health_insurance.ipynb

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        "### Data has always been a big part of healthcare and insurance industries. To establish a better health care system, there is a need to estimate the cost of health insurance. The prediction of the cost is one possibility to improve health care development. Big data analytics has potential for benefit for everyone in the value chain Provider, Payer and the Patient."
      ]
    },
    {
      "cell_type": "markdown",
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        "id": "CED59Vf9jmwP"
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        "### The given data had a few missing values and outliers. This data shows a strong relationship between BMI, age and the cost of health insurance. In this project I have used Linear Regression, Support Vector Machine, Random Forest.\n",
        "\n",
        "\n",
        "---\n",
        "\n"
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              "      <th>health_insurance_price</th>\n",
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              "    age  gender     BMI  Children smoking_status   location  \\\n",
              "0  19.0  female     NaN       0.0            yes  southwest   \n",
              "1  18.0    male  33.770       1.0             no  southeast   \n",
              "2  28.0    male  33.000       3.0             no  southeast   \n",
              "3  33.0    male  22.705       0.0             no  northwest   \n",
              "4  32.0    male  28.880       0.0             no  northwest   \n",
              "\n",
              "   health_insurance_price  \n",
              "0             16884.92400  \n",
              "1              1725.55230  \n",
              "2              4449.46200  \n",
              "3             21984.47061  \n",
              "4              3866.85520  "
            ]
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          "execution_count": 107,
          "metadata": {},
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      ],
      "source": [
        "#importing important libraries\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import scipy\n",
        "from scipy import stats\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn import metrics\n",
        "df = pd.read_excel(\"/content/Health_insurance_cost.xlsx\")#reading the dataframe\n",
        "df.head() #viewing the first five rows and columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZBSfBPhztyxd"
      },
      "source": [
        "### Gathering some very basic information about given data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
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        "id": "sIfVJozBbei4",
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1338, 7)"
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        "df.shape"
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              "      <th>25%</th>\n",
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            "text/plain": [
              "               age          BMI     Children  health_insurance_price\n",
              "count  1310.000000  1315.000000  1338.000000             1336.000000\n",
              "mean     39.166412    30.638217     1.094918            13268.527719\n",
              "std      14.055378     6.110302     1.205493            12112.797724\n",
              "min      18.000000    15.960000     0.000000             1121.873900\n",
              "25%      26.000000    26.210000     0.000000             4744.325050\n",
              "50%      39.000000    30.305000     1.000000             9382.033000\n",
              "75%      51.000000    34.580000     2.000000            16604.302645\n",
              "max      64.000000    53.130000     5.000000            63770.428010"
            ]
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          "execution_count": 109,
          "metadata": {},
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      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "metadata": {
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        },
        "id": "vOJKFd6aG7k1",
        "outputId": "0c723446-4277-4502-db26-60ccef084a5c"
      },
      "outputs": [
        {
          "name": "stdout",
          "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                     1310 non-null   float64\n",
            " 1   gender                  1338 non-null   object \n",
            " 2   BMI                     1315 non-null   float64\n",
            " 3   Children                1338 non-null   float64\n",
            " 4   smoking_status          1338 non-null   object \n",
            " 5   location                1338 non-null   object \n",
            " 6   health_insurance_price  1336 non-null   float64\n",
            "dtypes: float64(4), object(3)\n",
            "memory usage: 73.3+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
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    {
      "cell_type": "code",
      "execution_count": 115,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7LpQSdQtwCAi",
        "outputId": "6fbca0b1-9569-4421-8e2a-f867c72b5837"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "age                       0\n",
              "gender                    0\n",
              "BMI                       0\n",
