insurance-cost-prediction.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# **1. Importing Libraries**","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import accuracy_score, classification_report, confusion_matrix\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn import svm\nfrom sklearn import metrics\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import mean_squared_error, r2_score\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.neighbors import KNeighborsRegressor\nfrom sklearn.svm import SVR\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.tree import DecisionTreeRegressor","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-12-29T23:06:36.277322Z","iopub.execute_input":"2023-12-29T23:06:36.277716Z","iopub.status.idle":"2023-12-29T23:06:36.284359Z","shell.execute_reply.started":"2023-12-29T23:06:36.277688Z","shell.execute_reply":"2023-12-29T23:06:36.283214Z"},"trusted":true},"execution_count":176,"outputs":[]},{"cell_type":"markdown","source":"# **2. Loading the Dataset**","metadata":{}},{"cell_type":"code","source":"fpath = '/kaggle/input/insurance/insurance.csv'\ninsurance = pd.read_csv(fpath)\ninsurance.head(5)","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:06:36.292147Z","iopub.execute_input":"2023-12-29T23:06:36.292498Z","iopub.status.idle":"2023-12-29T23:06:36.319458Z","shell.execute_reply.started":"2023-12-29T23:06:36.292470Z","shell.execute_reply":"2023-12-29T23:06:36.318134Z"},"trusted":true},"execution_count":177,"outputs":[{"execution_count":177,"output_type":"execute_result","data":{"text/plain":"   age     sex     bmi  children smoker     region      charges\n0   19  female  27.900         0    yes  southwest  16884.92400\n1   18    male  33.770         1     no  southeast   1725.55230\n2   28    male  33.000         3     no  southeast   4449.46200\n3   33    male  22.705         0     no  northwest  21984.47061\n4   32    male  28.880         0     no  northwest   3866.85520","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>sex</th>\n      <th>bmi</th>\n      <th>children</th>\n      <th>smoker</th>\n      <th>region</th>\n      <th>charges</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>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>"},"metadata":{}}]},{"cell_type":"markdown","source":"# **3. Understanding and Pre-Processing the Data**","metadata":{}},{"cell_type":"code","source":"insurance.describe()","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:06:36.321432Z","iopub.execute_input":"2023-12-29T23:06:36.321715Z","iopub.status.idle":"2023-12-29T23:06:36.342023Z","shell.execute_reply.started":"2023-12-29T23:06:36.321692Z","shell.execute_reply":"2023-12-29T23:06:36.340902Z"},"trusted":true},"execution_count":178,"outputs":[{"execution_count":178,"output_type":"execute_result","data":{"text/plain":"               age          bmi     children       charges\ncount  1338.000000  1338.000000  1338.000000   1338.000000\nmean     39.207025    30.663397     1.094918  13270.422265\nstd      14.049960     6.098187     1.205493  12110.011237\nmin      18.000000    15.960000     0.000000   1121.873900\n25%      27.000000    26.296250     0.000000   4740.287150\n50%      39.000000    30.400000     1.000000   9382.033000\n75%      51.000000    34.693750     2.000000  16639.912515\nmax      64.000000    53.130000     5.000000  63770.428010","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>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>"},"metadata":{}}]},{"cell_type":"markdown","source":"Get rid of outliers","metadata":{}},{"cell_type":"code","source":"upper_limit = insurance.charges.mean() + 3*insurance.charges.std()\nupper_limit","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:06:36.343507Z","iopub.execute_input":"2023-12-29T23:06:36.343880Z","iopub.status.idle":"2023-12-29T23:06:36.351737Z","shell.execute_reply.started":"2023-12-29T23:06:36.343848Z","shell.execute_reply":"2023-12-29T23:06:36.350817Z"},"trusted":true},"execution_count":179,"outputs":[{"execution_count":179,"output_type":"execute_result","data":{"text/plain":"49600.45597522326"},"metadata":{}}]},{"cell_type":"code","source":"lower_limit = insurance.charges.mean() - 3*insurance.charges.std()\nlower_limit","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:06:36.353056Z","iopub.execute_input":"2023-12-29T23:06:36.353752Z","iopub.status.idle":"2023-12-29T23:06:36.362541Z","shell.execute_reply.started":"2023-12-29T23:06:36.353720Z","shell.execute_reply":"2023-12-29T23:06:36.361809Z"},"trusted":true},"execution_count":180,"outputs":[{"execution_count":180,"output_type":"execute_result","data":{"text/plain":"-23059.611444940747"},"metadata":{}}]},{"cell_type":"code","source":"insurance[(insurance.charges > upper_limit) | (insurance.charges < lower_limit)]","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:06:36.365128Z","iopub.execute_input":"2023-12-29T23:06:36.365458Z","iopub.status.idle":"2023-12-29T23:06:36.378815Z","shell.execute_reply.started":"2023-12-29T23:06:36.365428Z","shell.execute_reply":"2023-12-29T23:06:36.377724Z"},"trusted":true},"execution_count":181,"outputs":[{"execution_count":181,"output_type":"execute_result","data":{"text/plain":"      age     sex     bmi  children smoker     region      charges\n34     28    male  36.400         1    yes  southwest  51194.55914\n543    54  female  47.410         0    yes  southeast  63770.42801\n577    31  female  38.095         1    yes  northeast  58571.07448\n819    33  female  35.530         0    yes  northwest  55135.40209\n1146   60    male  32.800         0    yes  southwest  52590.82939\n1230   52    male  34.485         3    yes  northwest  60021.39897\n1300   45    male  30.360         0    yes  southeast  62592.87309","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>sex</th>\n      <th>bmi</th>\n      <th>children</th>\n      <th>smoker</th>\n      <th>region</th>\n      <th>charges</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>34</th>\n      <td>28</td>\n      <td>male</td>\n      <td>36.400</td>\n      <td>1</td>\n      <td>yes</td>\n      <td>southwest</td>\n      <td>51194.55914</td>\n    </tr>\n    <tr>\n      <th>543</th>\n      <td>54</td>\n      <td>female</td>\n      <td>47.410</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>southeast</td>\n      <td>63770.42801</td>\n    </tr>\n    <tr>\n      <th>577</th>\n      <td>31</td>\n      <td>female</td>\n      <td>38.095</td>\n      <td>1</td>\n      <td>yes</td>\n      <td>northeast</td>\n      <td>58571.07448</td>\n    </tr>\n    <tr>\n      <th>819</th>\n      <td>33</td>\n      <td>female</td>\n      <td>35.530</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>northwest</td>\n      <td>55135.40209</td>\n    </tr>\n    <tr>\n      <th>1146</th>\n      <td>60</td>\n      <td>male</td>\n      <td>32.800</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>southwest</td>\n      <td>52590.82939</td>\n    </tr>\n    <tr>\n      <th>1230</th>\n      <td>52</td>\n      <td>male</td>\n      <td>34.485</td>\n      <td>3</td>\n      <td>yes</td>\n      <td>northwest</td>\n      <td>60021.39897</td>\n    </tr>\n    <tr>\n      <th>1300</th>\n      <td>45</td>\n      <td>male</td>\n      <td>30.360</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>southeast</td>\n      <td>62592.87309</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"insurance = insurance[insurance.charges < upper_limit]","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:06:36.380033Z","iopub.execute_input":"2023-12-29T23:06:36.380938Z","iopub.status.idle":"2023-12-29T23:06:36.388446Z","shell.execute_reply.started":"2023-12-29T23:06:36.380905Z","shell.execute_reply":"2023-12-29T23:06:36.387658Z"},"trusted":true},"execution_count":182,"outputs":[]},{"cell_type":"code","source":"insurance.charges.max()\n","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:06:36.389490Z","iopub.execute_input":"2023-12-29T23:06:36.390341Z","iopub.status.idle":"2023-12-29T23:06:36.399656Z","shell.execute_reply.started":"2023-12-29T23:06:36.390318Z","shell.execute_reply":"2023-12-29T23:06:36.399009Z"},"trusted":true},"execution_count":183,"outputs":[{"execution_count":183,"output_type":"execute_result","data":{"text/plain":"49577.6624"},"metadata":{}}]},{"cell_type":"code","source":"insurance.info()","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:06:36.400986Z","iopub.execute_input":"2023-12-29T23:06:36.401407Z","iopub.status.idle":"2023-12-29T23:06:36.415379Z","shell.execute_reply.started":"2023-12-29T23:06:36.401373Z","shell.execute_reply":"2023-12-29T23:06:36.414480Z"},"trusted":true},"execution_count":184,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nIndex: 1331 entries, 0 to 1337\nData columns (total 7 columns):\n #   Column    Non-Null Count  Dtype  \n---  ------    --------------  -----  \n 0   age       1331 non-null   int64  \n 1   sex       1331 non-null   object \n 2   bmi       1331 non-null   float64\n 3   children  1331 non-null   int64  \n 4   smoker    1331 non-null   object \n 5   region    1331 non-null   object \n 6   charges   1331 non-null   float64\ndtypes: float64(2), int64(2), object(3)\nmemory usage: 83.2+ KB\n","output_type":"stream"}]},{"cell_type":"markdown","source":"Dropping duplicated