmXL5HA4NG3aNCUlJWnJkiVq3ry5UlNTfevVNAYAsJctwWjVqpVeeeWVKsciIyO1fv36cx4DANiLT3oDAIwQDACAEYIBADBCMAAARggGAMAIwQAAGCEYAAAjBAMAYIRgAACMEAwAgBGCAQAwQjAAAEYIBgDACMEAABghGAAAIwQDAGCEYAAAjBAMAIARggEAMEIwAABGCAYAwAjBAAAYIRgAACMEAwBghGAAAIwQDACAEYIBADBCMAAARggGAMAIwQAAGCEYAAAjBAMAYIRgAACMEAwAgBGCAQAwQjAAAEZsCcYPP/ygUaNGKS4uTrfddpvGjRun4uJiSdKuXbs0cOBAxcXFacSIESoqKvKtV9MYAMBetgTD4XBo5MiRysrKUkZGhq644grNnTtXXq9XEydOVHJysrKyshQTE6O5c+dKUo1jAAD72RKMoKAgdevWzfd7586ddeTIEe3Zs0cBAQGKiYmRJA0bNkxvv/22JNU4BgCwn+2vYXi9Xv39739XbGys8vPz1bp1a99YcHCwvF6vSkpKahwDANjPz+4NzpgxQ02aNNHw4cP17rvv1vn2QkIC63wbuPSEhjar7ykA1aqr/dPWYKSmpurgwYNatmyZnE6n3G63jhw54hsvLi6W0+lUUFBQjWPnoqioTF6v9Zvmy5MCqnP0aGl9T4H9E9U6n/3T6XRU+4e2baek5s2bpz179mjx4sXy9/eXJHXs2FHl5eXauXOnJGndunXq16/fWccAAPaz5Qjj66+/1vPPP6+2bdtq2LBhkqTLL79cixcvVlpamlJSUlRRUaE2bdooPT1dkuR0OqsdAwDYz5Zg/O53v9OXX35Z5ViXLl2UkZFxzmMAAHvxSW8AgBGCAQAwQjAAAEYIBgDACMEAABghGAAAIwQDAGCEYAAAjBAMAIARggEAMEIwAABGCAYAwAjBAAAYIRgAACMEAwBghGAAAIwQDACAEYIBADBCMAAARggGAMAIwQAAGCEYAAAjBAMAYIRgAACMEAwAgBGCAQAwQjAAAEYIBgDACMEAABghGAAAIwQDAGCEYAAAjBAMAIARggEAMEIwAABGCAYAwAjBAAAYsSUYqampio2NVYcOHfTVV1/5lu/fv19Dhw5VXFychg4dqgMHDhiNAQDsZ0swevfurTVr1qhNmzanLU9JSVFCQoKysrKUkJCg5ORkozEAgP1sCUZMTIzcbvdpy4qKipSbm6v4+HhJUnx8vHJzc1VcXFzjGACgfvjV14bz8/MVHh4ul8slSXK5XAoLC1N+fr4sy6p2LDg4uL6mDACXtHoLhl1CQgLrewq4CIWGNqvvKQDVqqv9s96C4Xa7VVBQII/HI5fLJY/Ho8LCQrndblmWVe3YuSoqKpPXa/2mOfKkgOocPVpa31Ng/0S1zmf/dDod1f6hXW9vqw0JCVFUVJQyMzMlSZmZmYqKilJwcHCNYwCA+mHLEcbMmTP1zjvv6Pvvv9eDDz6ooKAgvfnmm5o2bZqSkpK0ZMkSNW/eXKmpqb51ahoDANjPlmBMmTJFU6ZMOWN5ZGSk1q9fX+U6NY0BAOzHJ70BAEYIBgDACMEAABghGAAAIwQDAGCEYAAAjBAMAIARggEAMEIwAABGCAYAwAjBAAAYIRgAACMEAwBghGAAAIwQDACAEYIBADBCMAAARggGAMAIwQAAGCEYAAAjBAMAYIRgAACMEAwAgBGCAQAwQjAAAEYIBgDACMEAABghGAAAIwQDAGCEYAAAjBAMAIARggEAMEIwAABGCAYAwAjBAAAYIRgAACMEAwBgpMEHY//+/Ro6dKji4uI0dOhQHThwoL6nBACXpAYfjJSUFCUkJCgrK0sJCQlKTk6u7ykBwCWpQQejqKhIubm5io+PlyTFx8crNzdXxcXF9TwzALj0+NX3BGqSn5+v8PBwuVwuSZLL5VJYWJjy8/MVHBxsdB9Op+O85tCqZdPzWh8Xp/Pdr2qLf/OQ+p4CGqDz2T9rWrdBB6M2tDzPJ/wF/29wLc0EF5OQkMD6noIk6X/HpNb3FNAA1dX+2aBPSbndbhUUFMjj8UiSPB6PCgsL5Xa763lmAHDpadDBCAkJUVRUlDIzMyVJmZmZioqKMj4dBQCoPQ7Lsqz6nkRN9u3bp6SkJP34449q3ry5UlNT1b59+/qeFgBcchp8MAAADUODPiUFAGg4CAYAwAjBAAAYIRgAACMEAwBghGBcAjZt2qT+/ftr8ODB+vbbb+t0W0lJSXr55ZfrdBvA2SxcuFCpqXwKvrZd9JcGgbRu3To99thj6t+/f31PBcAFjGBc5GbPnq1///vf2r9/v9auXasJEyZo7ty5On78uCTpscce080336y8vDwNGTJEd911l7Zu3ary8nLNnTtX69at02effaZGjRppyZIlCg0N1ZdffqlnnnlGJ06cUEVFhe666y498MADZ2y7srJSzz77rD755BNVVlaqQ4cOmjZtmpo25YKOqF6HDh30+OOPa9OmTSopKdHMmTO1bds2bd26VSdPntT8+fMVGRmpo0ePavz48Tp+/LgqKirUs2dPTZo0qcr7XL58ud555x15PB6Fh4drxowZCg0NtfmRXQQsXPSGDx9uZWdnW8eOHbMGDRpkFRQUWJZlWQUFBVaPHj2sY8eOWYcOHbKuuuoq67333rMsy7JWrFhhde3a1crNzbUsy7JSUlKsefPmWZZlWaWlpVZFRYVlWZZVVlZm9e/f3/rmm28sy7Ksp556ylq9erVlWZa1ePFia/Hixb55pKWl+e4DqM5VV11lvfzyy5ZlWdZbb71lde7c2crOzrYsy7KWL19uPfnkk5ZlWVZ5eblVVlZmWZZlVVZWWvfee6+1ZcsWy7Isa8GCBdacOXMsy7KsDRs2WFOmTLE8Ho9lWZa1Zs0aa/z48bY+posFRxiXkJycHOXl5WnUqFG+ZQ6HQwcPHlTLli3VpEkT3XzzzZKka6+9VhEREYqKivL9vm3bNklSeXm5pk2bpi+//FIOh0OFhYXau3evIiMjT9tedna2ysrKlJWVJenUEcfVV19twyPFhe6X06fXXnutJKlXr16SpI4dO+rdd9+VdOpipGlpacrJyZFlWfr++++1d+9e3XTTTafdV3Z2tvbs2aM//vGPvvUCAxvG1YYvNATjEmJZljp06KA1a9acMZaXlyd/f3/f706n87TfXS6X76rB8+bNU2hoqObMmSM/Pz+NGDFCFRUVVW4vJSVFN954Yx08GlzMAgICJJ25HzqdTp08eVKS9NJLL+nHH3/U+vXrFRAQoKlTp1a7H44dO1Z33HGHPZO/iPEuqUtIdHS0Dh48qI8++si3bPfu3bLO8XJipaWlioiIkJ+fn7766ivt3LmzytvFxsZq1apVKi8vlySVlZVp3759v/0BAL9SWlqq0NBQBQQEqKCgQJs3b67ydrGxsVq7dq2OHTsm6dSR7t69e+2c6kWDI4xLSIsWLbRkyRKlp6dr9uzZ+vnnn3XFFVdo2bJl53Q/Y8eO1aRJk/Tqq6+qXbt2uv7666u83ejRo7Vo0SLdcccdcjgccjgcGjdu3BmnroDf4t5771ViYqLi4+MVHh5e7ZHs4MGDVVJSouHDh0s6dcRx9913c3r0N+BqtQAAI5ySAgAYIRgAACMEAwBghGAAAIwQDACAEYIBADBCMAAARggGAMAIwQBq2fLly9WjRw9FR0crLi5O27dvl9fr1fLly9WnTx9169ZNiYmJKikpkSSlpKTo0Ucf9a2fnp6u+++//5wv2QLUNT7pDdSib7/9Vg8++KBeeeUVhYeHKy8vT16vV43/1aMAAAHESURBVO+9957efPNNLViwQMHBwZo5c6bKyso0b948nThxQoMHD9bDDz+s//mf/9EjjzyijRs3KiIior4fDnAariUF1CKXy6XKykrt27dPwcHBuvzyyyWd+tbD5ORkXwTGjRunXr166eTJk2rcuLHS0tI0atQoNW3aVFOnTiUWaJA4wgBqWUZGhtauXatvvvlG3bt3V1JSkm655Ra5XC45nf93FriiokKbNm1SeHi4JOn2229XcXGxNm/eLJfLVV/TB6pFMIA6UlZWpuTkZLlcLu3evVuzZ89W165dq7ztmjVrtG7dOjVu3Fi9e/fWww8/bPNsgbPjRW+gFn377bfavn27Kisr5e/vr4CAADmdTt1999167rnndPjwYUlScXGxNm3aJEnav3+/nnvuOaWnpystLU0vvPCCvvjii/p8GECVeA0DqEWVlZX6y1/+on379umyyy5TdHS0pk+frtDQUFmWpREjRqiwsFAhISG69dZbdfPNN2vixIkaNWqU7/sZnnjiCU2aNEmvvfbaad82B9Q3TkkBAIxwSgoAYIRgAACMEAwAgBGCAQAwQjAAAEYIBgDACMEAABghGAAAIwQDAGDk/wOd3q0B/JjM4wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zV5Jx_ytVmIy",
        "outputId": "e13401fc-4019-4588-e37b-0940d0e942f3"
      },
      "source": [
        "insurance_dataset['sex'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "male      676\n",
              "female    662\n",
              "Name: sex, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "id": "bqUZ1u_9Vyg_",
        "outputId": "ba4da98b-e778-4670-9fdd-c0c5a1c12da1"
      },
      "source": [
        "# bmi distribution\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.distplot(insurance_dataset['bmi'])\n",
        "plt.title('BMI Distribution')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0wAqPe_vWJ23"
      },
      "source": [
        "Normal BMI Range --> 18.5 to 24.9"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "xNwyB3IzWAsU",
        "outputId": "0fa5aa44-0d3f-4fd4-da81-025fdb05f1f8"
      },
      "source": [
        "# children column\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.countplot(x='children', data=insurance_dataset)\n",
        "plt.title('Children')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4TMelPK-Wx5x",
        "outputId": "5bf9fd62-c692-4eac-e82b-0a095949ee59"
      },
      "source": [
        "insurance_dataset['children'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    574\n",
              "1    324\n",
              "2    240\n",
              "3    157\n",
              "4     25\n",
              "5     18\n",
              "Name: children, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "poIPFFSBW9n4",
        "outputId": "ffbd4e83-65cc-40c2-919f-241cf71ac844"
      },
      "source": [
        "# smoker column\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.countplot(x='smoker', data=insurance_dataset)\n",
        "plt.title('smoker')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2OHFYb59XI2h",
        "outputId": "2bf04042-a6c2-4913-9ad9-9d2c8598a680"
      },
      "source": [
        "insurance_dataset['smoker'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "no     1064\n",
              "yes     274\n",
              "Name: smoker, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "5972T2X4XRkv",
        "outputId": "65e4bcc9-0fe5-4564-8f87-f5e1d4e890a6"
      },
      "source": [
        "# region column\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.countplot(x='region', data=insurance_dataset)\n",