              "Children                  0\n",
              "smoking_status            0\n",
              "location                  0\n",
              "health_insurance_price    0\n",
              "dtype: int64"
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          },
          "execution_count": 115,
          "metadata": {},
          "output_type": "execute_result"
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      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Dt4yLlzr0aH"
      },
      "source": [
        "### **DATA** **CLEANING** : Dropping the duplicates and imputing the missing values using MICE imputer. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AuFKfxG4vAtP"
      },
      "source": [
        "### We need to convert categorical data like gender, smoking status and location into numerical data before replcaing the missing data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "metadata": {
        "id": "7BkVqUyfhPuO"
      },
      "outputs": [],
      "source": [
        "df['gender']= df.gender.map({'male': 0, 'female': 1})\n",
        "df['smoking_status']= df.smoking_status.map({'yes': 0, 'no': 1})\n",
        "df['location']= df.location.map({'southwest': 0, 'southeast': 1,'northwest' : 2, 'northeast': 3})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1SrWcx0rvqLb"
      },
      "source": [
        "### MICE algorithm imputes missing values in a dataset by looking at data from other columns and trying to estimate the best prediction for each missing value."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F8PLgYNHelJK",
        "outputId": "d8652c12-6bbb-4d2a-b34d-3acaf77b2f2d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: fancyimpute in /usr/local/lib/python3.8/dist-packages (0.7.0)\n",
            "Requirement already satisfied: cvxopt in /usr/local/lib/python3.8/dist-packages (from fancyimpute) (1.3.0)\n",
            "Requirement already satisfied: cvxpy in /usr/local/lib/python3.8/dist-packages (from fancyimpute) (1.2.3)\n",
            "Requirement already satisfied: pytest in /usr/local/lib/python3.8/dist-packages (from fancyimpute) (3.6.4)\n",
            "Requirement already satisfied: scikit-learn>=0.24.2 in /usr/local/lib/python3.8/dist-packages (from fancyimpute) (1.0.2)\n",
            "Requirement already satisfied: nose in /usr/local/lib/python3.8/dist-packages (from fancyimpute) (1.3.7)\n",
            "Requirement already satisfied: knnimpute>=0.1.0 in /usr/local/lib/python3.8/dist-packages (from fancyimpute) (0.1.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.8/dist-packages (from knnimpute>=0.1.0->fancyimpute) (1.15.0)\n",
            "Requirement already satisfied: numpy>=1.10 in /usr/local/lib/python3.8/dist-packages (from knnimpute>=0.1.0->fancyimpute) (1.21.6)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.24.2->fancyimpute) (3.1.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.24.2->fancyimpute) (1.2.0)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.24.2->fancyimpute) (1.7.3)\n",
            "Requirement already satisfied: scs>=1.1.6 in /usr/local/lib/python3.8/dist-packages (from cvxpy->fancyimpute) (3.2.2)\n",
            "Requirement already satisfied: osqp>=0.4.1 in /usr/local/lib/python3.8/dist-packages (from cvxpy->fancyimpute) (0.6.2.post0)\n",
            "Requirement already satisfied: setuptools<=64.0.2 in /usr/local/lib/python3.8/dist-packages (from cvxpy->fancyimpute) (57.4.0)\n",
            "Requirement already satisfied: ecos>=2 in /usr/local/lib/python3.8/dist-packages (from cvxpy->fancyimpute) (2.0.12)\n",
            "Requirement already satisfied: pluggy<0.8,>=0.5 in /usr/local/lib/python3.8/dist-packages (from pytest->fancyimpute) (0.7.1)\n",
            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.8/dist-packages (from pytest->fancyimpute) (22.2.0)\n",
            "Requirement already satisfied: py>=1.5.0 in /usr/local/lib/python3.8/dist-packages (from pytest->fancyimpute) (1.11.0)\n",
            "Requirement already satisfied: atomicwrites>=1.0 in /usr/local/lib/python3.8/dist-packages (from pytest->fancyimpute) (1.4.1)\n",
            "Requirement already satisfied: more-itertools>=4.0.0 in /usr/local/lib/python3.8/dist-packages (from pytest->fancyimpute) (9.0.0)\n",
            "Requirement already satisfied: qdldl in /usr/local/lib/python3.8/dist-packages (from osqp>=0.4.1->cvxpy->fancyimpute) (0.1.5.post2)\n"
          ]
        }
      ],
      "source": [
        "!pip install fancyimpute\n",
        "from fancyimpute import IterativeImputer\n",
        "MICE_imputer = IterativeImputer()\n",
        "df_mice = df.copy(deep = True)\n",
        "df.iloc[:,:]=MICE_imputer.fit_transform(df_mice)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OUOKpKo-w24v"
      },
      "source": [
        "### We check for null values once again."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CQ9cgpISw0b4",
        "outputId": "7f3a697b-9afc-4e04-95de-161cfaebbd60"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "age                       0\n",
              "gender                    0\n",
              "BMI                       0\n",
              "Children                  0\n",
              "smoking_status            0\n",
              "location                  0\n",