values","metadata":{}},{"cell_type":"code","source":"insurance.duplicated().sum()","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:06:36.417747Z","iopub.execute_input":"2023-12-29T23:06:36.418050Z","iopub.status.idle":"2023-12-29T23:06:36.426897Z","shell.execute_reply.started":"2023-12-29T23:06:36.418028Z","shell.execute_reply":"2023-12-29T23:06:36.426225Z"},"trusted":true},"execution_count":185,"outputs":[{"execution_count":185,"output_type":"execute_result","data":{"text/plain":"1"},"metadata":{}}]},{"cell_type":"code","source":"insurance.drop_duplicates(inplace=True)","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:06:36.427756Z","iopub.execute_input":"2023-12-29T23:06:36.428503Z","iopub.status.idle":"2023-12-29T23:06:36.435884Z","shell.execute_reply.started":"2023-12-29T23:06:36.428480Z","shell.execute_reply":"2023-12-29T23:06:36.435038Z"},"trusted":true},"execution_count":186,"outputs":[]},{"cell_type":"code","source":"label_encoder = LabelEncoder()\ninsurance['sex'] = label_encoder.fit_transform(insurance['sex'])\ninsurance['smoker'] = label_encoder.fit_transform(insurance['smoker'])\ninsurance['region'] = label_encoder.fit_transform(insurance['region'])","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:06:36.436810Z","iopub.execute_input":"2023-12-29T23:06:36.437105Z","iopub.status.idle":"2023-12-29T23:06:36.446984Z","shell.execute_reply.started":"2023-12-29T23:06:36.437078Z","shell.execute_reply":"2023-12-29T23:06:36.445826Z"},"trusted":true},"execution_count":187,"outputs":[]},{"cell_type":"code","source":"insurance.head(5)","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:06:36.449182Z","iopub.execute_input":"2023-12-29T23:06:36.450072Z","iopub.status.idle":"2023-12-29T23:06:36.463145Z","shell.execute_reply.started":"2023-12-29T23:06:36.450024Z","shell.execute_reply":"2023-12-29T23:06:36.462257Z"},"trusted":true},"execution_count":188,"outputs":[{"execution_count":188,"output_type":"execute_result","data":{"text/plain":"   age  sex     bmi  children  smoker  region      charges\n0   19    0  27.900         0       1       3  16884.92400\n1   18    1  33.770         1       0       2   1725.55230\n2   28    1  33.000         3       0       2   4449.46200\n3   33    1  22.705         0       0       1  21984.47061\n4   32    1  28.880         0       0       1   3866.85520","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>sex</th>\n      <th>bmi</th>\n      <th>children</th>\n      <th>smoker</th>\n      <th>region</th>\n      <th>charges</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>19</td>\n      <td>0</td>\n      <td>27.900</td>\n      <td>0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>16884.92400</td>\n    </tr>\n    <tr>\n      <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      <td>2</td>\n      <td>1725.55230</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>28</td>\n      <td>1</td>\n      <td>33.000</td>\n      <td>3</td>\n      <td>0</td>\n      <td>2</td>\n      <td>4449.46200</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>33</td>\n      <td>1</td>\n      <td>22.705</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>21984.47061</td>\n    </tr>\n    <tr>\n      <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      <td>1</td>\n      <td>3866.85520</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"plt.figure(figsize=(7,4)) \nsns.heatmap(insurance.corr(),annot=True,cmap='Blues') #draws  heatmap with input as the correlation matrix calculted by(iris.corr())\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:06:36.464078Z","iopub.execute_input":"2023-12-29T23:06:36.464965Z","iopub.status.idle":"2023-12-29T23:06:36.776068Z","shell.execute_reply.started":"2023-12-29T23:06:36.464939Z","shell.execute_reply":"2023-12-29T23:06:36.775066Z"},"trusted":true},"execution_count":189,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 700x400 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"# **4. Splitting the Data**","metadata":{}},{"cell_type":"code","source":"train, test = train_test_split(insurance, test_size = 0.2)# in this our main data is split into train and test\n# the attribute test_size=0.3 splits the data into 70% and 30% ratio. train=70% and test=30%\nprint(train.shape)\nprint(test.shape)","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:06:36.778810Z","iopub.execute_input":"2023-12-29T23:06:36.779071Z","iopub.status.idle":"2023-12-29T23:06:36.785324Z","shell.execute_reply.started":"2023-12-29T23:06:36.779050Z","shell.execute_reply":"2023-12-29T23:06:36.784296Z"},"trusted":true},"execution_count":190,"outputs":[{"name":"stdout","text":"(1064, 7)\n(266, 7)\n","output_type":"stream"}]},{"cell_type":"code","source":"train_X = train[['age','sex','bmi','children','smoker','region']]# taking the training data features\ntrain_y=train.charges# output of our training data\ntest_X= test[['age','sex','bmi','children','smoker','region']] # taking test data features\ntest_y =test.charges","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:06:36.786559Z","iopub.execute_input":"2023-12-29T23:06:36.786816Z","iopub.status.idle":"2023-12-29T23:06:36.797941Z","shell.execute_reply.started":"2023-12-29T23:06:36.786794Z","shell.execute_reply":"2023-12-29T23:06:36.797008Z"},"trusted":true},"execution_count":191,"outputs":[]},{"cell_type":"code","source":"train_X.head(5)","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:06:36.799229Z","iopub.execute_input":"2023-12-29T23:06:36.799518Z","iopub.status.idle":"2023-12-29T23:06:36.813735Z","shell.execute_reply.started":"2023-12-29T23:06:36.799496Z","shell.execute_reply":"2023-12-29T23:06:36.812646Z"},"trusted":true},"execution_count":192,"outputs":[{"execution_count":192,"output_type":"execute_result","data":{"text/plain":"     age  sex     bmi  children  smoker  region\n67    40    1  26.315         1       0       1\n638   39    1  26.410         0       1       0\n832   28    0  23.845         2       0       1\n668   62    1  32.015         0       1       0\n541   20    0  31.790         2       0       2","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>sex</th>\n      <th>bmi</th>\n      <th>children</th>\n      <th>smoker</th>\n      <th>region</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>67</th>\n      <td>40</td>\n      <td>1</td>\n      <td>26.315</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>638</th>\n      <td>39</td>\n      <td>1</td>\n      <td>26.410</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>832</th>\n      <td>28</td>\n      <td>0</td>\n      <td>23.845</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>668</th>\n      <td>62</td>\n      <td>1</td>\n      <td>32.015</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>541</th>\n      <td>20</td>\n      <td>0</td>\n      <td>31.790</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"# **5. Training the Models**","metadata":{}},{"cell_type":"markdown","source":"**1. Linear Regression Model**","metadata":{}},{"cell_type":"code","source":"lr_model = LinearRegression()\nlr_model.fit(train_X, train_y)","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:09:25.531574Z","iopub.execute_input":"2023-12-29T23:09:25.532620Z","iopub.status.idle":"2023-12-29T23:09:25.542485Z","shell.execute_reply.started":"2023-12-29T23:09:25.532587Z","shell.execute_reply":"2023-12-29T23:09:25.541455Z"},"trusted":true},"execution_count":209,"outputs":[{"execution_count":209,"output_type":"execute_result","data":{"text/plain":"LinearRegression()","text/html":"<style>#sk-container-id-35 {color: black;background-color: white;}#sk-container-id-35 pre{padding: 0;}#sk-container-id-35 div.sk-toggleable {background-color: white;}#sk-container-id-35 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-35 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-35 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-35 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-35 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-35 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-35 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-35 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-35 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-35 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-35 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-35 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-35 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-35 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-35 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-35 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-35 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-35 div.sk-item {position: relative;z-index: 1;}#sk-container-id-35 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-35 div.sk-item::before, #sk-container-id-35 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-35 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-35 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-35 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-35 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-35 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-35 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-35 div.sk-label-container {text-align: center;}#sk-container-id-35 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-35 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-35\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" checked><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"y_pred_lr = lr_model.predict(test_X)","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:09:25.928904Z","iopub.execute_input":"2023-12-29T23:09:25.929328Z","iopub.status.idle":"2023-12-29T23:09:25.936667Z","shell.execute_reply.started":"2023-12-29T23:09:25.929295Z","shell.execute_reply":"2023-12-29T23:09:25.935394Z"},"trusted":true},"execution_count":210,"outputs":[]},{"cell_type":"code","source":"mse_lr = mean_squared_error(test_y, y_pred_lr)\nr2_lr = r2_score(test_y, y_pred_lr)","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:09:26.289459Z","iopub.execute_input":"2023-12-29T23:09:26.289829Z","iopub.status.idle":"2023-12-29T23:09:26.296935Z","shell.execute_reply.started":"2023-12-29T23:09:26.289799Z","shell.execute_reply":"2023-12-29T23:09:26.296099Z"},"trusted":true},"execution_count":211,"outputs":[]},{"cell_type":"code","source":"print(f'Mean Squared Error for Linear Regression: {mse_lr}')\nprint(f'R^2 Score for Linear Regression: {r2_lr}')","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:09:26.570853Z","iopub.execute_input":"2023-12-29T23:09:26.571350Z","iopub.status.idle":"2023-12-29T23:09:26.576087Z","shell.execute_reply.started":"2023-12-29T23:09:26.571327Z","shell.execute_reply":"2023-12-29T23:09:26.574615Z"},"trusted":true},"execution_count":212,"outputs":[{"name":"stdout","text":"Mean Squared Error for Linear Regression: 34449393.944885224\nR^2 Score for Linear Regression: 0.7512646548852024\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**2. Random Forrest Model**","metadata":{}},{"cell_type":"code","source":"rf_model = RandomForestRegressor(n_estimators=100, random_state=42)\nrf_model.fit(train_X, train_y)","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:09:26.831205Z","iopub.execute_input":"2023-12-29T23:09:26.831555Z","iopub.status.idle":"2023-12-29T23:09:27.100730Z","shell.execute_reply.started":"2023-12-29T23:09:26.831531Z","shell.execute_reply":"2023-12-29T23:09:27.099698Z"},"trusted":true},"execution_count":213,"outputs":[{"execution_count":213,"output_type":"execute_result","data":{"text/plain":"RandomForestRegressor(random_state=42)","text/html":"<style>#sk-container-id-36 {color: black;background-color: white;}#sk-container-id-36 pre{padding: 0;}#sk-container-id-36 div.sk-toggleable {background-color: white;}#sk-container-id-36 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-36 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-36 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-36 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-36 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-36 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-36 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-36 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-36 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-36 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-36 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-36 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-36 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-36 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-36 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-36 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-36 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-36 div.sk-item {position: relative;z-index: 1;}#sk-container-id-36 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-36 div.sk-item::before, #sk-container-id-36 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-36 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-36 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-36 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-36 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-36 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-36 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-36 div.sk-label-container {text-align: center;}#sk-container-id-36 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-36 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-36\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" checked><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"y_pred_rf = rf_model.predict(test_X)","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:09:27.102288Z","iopub.execute_input":"2023-12-29T23:09:27.102853Z","iopub.status.idle":"2023-12-29T23:09:27.119028Z","shell.execute_reply.started":"2023-12-29T23:09:27.102829Z","shell.execute_reply":"2023-12-29T23:09:27.117843Z"},"trusted":true},"execution_count":214,"outputs":[]},{"cell_type":"code","source":"mse_rf = mean_squared_error(test_y, y_pred_rf)\nr2_rf = r2_score(test_y, y_pred_rf)","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:09:27.292372Z","iopub.execute_input":"2023-12-29T23:09:27.292710Z","iopub.status.idle":"2023-12-29T23:09:27.299463Z","shell.execute_reply.started":"2023-12-29T23:09:27.292686Z","shell.execute_reply":"2023-12-29T23:09:27.298402Z"},"trusted":true},"execution_count":215,"outputs":[]},{"cell_type":"code","source":"print(f'Mean Squared Error for Linear Regression: {mse_rf}')\nprint(f'R^2 Score for Linear Regression: {r2_rf}')","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:09:27.636453Z","iopub.execute_input":"2023-12-29T23:09:27.636827Z","iopub.status.idle":"2023-12-29T23:09:27.644093Z","shell.execute_reply.started":"2023-12-29T23:09:27.636800Z","shell.execute_reply":"2023-12-29T23:09:27.642321Z"},"trusted":true},"execution_count":216,"outputs":[{"name":"stdout","text":"Mean Squared Error for Linear Regression: 19008277.157574136\nR^2 Score for Linear Regression: 0.8627543234464099\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**3. Polynomial Regression Model**","metadata":{}},{"cell_type":"code","source":"degree = 5  # Change the degree as needed\npoly_features = PolynomialFeatures(degree=degree)\nX_poly_train = poly_features.fit_transform(train_X)\nX_poly_test = poly_features.transform(test_X)\n\n# Create a Polynomial Regression model\npoly_model = LinearRegression()\npoly_model.fit(train_X, train_y)","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:09:27.906452Z","iopub.execute_input":"2023-12-29T23:09:27.906814Z","iopub.status.idle":"2023-12-29T23:09:27.927386Z","shell.execute_reply.started":"2023-12-29T23:09:27.906786Z","shell.execute_reply":"2023-12-29T23:09:27.926505Z"},"trusted":true},"execution_count":217,"outputs":[{"execution_count":217,"output_type":"execute_result","data":{"text/plain":"LinearRegression()","text/html":"<style>#sk-container-id-37 {color: black;background-color: white;}#sk-container-id-37 pre{padding: 0;}#sk-container-id-37 div.sk-toggleable {background-color: white;}#sk-container-id-37 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-37 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-37 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-37 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-37 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-37 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-37 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-37 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-37 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-37 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-37 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-37 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-37 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-37 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-37 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-37 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-37 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-37 div.sk-item {position: relative;z-index: 1;}#sk-container-id-37 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-37 div.sk-item::before, #sk-container-id-37 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-37 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-37 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-37 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-37 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-37 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-37 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-37 div.sk-label-container {text-align: center;}#sk-container-id-37 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-37 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-37\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" checked><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"y_pred_poly = poly_model.predict(test_X)","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:09:28.119074Z","iopub.execute_input":"2023-12-29T23:09:28.119441Z","iopub.status.idle":"2023-12-29T23:09:28.125799Z","shell.execute_reply.started":"2023-12-29T23:09:28.119416Z","shell.execute_reply":"2023-12-29T23:09:28.124806Z"},"trusted":true},"execution_count":218,"outputs":[]},{"cell_type":"code","source":"mse_poly = mean_squared_error(test_y, y_pred_poly)\nr2_poly = r2_score(test_y, y_pred_poly)","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:09:28.519717Z","iopub.execute_input":"2023-12-29T23:09:28.520082Z","iopub.status.idle":"2023-12-29T23:09:28.526121Z","shell.execute_reply.started":"2023-12-29T23:09:28.520058Z","shell.execute_reply":"2023-12-29T23:09:28.524953Z"},"trusted":true},"execution_count":219,"outputs":[]},{"cell_type":"code","source":"print(f'Mean Squared Error for Linear Regression: {mse_poly}')\nprint(f'R^2 Score for Linear Regression: {r2_poly}')","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:09:28.904750Z","iopub.execute_input":"2023-12-29T23:09:28.905202Z","iopub.status.idle":"2023-12-29T23:09:28.910694Z","shell.execute_reply.started":"2023-12-29T23:09:28.905171Z","shell.execute_reply":"2023-12-29T23:09:28.909390Z"},"trusted":true},"execution_count":220,"outputs":[{"name":"stdout","text":"Mean Squared Error for Linear Regression: 34449393.944885224\nR^2 Score for Linear Regression: 0.7512646548852024\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**4. Decision Tree Model**","metadata":{}},{"cell_type":"code","source":"tree_model = DecisionTreeRegressor(max_depth=4, random_state=42)\ntree_model.fit(train_X, train_y)","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:09:29.310034Z","iopub.execute_input":"2023-12-29T23:09:29.310390Z","iopub.status.idle":"2023-12-29T23:09:29.321814Z","shell.execute_reply.started":"2023-12-29T23:09:29.310365Z","shell.execute_reply":"2023-12-29T23:09:29.320833Z"},"trusted":true},"execution_count":221,"outputs":[{"execution_count":221,"output_type":"execute_result","data":{"text/plain":"DecisionTreeRegressor(max_depth=4, random_state=42)","text/html":"<style>#sk-container-id-38 {color: black;background-color: white;}#sk-container-id-38 pre{padding: 0;}#sk-container-id-38 div.sk-toggleable {background-color: white;}#sk-container-id-38 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-38 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-38 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-38 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-38 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-38 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-38 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-38 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-38 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-38 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-38 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-38 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-38 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-38 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-38 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-38 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-38 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-38 div.sk-item {position: relative;z-index: 1;}#sk-container-id-38 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-38 div.sk-item::before, #sk-container-id-38 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-38 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-38 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-38 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-38 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-38 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-38 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-38 div.sk-label-container {text-align: center;}#sk-container-id-38 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-38 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-38\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=4, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" checked><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=4, random_state=42)</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"y_pred_tree = tree_model.predict(test_X)","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:09:29.649653Z","iopub.execute_input":"2023-12-29T23:09:29.650036Z","iopub.status.idle":"2023-12-29T23:09:29.658126Z","shell.execute_reply.started":"2023-12-29T23:09:29.650008Z","shell.execute_reply":"2023-12-29T23:09:29.657034Z"},"trusted":true},"execution_count":222,"outputs":[]},{"cell_type":"code","source":"mse_tree = mean_squared_error(test_y, y_pred_tree)\nr2_tree = r2_score(test_y, y_pred_tree)","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:09:30.037006Z","iopub.execute_input":"2023-12-29T23:09:30.037372Z","iopub.status.idle":"2023-12-29T23:09:30.043714Z","shell.execute_reply.started":"2023-12-29T23:09:30.037344Z","shell.execute_reply":"2023-12-29T23:09:30.042638Z"},"trusted":true},"execution_count":223,"outputs":[]},{"cell_type":"code","source":"print(f'Mean Squared Error for Linear Regression: {mse_tree}')\nprint(f'R^2 Score for Linear Regression: {r2_tree}')","metadata":{"execution":{"iopub.status.busy":"2023-12-29T23:09:30.774642Z","iopub.execute_input":"2023-12-29T23:09:30.775018Z","iopub.status.idle":"2023-12-29T23:09:30.779826Z","shell.execute_reply.started":"2023-12-29T23:09:30.774991Z","shell.execute_reply":"2023-12-29T23:09:30.778944Z"},"trusted":true},"execution_count":224,"outputs":[{"name":"stdout","text":"Mean Squared Error for Linear Regression: 18767477.202195182\nR^2 Score for Linear Regression: 0.8644929740624595\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# **6.Conclusions**\n* We can observe that, for this problem Decision Tree and Random Forest prove to be more efficient Regression models, compared to Linear and Polynomial Regression.\n* I thought Polynomial Regression should have worked well, but the R2 value, plateaued after a certain degree.","metadata":{}}]}