xgboostregressor-boston-house-pricing.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":3139,"sourceType":"datasetVersion","datasetId":1815}],"dockerImageVersionId":30184,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-02-26T00:06:24.980568Z","iopub.execute_input":"2024-02-26T00:06:24.981226Z","iopub.status.idle":"2024-02-26T00:06:25.013678Z","shell.execute_reply.started":"2024-02-26T00:06:24.981096Z","shell.execute_reply":"2024-02-26T00:06:25.013016Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/input/boston-house-prices/housing.csv\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### 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 import metrics","metadata":{"execution":{"iopub.status.busy":"2024-02-26T00:06:25.015205Z","iopub.execute_input":"2024-02-26T00:06:25.015641Z","iopub.status.idle":"2024-02-26T00:06:26.310895Z","shell.execute_reply.started":"2024-02-26T00:06:25.015608Z","shell.execute_reply":"2024-02-26T00:06:26.309632Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"# Importing the Boston Housing dataset\nfrom sklearn.datasets import load_boston\nboston = load_boston()","metadata":{"execution":{"iopub.status.busy":"2024-02-26T00:06:26.312769Z","iopub.execute_input":"2024-02-26T00:06:26.313128Z","iopub.status.idle":"2024-02-26T00:06:26.406369Z","shell.execute_reply.started":"2024-02-26T00:06:26.313071Z","shell.execute_reply":"2024-02-26T00:06:26.405349Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n\n    The Boston housing prices dataset has an ethical problem. You can refer to\n    the documentation of this function for further details.\n\n    The scikit-learn maintainers therefore strongly discourage the use of this\n    dataset unless the purpose of the code is to study and educate about\n    ethical issues in data science and machine learning.\n\n    In this special case, you can fetch the dataset from the original\n    source::\n\n        import pandas as pd\n        import numpy as np\n\n\n        data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n        raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n        target = raw_df.values[1::2, 2]\n\n    Alternative datasets include the California housing dataset (i.e.\n    :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n    dataset. You can load the datasets as follows::\n\n        from sklearn.datasets import fetch_california_housing\n        housing = fetch_california_housing()\n\n    for the California housing dataset and::\n\n        from sklearn.datasets import fetch_openml\n        housing = fetch_openml(name=\"house_prices\", as_frame=True)\n\n    for the Ames housing dataset.\n    \n  warnings.warn(msg, category=FutureWarning)\n","output_type":"stream"}]},{"cell_type":"code","source":"df = pd.DataFrame(boston.data, columns = boston.feature_names)\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2024-02-26T00:06:26.408778Z","iopub.execute_input":"2024-02-26T00:06:26.409392Z","iopub.status.idle":"2024-02-26T00:06:26.446867Z","shell.execute_reply.started":"2024-02-26T00:06:26.409343Z","shell.execute_reply":"2024-02-26T00:06:26.445647Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n\n   PTRATIO       B  LSTAT  \n0     15.3  396.90   4.98  \n1     17.8  396.90   9.14  \n2     17.8  392.83   4.03  \n3     18.7  394.63   2.94  \n4     18.7  396.90   5.33  ","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>CRIM</th>\n      <th>ZN</th>\n      <th>INDUS</th>\n      <th>CHAS</th>\n      <th>NOX</th>\n      <th>RM</th>\n      <th>AGE</th>\n      <th>DIS</th>\n      <th>RAD</th>\n      <th>TAX</th>\n      <th>PTRATIO</th>\n      <th>B</th>\n      <th>LSTAT</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.00632</td>\n      <td>18.0</td>\n      <td>2.31</td>\n      <td>0.0</td>\n      <td>0.538</td>\n      <td>6.575</td>\n      <td>65.2</td>\n      <td>4.0900</td>\n      <td>1.0</td>\n      <td>296.0</td>\n      <td>15.3</td>\n      <td>396.90</td>\n      <td>4.98</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.02731</td>\n      <td>0.0</td>\n      <td>7.07</td>\n      <td>0.0</td>\n      <td>0.469</td>\n      <td>6.421</td>\n      <td>78.9</td>\n      <td>4.9671</td>\n      <td>2.0</td>\n      <td>242.0</td>\n      <td>17.8</td>\n      <td>396.90</td>\n      <td>9.14</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.02729</td>\n      <td>0.0</td>\n      <td>7.07</td>\n      <td>0.0</td>\n      <td>0.469</td>\n      <td>7.185</td>\n      <td>61.1</td>\n      <td>4.9671</td>\n      <td>2.0</td>\n      <td>242.0</td>\n      <td>17.8</td>\n      <td>392.83</td>\n      <td>4.03</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.03237</td>\n      <td>0.0</td>\n      <td>2.18</td>\n      <td>0.0</td>\n      <td>0.458</td>\n      <td>6.998</td>\n      <td>45.8</td>\n      <td>6.0622</td>\n      <td>3.0</td>\n      <td>222.0</td>\n      <td>18.7</td>\n      <td>394.63</td>\n      <td>2.94</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.06905</td>\n      <td>0.0</td>\n      <td>2.18</td>\n      <td>0.0</td>\n      <td>0.458</td>\n      <td>7.147</td>\n      <td>54.2</td>\n      <td>6.0622</td>\n      <td>3.0</td>\n      <td>222.0</td>\n      <td>18.7</td>\n      <td>396.90</td>\n      <td>5.33</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"What do columns actually mean?\n\nCRIM per capita crime rate by town\n\nZN proportion of residential land zoned for lots over 25,000 sq.ft.