              "health_insurance_price    0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 116,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The histogram given below is right skewed. This shows that the prices are not normally distributed."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 126,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "nhSTlYI6RN0J",
        "outputId": "e77fc98a-15f7-4aac-db3e-ad1e957238e6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.lines.Line2D at 0x7f1d3c9de550>"
            ]
          },
          "execution_count": 126,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.hist(df['health_insurance_price'])\n",
        "plt.axvline(x=df.BMI.mean(),color='red',alpha=0.5,label='Mean')\n",
        "plt.axvline(x=df.BMI.median(),c='blue',ls='--',alpha=0.5,label='Median')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 232,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "id": "me9-hxvNGzr2",
        "outputId": "0d07eb4f-c193-453d-8682-707e723b45e1"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: 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"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d3c7f6d00>"
            ]
          },
          "execution_count": 232,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df['price_log'] = np.log(df['health_insurance_price'])\n",
        "sns.distplot(df['price_log'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v6nMRnQ2xLlc"
      },
      "source": [
        "### **DATA** **VISUALIZATION** : "
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "f6s4sy8My3GI"
      },
      "source": [
        "As one can see BMI and age show a strong relationship towards health insurance price."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 233,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "id": "aazXjaIRp1p2",
        "outputId": "1cbd2821-4587-41f8-f214-1dc8a85e5b11"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d3d489640>"
            ]
          },
          "execution_count": 233,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 504x360 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.heatmap(df.corr(), annot=True, fmt='.1f')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "bqUZ1u_9Vyg_",
        "outputId": "6d19970b-f3f6-44c0-b837-7703f1caeaf2"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: 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"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# bmi distribution\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.distplot(df['BMI'])\n",
        "plt.title('BMI Distribution')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tA60sv-63EV-"
      },
      "source": [
        "Here we see histograms of all independent variables with dependent variable or target variable. Avoid the histograms of categorical features in this."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 234,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "etD2T7LI1MKl",
        "outputId": "093443e3-7713-42b8-b12b-6c23d951facf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['age', 'gender', 'BMI', 'Children', 'smoking_status', 'location', 'health_insurance_price', 'price_log']\n"
          ]
        },
        {
          "data": {
            "image/png": 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TMJMajMTOBoipP+3h1OwOwyKd6IxowTyvrpzv08LKYjnzdEkqkGmcgwEAgJkiLQHUxz72MbW2tmrp0qXp6G5WOt0dUXgwqmULAywnmQa7zap5frfmnb1yoGkmNTQSU99wTP1nQ4mjpwcmFv9YDYuKPA4Vue1nbx3yuu15u3/SXDYWi6tv6P1gqX/47O0HAojBSOxDC7vsNkMlZ2fDLagoVkmhUyVFTvm9qWN+r0tFHrvmlRfn7bJFfJjVsHwolHrv70jvYFTRWFy/+sMJNf0+dRXAK6p8WrqwVEsXBlRX45fbZZ+88wyaTXugYWbgHAwAAMwUaUlCKisrdeutt+q6665TaWnpOW0bN25Mx1PMaOPxhN440asSr1PzywtzXc6sYhgWFRc6VVzolM5uj/zecp7BSCy1x0wkpvbwiE52Dk88zm4zVFhgl8dlm7gil+fsfZZYpU/CNDUYSb0XA5HU8qrBSEwDw7GJpVapY1GNRj+8mbfHZUsts/Q6VV1WOBFIlHid8hU65S9yqaa6RD09w+d5dsw2LodNoYBNoYBHt31pmR78+avqHUotz+3sHdGRd4/q3587Kouk4kKHgr4CBYtdKilyZW1220zZAw2zB+dgAABgpkhLADU6OqrPf/7zisfjOnPmTDq6nFXefLdfsXFTn7jKz3KwLDjfcp5kMqmxWEKDIzENj6Q2QI6MxdUzMKbTZ6+69x6Xw6rCArsKnDb9P81vymWzqLTIpUCxS/4iV7ZfTl4xzaRicVOxeEKxcVOx8UTqTzz1dTRu6t2Hf6+evhENRFJj/eGdlqQCp21io+nLygtV5PGr2OOQ3+t6P2DyOi9qWRX/puYuu81QeYlb5SWpGZHxhKneoah6B8bUPTCmt08P6OjpAVkNiwJFLpX6XAr6ClTktvP3BrMG52AAAGCmSEsAdf/993/kxzY2Nqq5uVltbW1qampSbW2tJOn48ePasmWL+vv75fP51NjYqJqamgu25ZvUZrpDqpnnla/QeeEHICMsFosKnKnNpctLzm2LJ0xFRsc1PBZP3Z4Np7r7R7X32SPnXDbeIqmkyClvwfuXjU/dOlXsSe075Smwq9Blk9tll92Wf7OpksmkxhOmxiyuytwAACAASURBVOOm4nFz4uvxRDJ1/wPHPhguxcZTxydjs1rksFnlKbAr6CvQFZXFKvI4UjPUzoZN740RSyCRCTaroTJfgcp8BVqs1JUOwwNj6h4YVXf/mFpP9Enqk9NuqPTs7Kigr4Bl0ZjRpnMOBgAAkE1pOes+derUpG3V1dVTPnbt2rX6+te/rq9+9avnHG9oaNCGDRt00003ad++fdq2bZt+9rOfXbAt37Se6JXdZmjxfF+uS8EkbFbjA8v4zvW1v1iivqGouvpG1N03oq6+UfX0j57dh2ZMp7uH1T8U1XkuqCZJcp6dTeV1O+R22eRy2OR0WFN/7Gc3wHZYZbcbMiwWWQ1DhmGR1bDIarXIsFhksaQ2ZDbNpBJmUolEUmbyjN461a9kMhUomcnUcrf32s/9Oql4wpy4jScmKfYDrIZFdpshp90qh92Q2+mUw27IYUvdd9itcthSt06bIbvdOrHE6bYvLVN399C03hMgHew245yLGIxGU8Fyd/+YuvtH1XZ29mNhgT0VXJUUKFDslJXNnDGDTOccDAAAIJvSEkBdd911slgsSn5gqsh7yxsOHz485WPr6+s/dCwcDqu1tVWPPPKIJOmGG27Qvffeq97eXiWTyUnb/H5/Ol5O2nT1pX7RWVJTwoyPGcrltGn/r94451ipr0AFdkMV/tTlrpPJpKLjpqKxuKLjZ2cOxROpmURxU7G4qbHouIYisXOCobhpTgRE02WxpEIjq2HIZn0vwDImgiSr1SarYchuNWSzpY7ZrcbErc127tcGy5MwCxU4bZpf7tX8cq+SyaQGR8bPBlKjOnFmUO90DMpqWFRa7FJ5SYHKStxyu5gdhfw2nXMwAACAbErLmfWbb755zv3u7m796Ec/Om+4dDE6OjpUXl4uqzUV2litVpWVlamjo0PJZHLStnwKoJLJpN440Su306aaUFGuy0EGWSwWuRxWuRwfLWRMJpMTM5mSH7hvnj2Weo7U81gskiGLvvFX1+j/2vf6xHEAl8ZisUwsC72isljxRGq5XmffqLr6RtXZNyqpV163XWUlBSovKZDf65KRpc3MgYuV7nMwAACATMnIR7vBYFB33323rr/+et14442ZeIq0CAQyd0W6roGohkbG9elrQiryTn/jao8nPftHpaufTMjX2s5XV65rddqt8qbh71WmBIPevHy+XL9vU8nX2j5qXdl4Pel+juKiAi2oLlEymdTQSEzt3RG190R0vH1Qx9oGZbOmlvRVlHpUESycdO+obP/9vxj5WBMyY6acgwEAgLknY2sL3nnnHY2Ojn6kx4ZCIXV2diqRSMhqtSqRSKirq0uhUEjJZHLStksVDg/LTMPypz/lK3HrtaPd8hU65C90KBKJTqs/j8c57T7ek65+MiFfa/vTutL5fkxHPtQwmWzuARUMei/q+YJBb16PWb7W9lHqyta/kUw+h1VSddCj6qBH8bip7oH3Z0ad7hqW1Cl/kVPz/G6F/G55CuySpETClNWaf3tIxcYTGugfyXUZWWUYlox+0JTPpnMOBgAAkClpCaA2bNhwzjKg0dFRvf3227rjjjs+Un+BQEB1dXXav3+/brrpJu3fv191dXUTS+ymassHzX88qdFYQssXlbI8CgBmOJvNUCjgUSjgSe0dFYnpTO+oOnpH1HqiT60n+uR12xXyu3W8Y1DPvXAir/7v93ic2nD94lyXgQxJ9zkYAABApqQlgPrKV75yzv2CggItXrxYNTU1F3zsfffdp2eeeUY9PT265ZZb5PP59Ktf/Ur33HOPtmzZogcffFBFRUVqbGyceMxUbbkWHU/o579+S4Eip0qL83eJFADg0lkslomrZl4536fI2LjO9I7oTHhEb50e0N/9j/+Ux2VTZalHlUGPvG5HrkvGLDedczAAAIBsSksA9Vd/9Vcf+bFbt27V1q1bP3R84cKFevzxx8/7mKnacu03r7SpbyiqTy+dl1efgAMA0s/jsmthRbEWVhQrOp5QbU1AjzW/qbdOD+it0wMqcttVGfSootQjj8ue63IxC03nHAwAACCb0hJAjY+P68c//rH27dunrq4ulZWV6aabbtK3vvUtORxz69Pf37x6WtfWBhVg9hMAzClOu1X/26rLdLKtX2OxuDrCIzrdHdHhk/06fLJfJYUOVZd7VVnqkd2Wf/tEYWbiHAwAAMwUaQmgdu3apddee03f+973VFFRofb2dj344IMaHh7Wd7/73XQ8xYzx375Qq/olIf37r4/kuhQAQI64HDZdHirS5aEijYzF1d4T0anuYb12LKw3jvcqFHBrfnmhAkUuZstiWjgHAwAAM0VaAqinn35a+/btU0lJiSRpwYIFuuqqq3TTTTfNuZOf5VeUqqSI2U8AgBS3y6Yrqoq1sLJI/cMxvds1rLbuYZ3ujsjjSgVV1WWFzIrCR8I5GAAAmCnSEkAlk8lLOg4AwFxjsVhU4nWqxOvUkpoSdYRHdOLMkFqO9+rNd/tUXVaoy0NFKixgryhcPM7BAADATJGWAGrdunW6/fbbdccdd6iiokJtbW368Y9/rHXr1qWjewAAZhWb1VB1WaGqywrVNxTV8Y5BnTgzpOMdQ5rnd6u2qlg+rzPXZWIG4BwMAADMFGkJoDZv3qwf//jH2r59u7q6ulReXq6/+Iu/0O23356O7gEAmLVSs6KCuqqmRCc6UiHUf/WOKOhzqbbKx0UtMCXOwQAAwEwxrQ0nXn75Ze3atUsOh0MbN27Us88+q0OHDumZZ55RLBZTa2truuoEAGBWczlsWnxZib5QX6W6y0o0EInp+ZYzev71DvUOjuW6POQZzsEAAMBMM60A6uGHH9bHPvax87atWrVKDz300HS6BwBgzrHbDC2qKtYXVlZp6eV+DY+O63evn9FLb3ZpeHQ81+UhT3AOBgAAZpppBVCHDx/W6tWrz9v2qU99Si0tLdPpHgCAOctmNbSgokhrV1bpymqfuvpG9ZtX2/TasbCisUSuy0OOcQ4GAABmmmntATU8PKzx8XFZrdYPtcXjcUUikel0DwDAnGezGrpyvk+XzfPqrVP9OnlmSG3dEV1VU6L55YWyWCwX7CORMBUMerNQ7aWLjSc00D+S6zJmHM7BAADATDOtAGrBggX63e9+py984Qsfavvd736nBQsWTKd7AABwlsth1TULA7o85NVrx8I6dCysU13DumZhQEUex5SPtVoN/eSXh7JU6aW57UvLcl3CjMQ5GAAAmGmmtQTvr//6r9XQ0KBnnnlGpmlKkkzT1DPPPKN77rlHt9xyS1qKBAAAKV63Q59aOk/LrwhoeHRc/3moXa0n+pRImLkuDVnEORgAAJhppjUD6sYbb1RPT4++853vaHx8XD6fT/39/bLb7frv//2/64YbbkhXnQAA4CyLxaL55V6V+91qPdGnt9sG1Nk3opW1wQvOhsLswDkYAACYaaYVQEnSLbfcoq985St69dVX1d/fL5/Pp2uvvVaFhYXpqA8AAEzCabfq2kWlqix169WjPfqvQ+26qsavy0Pei9obCjNbOs7B/vZv/1anT5+WYRhyu936h3/4B9XV1en48ePasmXLRL+NjY2qqamRpCnbAAAAJjPtAEqSCgsLJ70SCwAAyKyyErf+7NpKHTzao5bjversG9G1i0rlcqTlxzzy2HTPwRobG+X1pjao//Wvf63vfve7euKJJ9TQ0KANGzbopptu0r59+7Rt2zb97Gc/k6Qp2wAAACYzrT2gAABAfnDarfp4XZmuWeBX72BUvz3YrvDAWK7LQp57L3ySUlfWs1gsCofDam1tnVjGd8MNN6i1tVW9vb1TtgEAAEyFj0YBAJglLBaLakJFChS79OLhLv3+jTOKJkwlk8lcl4Y8dvfdd+v5559XMpnUv/zLv6ijo0Pl5eWyWq2SJKvVqrKyMnV0dCiZTE7a5vf7L+r5AoHMbdPg8Tgz1vd0BYPeC3/TDDRbX1e+Yryzi/HOrtk63vxseh8BFAAAs4zX7dBnl4X08ls9OnC4S3v+/ZBsFslqsC8UPmzHjh2SpCeffFI7d+7Uxo0bM/p84fCwTDP9oWgw6FUkEk17v+nS3T2U6xLSLhj0zsrXla8Y7+xivLNrto73XPzZZBiWST9sYgkeAACzkN1m1aq6Ml11uV/Nfzyp37ec0VgsnuuykMf+8i//Ui+88ILmzZunzs5OJRIJSVIikVBXV5dCoZBCodCkbQAAAFMhgAIAYJayWCxatiio73y9XoORmH732hkNj47nuizkiUgkoo6Ojon7zz33nIqLixUIBFRXV6f9+/dLkvbv36+6ujr5/f4p2wAAAKbCEjwAAGa5zyyr1B8OtumPrZ363esd+sRV5fIV5u9+BMiO0dFRbdy4UaOjozIMQ8XFxXrooYdksVh0zz33aMuWLXrwwQdVVFSkxsbGicdN1QYAADAZAigAAOYAn9epz1wT0h/eOKPnXz+jj9eVKegryHVZyKHS0lL9/Oc/P2/bwoUL9fjjj19yGwAAwGQIoACkVSJhZv1qCrP1ihlAuhUW2PWZq0P6Y2unXmjt1IraoCpKPbkuCwAAAHMAARSAtLJaDf3kl4ey9nwej/Oirixx25eWZaEaIP8VOG369NXz9GJrlw4c6da1ZlLVZee/UgkAAACQLmxCDgDAHOOwWfWJJeUqLXbp1aM9Ot09nOuSAAAAMMsRQAEAMAfZrIY+XlemQJFTr77Vo/aeSK5LAgAAwCyW10vwTp8+rTvuuGPi/tDQkIaHh/Xiiy9qzZo1cjgccjpTV/HZtGmTVq9enatSAQCYcWxWQ6uuKtcf3+jUy291y2KRQgH2hAIAAED65XUAVVVVpX379k3c37FjhxKJxMT9Bx54QLW1tbkoDQCAWWEihGrt1IEj3frYYovm+d25LgsAAACzzIxZgheLxdTU1KQvf/nLuS4FAIBZxW4z9ImrylXscejAkW6FB8dyXRIAAABmmRkTQD333HMqLy/XkiVLJo5t2rRJN954o+655x4NDg7msDoAAGY2uy01E6rAYdWLh7s0NBLLdUkAAACYRfJ6Cd4H/eIXvzhn9tOjjz6qUCikWCymHTt2aPv27dq9e/cl9RkIZO6y0x6PMy/7S3dd6ZSvtZ2vrnyoNR9qmEy2a7vY52PMLt1HrSsbr2e2jVmmXUxdHklr6qv1zIvv6sXD3bpu1XwVODN/qhAMejP+HAAAAMitGRFAdXZ26qWXXtLOnTsnjoVCIUmSw+HQhg0bdPvtt19yv+HwsEwzmbY63xMMehWJRNPWn8fjTFt/6awr3fK1tj+tK53vx3TkQw2TyWZtl/J+MGaX7qPUla1/I7NpzDLpveDpYuuySFq1uEzPt5zRbw6c0qeXzpPNltkJ093dQ2nv0zAsGf2gCQAAAJdmRizBe+KJJ/S5z31OJSUlkqSRkRENDaVOVpPJpJ566inV1dXlskQAAGYNn9ep+iuDGozE9NKRrox8WAMAAIC5ZUbMgHriiSd09913T9wPh8O68847lUgkZJqmFi5cqIaGhhxWCADA7FLud+uaKwI69HZYr78T1rIrSnNdEgAAAGawGRFANTc3n3O/urpaTz75ZI6qAQBgbris3KvIaFxvtw2o2ONQTago1yUBAABghpoRS/AAAEBu1F3mU1lJgV4/3qvwwFiuywEAAMAMRQAFAAAmZbFYtLK2VG6nTS8d6dJINJ7rkgAAADADEUABAIAp2W1WfbyuTKaZ1EuHuxRPmLkuCQAAADMMARQAALggr9uhFbVBDURiOvR2WMkkV8YDAADAxSOAAgAAF2We363F831q64noxJmhXJcDAACAGYQACgAAXLRFVcUqLylQy/Fe9Q9Fc10OAAAAZggCKAAAcNEsFouuXVQql92qA0e6NR5P5LokAAAAzAAEUAAA4JI47FatvDKo0Vhcrx5lPygAAABcGAEUAAC4ZP4il+ouK9GZ3hEd72A/KAAAAEyNAAoAAHwkCyuKVF5SoDdO9KqP/aAAAAAwBQIoAADwkUzsB+Ww6uUj3RqPm7kuCQAAAHmKAAoAAHxkDrtVK2qDGonG1XI8nOtyAAAAkKcIoAAAwLQEilyqrSrWqa6I2nsiuS4HAAAAeYgACgAATFtttU++QocOHQtrNBrPdTkAAADIMwRQAABg2gzDohW1QZlmUq8e7VEymcx1SQAAAMgjBFAAACAtCgvsunqBXz0DYzrWNpjrcgAAAJBHCKAAAEDaVJcVKhRw6/C7fRoYjua6HAAAAOQJAigAAJA2FotFyxYG5LBZ9crRHiVMluLlq76+Pt122226/vrrdeONN+rb3/62ent7JUkHDx7UF7/4RV1//fX6xje+oXD4/SscTtUGAAAwGQIoAACQVg67VcuuCGhoZFxvnerPdTmYhMVi0a233qrm5mY1NTWpurpau3fvlmma2rx5s7Zt26bm5mbV19dr9+7dkjRlGwAAwFQIoAAAQNrN87tVXVaot08