        "plt.title('region')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rkckkdYVXYnV",
        "outputId": "c47a8e1b-c524-4e65-8ecc-2ac060e5c393"
      },
      "source": [
        "insurance_dataset['region'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "southeast    364\n",
              "northwest    325\n",
              "southwest    325\n",
              "northeast    324\n",
              "Name: region, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "id": "vV_nE8lNXgji",
        "outputId": "f1260601-76ff-4183-f476-d5e4a67000b0"
      },
      "source": [
        "# distribution of charges value\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.distplot(insurance_dataset['charges'])\n",
        "plt.title('Charges Distribution')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gKYZDvzqX3iR"
      },
      "source": [
        "Data Pre-Processing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pr9nJq15YFsY"
      },
      "source": [
        "Encoding the categorical features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QVXYBAt_XwPO"
      },
      "source": [
        "# encoding sex column\n",
        "insurance_dataset.replace({'sex':{'male':0,'female':1}}, inplace=True)\n",
        "\n",
        "3 # encoding 'smoker' column\n",
        "insurance_dataset.replace({'smoker':{'yes':0,'no':1}}, inplace=True)\n",
        "\n",
        "# encoding 'region' column\n",
        "insurance_dataset.replace({'region':{'southeast':0,'southwest':1,'northeast':2,'northwest':3}}, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EL0T11t-ZdNF"
      },
      "source": [
        "Splitting the Features and Target"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z5UK60q_ZMgr"
      },
      "source": [
        "X = insurance_dataset.drop(columns='charges', axis=1)\n",
        "Y = insurance_dataset['charges']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ACFgPgoUZvzP",
        "outputId": "af4b5262-090a-49cc-8c40-b37a4a228d94"
      },
      "source": [
        "print(X)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "      age  sex     bmi  children  smoker  region\n",
            "0      19    1  27.900         0       0       1\n",
            "1      18    0  33.770         1       1       0\n",
            "2      28    0  33.000         3       1       0\n",
            "3      33    0  22.705         0       1       3\n",
            "4      32    0  28.880         0       1       3\n",
            "...   ...  ...     ...       ...     ...     ...\n",
            "1333   50    0  30.970         3       1       3\n",
            "1334   18    1  31.920         0       1       2\n",
            "1335   18    1  36.850         0       1       0\n",
            "1336   21    1  25.800         0       1       1\n",
            "1337   61    1  29.070         0       0       3\n",
            "\n",
            "[1338 rows x 6 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7zcw-kgaZxvI",
        "outputId": "f3c875bd-194d-4efd-ee54-b5beeb57e630"
      },
      "source": [
        "print(Y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0       16884.92400\n",
            "1        1725.55230\n",
            "2        4449.46200\n",
            "3       21984.47061\n",
            "4        3866.85520\n",
            "           ...     \n",
            "1333    10600.54830\n",
            "1334     2205.98080\n",
            "1335     1629.83350\n",
            "1336     2007.94500\n",
            "1337    29141.36030\n",
            "Name: charges, Length: 1338, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N56oAuY3bQSF"
      },
      "source": [
        "Splitting the data into Training data & Testing Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8HQEpONYbL0-"
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GKh0p46hb3iD",
        "outputId": "125bef72-9197-4329-bcd6-e35baeeb87da"
      },
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1338, 6) (1070, 6) (268, 6)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DQoIaogAcCF2"
      },
      "source": [
        "Model Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-1RWRMnncEJj"
      },
      "source": [
        "Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "18wYy3bub9ej"
      },
      "source": [
        "# loading the Linear Regression model\n",
        "regressor = LinearRegression()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yRHiG9dqcxxN",
        "outputId": "1a7128e1-d38c-429b-96cc-cc0eb106b6ce"
      },
      "source": [
        "regressor.fit(X_train, Y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PGM93AzWc-VJ"
      },
      "source": [
        "Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NhRL9x_wc6-p"
      },
      "source": [
        "# prediction on training data\n",
        "training_data_prediction =regressor.predict(X_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bQ8gnyDMdMSb",
        "outputId": "a0291fed-6536-480c-9be1-fb01b70f65aa"
      },
      "source": [
        "# R squared value\n",
        "r2_train = metrics.r2_score(Y_train, training_data_prediction)\n",
        "print('R squared vale : ', r2_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R squared vale :  0.751505643411174\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pHQSjvekdsgO"
      },
      "source": [
        "# prediction on test data\n",
        "test_data_prediction =regressor.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YWPIzD8ud5KA",
        "outputId": "aaec9420-2c96-45f9-ad1a-9f9601a35ca9"
      },
      "source": [
        "# R squared value\n",
        "r2_test = metrics.r2_score(Y_test, test_data_prediction)\n",
        "print('R squared vale : ', r2_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R squared vale :  0.7447273869684077\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_RhHS3AkeOVA"
      },
      "source": [
        "Building a Predictive System"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H9bGdPbBd_Xd",
        "outputId": "ee59e2e7-c2b3-4b17-8fe8-e770dcd712c3"
      },
      "source": [
        "input_data = (31,1,25.74,0,1,0)\n",
        "\n",
        "# changing input_data to a numpy array\n",
        "input_data_as_numpy_array = np.asarray(input_data)\n",
        "\n",
        "# reshape the array\n",
        "input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n",
        "\n",
        "prediction = regressor.predict(input_data_reshaped)\n",
        "print(prediction)\n",
        "\n",
        "print('The insurance cost is USD ', prediction[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[3760.0805765]\n",
            "The insurance cost is USD  3760.0805764960587\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}