PqG+IpXj5yOfzadWqVRP3ly9frvb2drW0tMjpdKq+vl6StH79ej399NOSNGUbAADAVAigAABARiy9vEROh1WvHu1RwjRzXQ6mYJqmHnvsMa1Zs0YdHR2qqKiYaPP7/TJNU/39/VO2AQAATMWW6wIAAMDsZLdZtfyKUv2xtVNvvtuvJTX+XJeESdx7771yu926+eab9eyzz2b0uQKBwoz17fE4M9b3dAWD3lyXkBGz9XXlK8Y7uxjv7Jqt483PpvcRQAEAgIwpKynQZeWFOtY2qJDfLX+RK9cl4U80Njbq5MmTeuihh2QYhkKhkNrb2yfae3t7ZRiGfD7flG0XKxwelpmBzemDQa8ikfxd7tndPZTrEtIuGPTOyteVrxjv7GK8s2u2jvdc/NlkGJZJP2xiCR4AAMioJTV+FThTS/HiCZbi5ZPvf//7amlp0Z49e+RwOCRJS5cu1djYmA4cOCBJ2rt3r9atW3fBNgAAgKkwAwoAAGSUzWZo+RWl+sMbnXrzZL+WLmApXj44evSoHn74YdXU1Gj9+vWSpKqqKu3Zs0c7d+5UQ0ODotGoKisrtWvXLkmSYRiTtuXS0EhMe37yB51oH5BhWGS3GXLarXI5rPK47CossKvQbZdhseS6VAAA5iwCKAAAkHFBX4Fq5nn1TsegQgG3AsUsxcu1RYsW6ciRI+dtW7FihZqami65LVcMwyK3yy7Jonjc1MhYXNHxhOKJ95f6WQ2LijwO+b1OlfpcChS5ZLOyGAAAMPuZyaRi4wmNx00lzKQsFouSyfQvh78QAigAAJAVV9WUqKt/VK++3aM/W17BL/9IG4/Lrr//Wr1+8stD5xwfj5uKjI1reGRc/ZGY+oejOt4xqGPtgzIsqWB0XsCtioBHdht/HwEAM5tpJjVw9ufdQCSm4dFxRUZTH8r8qS+s6lZlSUFW68v7AGrNmjVyOBxyOlM7x2/atEmrV6/WwYMHtW3btnOmfwcCgRxXCwAAJmOzGrr2ilI933JGh0/26eoF/NxGZtlthnyFTvkKnao6eyyeMNU3FFVn36jOhEfU2RdWyzu9qij1aH55ofxepyws1QMAzBDDI+M60zuirv5R9Q1FlTh7oQ+HzVCh266ykgIVOK1y2q1y2KyyGhbZbIaWLQqqp2c4q7XmfQAlSQ888IBqa2sn7pumqc2bN+v+++9XfX29HnzwQe3evVv3339/DqsEAAAXEih26fKQV8c7hlQR8OS6HMxBNquhoK9AQV+BltSUqH84pnc7h3S6O6JTXcMqLLCpZl6R5pcXMksPAJCXIqPjOt0TUVt3RMOj45Ikr9uu+eWFChS55PM6VeCwTvmBSi4+bJkRAdSfamlpkdPpVH19vSRp/fr1Wrt2LQEUAAAzQN1lJersHdXBt3vOOyUcyBaLxaISr1MlXqeWXO5Xe09EJzuH1XK8V2+d7tfCimJdPs8rG8vzAAA5lkwm1dk3qnfaB9UzMCZJChSlPtgr97vlduZ/vJP/FSq17C6ZTGrlypW666671NHRoYqKiol2v98v0zTV398vn8930f0GAoWZKFeS5PE487K/dNeVTvla2/nqyoda86GGyWS7tot9Psbs0n3UurLxembbmGVavtW1auk8/ebl0/pf/3VMX1lbe+EHABlmsxqaX+7V/HKvwgNjeut0vw6f7NPbbQNaUFGkBaEi9okCAGTdeNzUu11DOt4xpJGxuFwOqxbP96m6rFAFMyB0+qC8r/bRRx9VKBRSLBbTjh07tH37dl133XVp6TscHpZppn/n92DQq0gkmrb+PB5n2vpLZ13plq+1/Wld6Xw/piMfaphMNmu7lPeDMbt0H6WubP0bmU1jlknvBU/5VpfXZdOyhQHVzi9Rd/dQ2vs3DEtGP2jC7BYodumTxfPUNxTVW6f6deTdfp3oGNTiy0o0v6yQPaIAABkXj5s61jGoY20DiieS8nudqrusRCG/W4YxM38O5f3HOKFQSJLkcDi0YcMGvfLKKwqFQmpvb5/4nt7eXhmGcUmznwAAQG5dNs+rZYuCuS4DmFSJ16lVV5Vr9TUhuV12HXo7rP861KHw2aUPAACkWyJh6u22Af365dM68m6/SosL9NlrQvrMNSFVlnpmbPgk5fkMqJGRESUSCXm9XiWTST311FOqq6vT0qVLNTY2pgMHDqi+vl579+7VunXrcl0uAAAAZqESr1OfuXqe2noiaj3Rp+dbzqii1K2ll/vlcuT16TQAYIZIJpM61TWsN9/t11gsoaDPpcXzS1Tiza9tFKYjr39ihsNh3XnnnUokEjJNUwsXLlRDQ4MMw9DOnTvV0NCgaDSqyspK7dq1K9flAgAAYJayWCyqChZqnt+tY22DOnq6X939Y1pSU6JqluUBAKahfziq19/pVd9QVCVep1bUBlVa7Mp1WWmX1wFUdXW1nnzyyfO2rVixQk1NTVmuCAAAAHOZzWroyvk+VZZ6dPBYjw6+HVZbT0TLFgbkdtlzXR4AYAaJxRN682S/TpwZksNu6NpFpaoKembthxp5HUABAAAA+ajQbdenl87TiTNDaj3Rp9+82q6rakpUM887a39xAACkT1tPRK+/E1Zs3NTlIa8Wz/fJbrPmuqyMIoACAAAAPgKLxaLLQ0Uq97t16O0evf5Or7r6RrV8Uamc9tn9SwQA4KOJjif02rGwOsIj8hU69MmrAiounD37PE2FAAoAAACYBrfTpk9cVa7jHUNqPdGr377apmsXBVVWUpDr0gAAeaS9J6LXjoUVT5iqu8ynhZXFMubQrFkCKAAAAGCaLBaLFlQUqbTYpZff6tYfWzu1IFSkupqSXJcGAMix8XhCrx3rVVtPRL5Ch5YvKlWR25HrsrKOAAoAAABIkyKPQ5+9JqTWk316p2NQvUNj+qs1tTJyXRgAICd6B8f08lvdGosltHi+T1dUza1ZTx/Ez0IAAAAgjaxWQ1cvCOhji8s0PDqujd//rQ4e7cl1WQCALDLNpPY+e0TPv35GFotFn7k6pNpq35wNnyRmQAEAAAAZEQq4VeSp0PEzw3rgF6/pzz8xX1/67AJZDT4DBoDZrG8oqv/5v97QkVP9qgx6dM2CgOw2/u9nBAAAAIAM8bjs2nXnav3Z8gr9xx/f1e7HDmowEst1WQCADHnrVL++98iLOnFmSH/331ZoZW2Q8OksRgEAAAD4/9m78/im6nz/4+8kbdKd7m0oS6FAKftSBhdwqSiobOIoDCPuOjNet5mro6MICDrXonccHfHn6LjjldFRQUABd8SFRdZSdgoUWrqX0r1Nzu+PakcdiwWanLR5PR/0QXO+OSfv801yknx6vt94kD3QpqvH9dVN4/tpf36F5r68XgeOVpgdCwDQhgzD0EffHNajr29ScFCgHrgmXRnpXc2O5VMoQAEAAABecOaARN131XBZJP3Pwo36Kuuo2ZEAAG2godGlF9/bqdc+2K0BPaL1wNXp6hwbanYsn0MBCgAAAPCS7onheuDaEUrpHKHnlmVr0Ud75HK7zY4FADhFpRW1euS1jVqzLV8TzkrWbb8cpJAgptv+KfQKAAAA4EURIXb9YeoQ/fPjvVq1Ple5hZX63eQBCgsONDsaAOAk7M4t19PvbFN9o1u3ThmoYX3izI7k0zgDCgAAAPCyAJtVv76wj667pK/2HC7X3JfW61DBcbNjAQBa4cfzPc28Op3iUytQgAIAAABMMnpQZ93z62FqdLn154Xf6JtdhWZHAgCcQEOjSy+8t4P5nk4BBSgAAADARCmdO2nWtSPUJS5MC97J0rtrcmQYhtmxAAA/UlpRq/9ZuFFfbDuqiWcz39PJoqcAAAAAk0WGOXTP9KF66f1dWrwmR4eLq3TDJWly2G1mRwMASNp1qEz/b3EW8z2dBgpQAAAAgA8IDLDpxvFp6hIfqn99sk+FZdW6bcogxXQKMjsaAPgtwzD08cYjWvTRHsVGBuuPUwYy5O4UMQQPAAAA8BEWi0UXj+yuO64YpKLyGs17eb32Hj5mdiwA8EvM99S2KEABAAAAPmZQSqzun5GuIEeA5r++UWu25psdCQD8CvM9tT0KUAAAAIAP6hwbqplXp6tP10i98N4OLfpoj1xut9mxAKDD23WoTA++tF5HS6t125SBmjy6p6wWi9mx2j3KdwAAAICPCgsO1O+vHKxFH+3VqvW5yiuu0m8n9VdIUKDZ0QCgwzEMQx99c1j//Hiv4iKDddvlA+WMYchdW+EMKAAAAD+UmZmpjIwMpaamavfu3c3Lc3JyNHXqVI0dO1ZTp07VgQMHWtUGz7FZrfr1hX10zbhU7ThYpode+UZHS6vNjgUAHUpDo0svLN+h//twjwb2jNHMq9MpPrUxClAAAAB+6IILLtBrr72mpKSkHyyfPXu2pk+frpUrV2r69OmaNWtWq9rgeecOSdLdvxqqypoGzXt5g7JySsyOBAAdQvN8T1lN8z3devlA5nvyAApQAAAAS8dyNwAAIABJREFUfig9PV1Op/MHy0pKSpSdna3x48dLksaPH6/s7GyVlpaesA3e06drpGZdk66YiCA9/sYWrVqfK8MwzI4FAO0W8z15DyU9AAAASJLy8/OVkJAgm80mSbLZbIqPj1d+fr4Mw2ixLTo6utW3ERMT5pHskhQa6vDYtk9XXFx4m27rL78/V4+/vlGLPtqjkuN1+t3lgxQYYGuz2ziZLPAe+tu76G/v8nZ/G4ahpWv26/l3t8sZE6r7r/uFuia0fQZ/eW1qDQpQAAAA8JqSkkq53W1/xk5cXLiqqurafLttpajoeJtv84ZL+io23KGlXx5QTt4x/ddlA9Up1N7mt9OSuLhwj+wXfhr97V30t3d5u7/rGlx6deUufZl1VEN6xerG8f0UZG37Y7U/vjZZrZYW/9jk0wWosrIy/fGPf9ShQ4dkt9vVvXt3zZ07V9HR0UpNTVWfPn1ktTaNIpw/f75SU1NNTgwAANB+OZ1OFRQUyOVyyWazyeVyqbCwUE6nU4ZhtNgGc1gtFl12Tk8lxYXqheU7NO/l9br98kHq5oG/4ANAR1FYXqMFb2/T4cJKTRrVQxPOTmbInZf49BxQFotFN954o1auXKmlS5eqa9eueuyxx5rbFy1apCVLlmjJkiUUnwAAAE5TTEyM0tLStGzZMknSsmXLlJaWpujo6BO2wVy/SEvQn64aLsOQ/rzwG23YWWh2JADwSVv3FWvui+tVcqxWd1wxSJNG9aD45EU+XYCKjIzUyJEjmy8PGTJEeXl5JiYCAADoGB566CGdc845Onr0qK677jpdeumlkqQ5c+Zo4cKFGjt2rBYuXKgHH3yweZ0TtcFc3RPDNeuadHWND9PTi7P01mf7PDLUEQDaI7dhaPHn+/XEm1sV2ylIs64boUEpsWbH8js+PQTv+9xut15//XVlZGQ0L5sxY4ZcLpfOOecc3XbbbbLbT27Me3uaBLOttufLE6D5arafyuULWX0hQ0u8na21t0efnbxTzeWN/elofeZpvppLYpJXs8ycOVMzZ878j+UpKSl68803f3KdE7XBfJ3CHPrjr4Zp4apdWv7VQe3Pq9BvJvZXhBfnhQIAX1NZ06B/LMvW1n0lOmtAomaMTZUj0Ptf2oB2VICaN2+eQkJCdNVVV0mSPv30UzmdTlVWVuruu+/WggUL9Pvf//6kttleJsEMDXW02fZ8eQI0X83241xteX+cDl/I0BJvZjuZ+4M+O3mnkstbz5GO1Gee9F3hyddyfZ+3J8AEOrLAAKuuuyRNvbp00sJVuzXnxXX63eQB6t0l0uxoAOB1B48e14J3tqnseJ1mXNRH5w1NkoUhd6bx6SF438nMzNTBgwf117/+tXnS8e8mvAwLC9MVV1yhjRs3mhkRAAAA8BmjB3XW/TOGyx5g0/z/26RV6w7JMBiSB8A/GIahj745rIdf3SCX29C9vx6m84d1ofhkMp8/A+ovf/mLsrKy9OyzzzYPsTt27JgcDoeCgoLU2NiolStXKi0tzeSkAAAAgO/olhCuWdem6/nlO7To473ae+SYrrskTcEOn/8IAACnrLKmQS++t0Ob9hRrUEqMbrg0TeEhDEX2BT796rNnzx79/e9/V3JysqZNmyZJ6tKli2688UbNmjVLFotFjY2NGjp0qO644w6T0wIAAAC+JSQoULdOGagV6w7prU/3K7dog343qb+6JTD3GoCOZ8/hcj377naVV9ZrWkYvXTiiK2c9+RCfLkD17t1bu3bt+sm2pUuXejkNAAAA0P5YLBZdPLK7ejoj9MyS7XrolW90xfkpGjOc4SgAOga329B7Xx/U4s9zFNPJoftmDFcPZ4TZsfAjPl2AAgAAANA2UrtF6cEbfqEXlu/Q6x/u0facUl1/aZoiGJoCoB0rr6zTc0uzteNgmX6RFq9rxvVlqLGPaheTkAMAAAA4fREhdt3xy0GaPqa3sg+Uafbz65SVU2J2LAA4JRt2FmrW8+u078gxXXtxX/1mYn+KTz6MewYAAADwIxaLRWPSuyq1W5SeWZKlv/xzizKGJemK83rJYbeZHQ8AflZVbYNeW7VbX2cXqIczXDeO7ydnTKjZsfAzKEABAAAAfqhrfJhmXTtCb322Tx9uOKys/aW6YXyaeneJNDsaALQoK6dEL763UxVV9Zo8uocuPbO7bFYGd7UHFKAAAAAAP+UItGn6mD4a1jtOL7y3Q48s3Kixv+imy87pocAAzoYC4Duqahv0z4/3as3WfHWODdVtlw9UciITjbcnFKAAAAAAP9e3e5QevP4XevOTvVqx7pA27S3WNWNT1bd7lNnRAPg5wzC0YVeRXvtgtyqrG3TxGd00eRRF8vaIAhQAAAAABTsCdPW4vhqWGqdXVuzS/Nc3adQgp648v5fCggPNjgfAD5Udr9PCVbu0aU+xuieG6w9XDla3hHCzY+EUUYACAAAA0GxAjxjNu3Gk3l2To5XrcrVlb7GmXdBbZ/RLkMViMTseAD/Q6HLrgw25eveLAzLchq48v5cuHNGFuZ7aOQpQAAAAAH7AEWjTFef30sh+CXp5xS49tzRbqzfn6VdjenP2AQCPytpfov/7cI+OllZrSK9YTRvTW/GRwWbHQhugAAUAAADgJ3VLCNf9M4brsy15emf1fj340nqdO7izJp/TU3FmhwPQoRSW1+ifH+3Rpj3FSogK1p1XDNaglBizY6ENUYACAAAA0CKr1aLzhybpF2nxWrImRx9/c0RrdxRq+thU/aJPLBMBAzgtFdX1evudrXr/ywOy2Sy6/NyeumhENwUGMNyuo6EABQAAAOBnhQYFavqYPjp3SJL++dEePf/udr0T4dCks3vorIGJzM0C4KTU1bu0cv0hrVh7SPWNbp0zyKmJo3ooMsxhdjR4CAUoAAAAAK2WFBuqP0wdoiNlNXrh3Sy9+P5OrVh3SJeN7qnhqXFMVA7ghOoaXPps0xG9v/aQjlXVa1ifON04eaCCqGF3eBSgAAAAAJy0IX3iNfPqdG3cXaS3V+/X04uz1CUuTOPP6q701HhZrRSiAPxbbX2jPtl4RCvXHVJFdYP6dovUf102UL26dFJcXLiKio6bHREeRgEKAAAAwCmxWCwanhqvob3j9NX2o1r+1UE9s2S74qP265IzuuvM/onM4wL4uYrqen266Yg+3HBYlTUNGtAjWhPOTlbvLpFmR4OXUYACAAAAcFqsVovOHujUmf0TtXF3kZZ/fVAvvb9TS9bk6PyhSTpnSGdFhNjNjgnAi44UVeqDDbn6anuBGhrdGpQSowlnJyulcyezo8EkFKAAAAAAtAmr1aL0vvEanhqn7QdKtWLtIb29er/e/eKARqbFK2N4F/VwRpgdE4CHNLrc2ravRB9vOqLtOaWyB1h19oBEjUnvqs6xoWbHg8koQAEAAABoUxaLRQN6xGhAjxjlFVfpo42H9eW2o/oi66h6OCM0apBTI9PiFRIUaHZUAG2goKxan2/J1xfb8nWsql6RYXZNOaenzhuapLBgnudoQgEKAAAAgMd0jg3VjItSdfk5KfpiW75Wb83Tqyt36fUP92hYn1iNGuhUWnKUbFbmigLak8qaBn2zq1Brswu081C5rBaLBqXE6JzBnTUwJZrnNP4DBSgAAAAAHhcSFKALR3TVmPQuOlhwXF9sPaqvs49q3Y5ChYcEanifOA3vG6++3SL54Ar4qJq6Rm3eU6y1Owq0PadULrehhOgQTTmnp84e6FRUuMPsiPBhFKAAAAAAeI3FYlFyYoSSEyN0ZUYvbdlbrA27CvXV9gJ9ujlPYcGBGto7VoNSYtUvOUrBDj6yAGYqKq/R5r3F2rK3WLsOlcvlNhQT4dBFI7rqF2kJ6pYQJovFYnZMtAMczQEAAACYIjDAqvS+8UrvG6/6Bpe27S/VN7sKtWFXoT7fmi+b1aLeXTppYEqM+idHq0t8mKx80AU8qqauUbtzy7XjYJm2HyjVkaIqSZIzJkQXjuiqob1jlZLUieciThoFKAAAAACmswfaNDw1TsNT49TocmvfkWPauq9E2/aX6M1P9ulN7VNoUID6dI1UatdIpXaLUpf4UIbrAaepqrZB+45UaO+RY9p5qEw5eRVyuQ0F2Kzq3aWTRmU4NaRXrBKiQ8yOinaOAhQAAAAAnxJgsyq1W5RSu0XpivN7qbSiVjsOlmlXbrl2HSrTpj3FkiR7oFXJCeHq0TlCPTt3UnJiuGI6BXFmBtCCmrpGHS6qVG5hpQ4cPa59R44pv6RakmSxSMmJ4Rr7i25KS45S76ROsgfaTE6MjoQCFAAAAACfFh0RpLMHOnX2QKckqbSiVrtyy5WTV6Gc/Ap99M0RrVyXK0ly2G1Kig1t+okLU1JcqLrEhioi1M48NfAbDY1uFR+rUX5JtXILK7/9Oa6i8trm64QFByqlc4TO7J+olKRO6uEMV5CdEgE8h0cXAAAAgHYlOiJIZ/ZP1Jn9EyVJjS63Dhc1ndGRV1SlI8VV2rK3WJ9vzW9eJzQoQLGRwYqLDFZcpyDFRQYrNrLp/5iIIAXYGMqH9sMwDB2vaVBZRZ1KK2pVUFajwvIaFZZVq7CsRiUVtTKMputaJMVHh6h7YoRGDeqsrvFh6hYfpqhwB0VZeFW7LkDl5OTo3nvvVXl5uSIjI5WZmank5GSzYwEAAHRovAeDrwmwWZu/We/7KqrqdaS4SkeKKpVfUq2iYzXKLazU5j1FanQZP7huWHCgIsPsigxzqNO3/0eGORQRaldoUIBCgwKb/g8OVJDdxgd3tDmX262qmkYdr2lQVU2DKr/3U1FVr/LKOpUdb/opr6z7j8dwaFCA4qNC1Cupk84akKiEqBDFRwerS2yYHHaG0sF87boANXv2bE2fPl2TJk3SkiVLNGvWLL3yyitmxwIAAOjQeA+G9iIi1K6IULvSukf9YLnbbai8sk5F5TUqKq9VSUWtjlXWqbyyXseq6nSkuErHKuvlNoyf3K7VYlFocIBCggIVFtT0v8NukyPQKkeg7dvfbQoKtMlub/rfEWhTYIBVAbZvfwIsCrBZFfjdZZtFAd+2B9qsslopcPkSwzDU6HKrobHp/0aXWw0utxob3Wp0GWpwudXQ2LS8sfHbNpdbdQ1u1dY3qq7epdrmn+9dbnCpprap6FRT19ji7dsDrIoMdyg63KFeXTopKsyhqPCmn+iIpjP5woIDvdgjwMlrtwWokpISZWdn68UXX5QkjR8/XvPmzVNpaamio6NbtQ1PHtTDQtruyR8SFCiL4W6TbbVlrrbmq9l+nKst74/T4av9JXk328ncH/TZyTuVXN56jnSkPvOkkKCmPL6W6/s88XrMBzfPOd33YO3l/Vdb66iPyfa6X1arRbGRwYqNDFZaC9dxG4aqaxtV+W1hoLq2UdV1DaqudTVd/nZZTV2DquuaigoVVW7VN7hU1+iSy/XTxavWsliaCl0Wq0VWi0UB3/b1d5etVouskixW/fuy1SKLvmuXJIsslqYhWGq+qyzf/Wtebvl3y7e3bWnO8O/VLN9b3fL9iz9Yx/i2aPfjvf+ulmfIaLHR+NF1/938wwXGj/6X0fJywzDkdhtyG4bcRtPG3d9bZrglt/Tt703XMdyGDEkuwy23u2kdl/v07k+p6ZseHYFNhUp7oE2REY6mQqU94Nuz7JqKmSGOgKYCp6PprLuQ4EDZAzr+ENH2ejz5Of722nSibVqMHz+b24msrCzdc889Wr58efOySy65RI8++qj69+9vYjIAAICOi/dgAADgVHT8MioAAAAAAABM1W4LUE6nUwUFBXK5XJIkl8ulwsJCOZ1Ok5MBAAB0XLwHAwAAp6LdFqBiYmKUlpamZcuWSZKWLVumtLS0Vs//BAAAgJPHezAAAHAq2u0cUJK0b98+3XvvvaqoqFBERIQyMzPVs2dPs2MBAAB0aLwHAwAAJ6tdF6AAAAAAAADg+9rtEDwAAAAAAAC0DxSgAAAAAAAA4FEUoAAAAAAAAOBRFKAAAAAAAADgURSgfEhZWZluuukmjR07VhMmTNCtt96q0tJSSdLmzZs1ceJEjR07Vtdff71KSkpMTusfbrnlFk2cOFGTJ0/W9OnTtWPHDklSTk6Opk6dqrFjx2rq1Kk6cOCAuUH9zFNPPaXU1FTt3r1bEs8Ps2RkZGjcuHGaNGmSJk2apM8//1wS94eZ6urqNHv2bF100UWaMGGCHnjgAUkcs9CxtObx7HK59OCDD2rMmDG68MIL9eabb3o/aAfRmv5esGCBLr30Uk2YMEFTpkxpfj3AyTuZ4/X+/fs1ePBgZWZmei9gB9Pa/n7vvfc0YcIEjR8/XhMmTFBxcbF3g3YQrenvkpIS3XzzzZowYYIuvvhizZkzR42Njd4P2wFkZmYqIyPjB5+bfszrr5cGfEZZWZnx9ddfN19+5JFHjD/96U+Gy+UyxowZY6xfv94wDMNYsGCBce+995oV069UVFQ0//7BBx8YkydPNgzDMGbMmGEsXrzYMAzDWLx4sTFjxgxT8vmjrKws44YbbjDOP/98Y9euXTw/TPTdffB93B/mmjdvnvHwww8bbrfbMAzDKCoqMgyDYxY6ltY8nt955x3j+uuvN1wul1FSUmKMHj3ayM3N9XbUDqE1/b169WqjurraMAzD2LFjhzF8+HCjpqbGqzk7itYerxsbG42rrrrK+MMf/mA88sgj3ozYobSmv7du3WpcfPHFRmFhoWEYTZ8PamtrvZqzo2hNfz/00EPNj+n6+nrjl7/8pbF8+XKv5uwo1q9fb+Tl5f3ke/bvePv1kjOgfEhkZKRGjhzZfHnIkCHKy8tTVlaWHA6H0tPTJUnTpk3TihUrzIrpV8LDw5t/r6yslMViUUlJibKzszV+/HhJ0vjx45Wdnd18tho8p76+XnPnztWcOXOal/H88C3cH+apqqrS4sWLdccdd8hisUiSYmNjOWahQ2nt4/m9997TFVdcIavVqujoaI0ZM4Zj0SlobX+PHj1awcHBkqTU1FQZhqHy8nKv523vTuZ4/eyzz+q8885TcnKyl1N2HK3t75deeknXX3+94uLiJDV9PnA4HF7P2961tr8tFouqqqrkdrtVX1+vhoYGJSQkmBG53UtPT5fT6Tzhdbz9ekkByke53W69/vrrysjIUH5+vjp37tzcFh0dLbfbzQu7l9x///0677zz9PjjjyszM1P5+flKSEiQzWaTJNlsNsXHxys/P9/kpB3fE088oYkTJ6pLly7Ny3h+mOuuu+7ShAkTNGfOHFVUVHB/mCg3N1eRkZF66qmnNGXKFM2YMUMbNmzgmIUOpbWP5x8fi5xOp44ePerVrB3BqRw/Fi9erG7duikxMdFbMTuM1vb3zp07tWbNGl177bUmpOw4Wtvf+/btU25urn7961/rsssu09NPPy3DMMyI3K61tr9vueUW5eTkaNSoUc0/w4cPNyOyX/D26yUFKB81b948hYSE6KqrrjI7it97+OGH9emnn+r3v/+95s+fb3Ycv7Vp0yZlZWVp+vTpZkfBt1577TW9++67euutt2QYhubOnWt2JL/mcrmUm5urfv366e2339Zdd92l2267TdXV1WZHA+An1q1bpyeeeEL/+7//a3aUDquhoUEPPPCAHnzwweYP8vAsl8ulXbt26cUXX9Srr76q1atXa8mSJWbH6rBWrFih1NRUrVmzRqtXr9aGDRs4g7UDoQDlgzIzM3Xw4EH99a9/ldVqldPpVF5eXnN7aWmprFarIiMjTUzpfyZPnqy1a9cqMTFRBQUFcrlckppelAoLC3/29EacnvXr12vfvn264IILlJGRoaNHj+qGG27QwYMHeX6Y5LvHvN1u1/Tp07Vx40aOVyZyOp0KCAhoPrV98ODBioqKUlBQEMcsdBhOp7NVj+cfH4vy8/M5I+cUtLa/paY/FN19991asGCBevbs6e2oHUJr+ruoqEiHDh3SzTffrIy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",
            "text/plain": [
              "<Figure size 1440x2160 with 8 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "independent_features=[feature for feature in df.columns if feature not in ['Outcome']]\n",
        "print(independent_features)\n",
        "plt.figure(figsize=(20,30))\n",
        "for i in enumerate(independent_features):\n",
        "  plt.subplot(5, 2, i[0]+1)\n",
        "  sns.set(rc={'figure.figsize':(7,5)})\n",
        "  sns.histplot(data=df, x=i[1], kde=True)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "LLG2IyZN4-6Q"
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "**REGRESSION** : Here we are using multiple regression model since we have multiple independent variables.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 235,
      "metadata": {
        "id": "Z5UK60q_ZMgr"
      },
      "outputs": [],
      "source": [
        "X = df[['BMI','age','gender','Children','smoking_status','location']]\n",
        "Y = df['price_log']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l-AMl0X_71vp"
      },
      "source": [
        "Spliting the data into test and train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 236,
      "metadata": {
        "id": "8HQEpONYbL0-"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 237,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GKh0p46hb3iD",
        "outputId": "d8b20522-4074-40e1-a2c6-c71fb4a98f1f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(1338, 6) (1070, 6) (268, 6)\n"
          ]
        }
      ],
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 238,
      "metadata": {
        "id": "id1XFPej5ZOM"
      },
      "outputs": [],
      "source": [
        "# loading the Linear Regression model\n",
        "regressor = LinearRegression()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D5UUA3qG76tB"
      },
      "source": [
        "Feature Scaling\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 239,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vdhHo3O18D6B",
        "outputId": "5e2509c4-b238-4af2-9ef5-0517e41fd99b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[-1.42342781, -1.34192511,  1.01886959, -0.93004238,  0.50729114,\n",
              "         1.3755987 ],\n",
              "       [ 0.03150243, -0.18151923, -0.98147988,  1.56197434,  0.50729114,\n",
              "         0.47148899],\n",
              "       [ 0.4018937 ,  1.34151348,  1.01886959, -0.93004238,  0.50729114,\n",
              "        -1.33673044],\n",
              "       ...,\n",
              "       [ 2.116514  ,  1.55908958, -0.98147988, -0.93004238,  0.50729114,\n",
              "        -1.33673044],\n",
              "       [-0.81332262,  0.1770423 ,  1.01886959, -0.09937014,  0.50729114,\n",
              "        -1.33673044],\n",
              "       [ 0.28453377,  1.34151348,  1.01886959, -0.09937014,  0.50729114,\n",
              "         1.3755987 ]])"
            ]
          },
          "execution_count": 239,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train=sc.fit_transform(X_train)\n",
        "X_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 240,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Eixii-W0-l7D",
        "outputId": "630360b5-8094-4e4b-f521-9daa74c38397"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[-1.13876754, -1.19687437, -0.98147988, -0.93004238,  0.50729114,\n",
              "         1.3755987 ],\n",
              "       [-0.14245662,  1.12393738,  1.01886959, -0.93004238,  0.50729114,\n",
              "         1.3755987 ],\n",
              "       [-0.5378181 ,  0.76131054, -0.98147988, -0.09937014,  0.50729114,\n",
              "         1.3755987 ],\n",
              "       ...,\n",
              "       [-0.2689723 ,  1.55908958,  1.01886959, -0.93004238, -1.97125461,\n",
              "         0.47148899],\n",
              "       [ 1.09939458,  0.61625981, -0.98147988,  0.7313021 ,  0.50729114,\n",
              "        -0.43262072],\n",
              "       [-0.98062295,  0.83383591, -0.98147988,  0.7313021 , -1.97125461,\n",
              "         0.47148899]])"
            ]
          },
          "execution_count": 240,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_test=sc.transform(X_test)\n",
        "X_test"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mq9_lyID-4LJ"
      },
      "source": [
        "Fitting linear regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 241,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yRHiG9dqcxxN",
        "outputId": "d23a66e1-085d-47ec-ae5e-846ef2fc5631"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "execution_count": 241,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "regressor.fit(X_train, Y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4TiZXhrm8s3d"
      },
      "source": [
        "### Training the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 256,
      "metadata": {
        "id": "NhRL9x_wc6-p"
      },
      "outputs": [],
      "source": [
        "# prediction on training data\n",
        "training_data_prediction =regressor.predict(X_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 257,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bQ8gnyDMdMSb",
        "outputId": "bca7c0de-0a4f-4c42-d8c6-5150433829a1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "R squared value :  0.7580138779753024\n"
          ]
        }
      ],
      "source": [
        "# R squared value\n",
        "r2_train = metrics.r2_score(Y_train, training_data_prediction)\n",
        "print('R squared value : ', r2_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3asELJqP85ka"
      },
      "source": [
        "### Testing the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 244,
      "metadata": {
        "id": "pHQSjvekdsgO"
      },
      "outputs": [],
      "source": [
        "# prediction on test data\n",
        "test_data_prediction =regressor.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 245,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YWPIzD8ud5KA",
        "outputId": "5b2885de-970b-4406-c3e8-10c5a80ff0fe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "R squared value :  0.7945281267218772\n"
          ]
        }
      ],
      "source": [
        "# R squared value\n",
        "r2_test = metrics.r2_score(Y_test, test_data_prediction)\n",
        "print('R squared value : ', r2_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KQ1pMsXJKdAV",
        "outputId": "5b2885de-970b-4406-c3e8-10c5a80ff0fe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "R squared value :  0.7945281267218772\n"
          ]
        }
      ],
      "source": [
        "# R squared value\n",
        "r2_test = metrics.r2_score(Y_test, test_data_prediction)\n",
        "print('R squared value : ', r2_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2xrUl0NKAygn"
      },
      "source": [
        "### **Random Forest Regressor**  : Random Forest method is used for both regression and classification. Here we use it to train and test the given data for prediction "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 260,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2pCmFWB2AyNX",
        "outputId": "fe504700-7f68-43e2-a5d0-612515e3ecc7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8286985241211432"
            ]
          },
          "execution_count": 260,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import r2_score\n",
        "model = RandomForestRegressor()\n",
        "model.fit(X_train, Y_train)\n",
        "pred = model.predict(X_test)\n",
        "r2_score(Y_test, pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 261,
      "metadata": {
        "id": "oa6ERwnLJmxE"
      },
      "outputs": [],
      "source": [
        "training_data_prediction_random_forest = model.predict(X_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 262,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "scgQyPZwKLgc",
        "outputId": "acf8bdef-e2b4-43a5-934a-a58d4aa30b97"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "R squared value :  0.9725891753470589\n"
          ]
        }
      ],
      "source": [
        "# R squared value\n",
        "r2_train = metrics.r2_score(Y_train, training_data_prediction_random_forest)\n",
        "print('R squared value : ', r2_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HMVBepEZKbz7"
      },
      "outputs": [],
      "source": [
        "# prediction on test data\n",
        "test_data_prediction =regressor.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wXXXQSgGKfOF",
        "outputId": "5b2885de-970b-4406-c3e8-10c5a80ff0fe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "R squared value :  0.7945281267218772\n"
          ]
        }
      ],
      "source": [
        "# R squared value\n",
        "r2_test = metrics.r2_score(Y_test, test_data_prediction)\n",
        "print('R squared value : ', r2_test)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
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        "0Dt4yLlzr0aH",
        "AuFKfxG4vAtP",
        "1SrWcx0rvqLb",
        "OUOKpKo-w24v"
      ],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}