\n\nINDUS proportion of non-retail business acres per town\n\nCHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\nNOX nitric oxides concentration (parts per 10 million)\n\nRM average number of rooms per dwelling\n\nAGE proportion of owner-occupied units built prior to 1940\n\nDIS weighted distances to five Boston employment centres\n\nRAD index of accessibility to radial highways\n\nTAX full-value property-tax rate per 10,000usd\n\nPTRATIO pupil-teacher ratio by town\n\nB 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n\nLSTAT % lower status of the population","metadata":{}},{"cell_type":"code","source":"x = df\ny = boston.target","metadata":{"execution":{"iopub.status.busy":"2024-02-26T00:06:26.447971Z","iopub.execute_input":"2024-02-26T00:06:26.448441Z","iopub.status.idle":"2024-02-26T00:06:26.452554Z","shell.execute_reply.started":"2024-02-26T00:06:26.448406Z","shell.execute_reply":"2024-02-26T00:06:26.451799Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"col_len = len(boston.feature_names)\n\nfeature = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT']\n           \nfor i in range(0, col_len):\n    x_i = df[feature[i]] \n    #print(x_i)\n    plt.bar(x_i, y)\n    plt.xlabel(feature[i])\n    plt.ylabel('House Price')\n    plt.show()\n\n","metadata":{"execution":{"iopub.status.busy":"2024-02-26T00:06:26.453614Z","iopub.execute_input":"2024-02-26T00:06:26.454358Z","iopub.status.idle":"2024-02-26T00:06:40.388468Z","shell.execute_reply.started":"2024-02-26T00:06:26.454305Z","shell.execute_reply":"2024-02-26T00:06:40.387641Z"},"trusted":true},"execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"df.describe()","metadata":{"execution":{"iopub.status.busy":"2024-02-26T00:06:40.389584Z","iopub.execute_input":"2024-02-26T00:06:40.390014Z","iopub.status.idle":"2024-02-26T00:06:40.446851Z","shell.execute_reply.started":"2024-02-26T00:06:40.389977Z","shell.execute_reply":"2024-02-26T00:06:40.446011Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\ncount  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \nmean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \nstd      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \nmin      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \nmax     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n\n              AGE         DIS         RAD         TAX     PTRATIO           B  \\\ncount  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \nmean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \nstd     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \nmin      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \nmax    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n\n            LSTAT  \ncount  506.000000  \nmean    12.653063  \nstd      7.141062  \nmin      1.730000  \n25%      6.950000  \n50%     11.360000  \n75%     16.955000  \nmax     37.970000  ","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>CRIM</th>\n      <th>ZN</th>\n      <th>INDUS</th>\n      <th>CHAS</th>\n      <th>NOX</th>\n      <th>RM</th>\n      <th>AGE</th>\n      <th>DIS</th>\n      <th>RAD</th>\n      <th>TAX</th>\n      <th>PTRATIO</th>\n      <th>B</th>\n      <th>LSTAT</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>506.000000</td>\n      <td>506.000000</td>\n      <td>506.000000</td>\n      <td>506.000000</td>\n      <td>506.000000</td>\n      <td>506.000000</td>\n      <td>506.000000</td>\n      <td>506.000000</td>\n      <td>506.000000</td>\n      <td>506.000000</td>\n      <td>506.000000</td>\n      <td>506.000000</td>\n      <td>506.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>3.613524</td>\n      <td>11.363636</td>\n      <td>11.136779</td>\n      <td>0.069170</td>\n      <td>0.554695</td>\n      <td>6.284634</td>\n      <td>68.574901</td>\n      <td>3.795043</td>\n      <td>9.549407</td>\n      <td>408.237154</td>\n      <td>18.455534</td>\n      <td>356.674032</td>\n      <td>12.653063</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>8.601545</td>\n      <td>23.322453</td>\n      <td>6.860353</td>\n      <td>0.253994</td>\n      <td>0.115878</td>\n      <td>0.702617</td>\n      <td>28.148861</td>\n      <td>2.105710</td>\n      <td>8.707259</td>\n      <td>168.537116</td>\n      <td>2.164946</td>\n      <td>91.294864</td>\n      <td>7.141062</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.006320</td>\n      <td>0.000000</td>\n      <td>0.460000</td>\n      <td>0.000000</td>\n      <td>0.385000</td>\n      <td>3.561000</td>\n      <td>2.900000</td>\n      <td>1.129600</td>\n      <td>1.000000</td>\n      <td>187.000000</td>\n      <td>12.600000</td>\n      <td>0.320000</td>\n      <td>1.730000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.082045</td>\n      <td>0.000000</td>\n      <td>5.190000</td>\n      <td>0.000000</td>\n      <td>0.449000</td>\n      <td>5.885500</td>\n      <td>45.025000</td>\n      <td>2.100175</td>\n      <td>4.000000</td>\n      <td>279.000000</td>\n      <td>17.400000</td>\n      <td>375.377500</td>\n      <td>6.950000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.256510</td>\n      <td>0.000000</td>\n      <td>9.690000</td>\n      <td>0.000000</td>\n      <td>0.538000</td>\n      <td>6.208500</td>\n      <td>77.500000</td>\n      <td>3.207450</td>\n      <td>5.000000</td>\n      <td>330.000000</td>\n      <td>19.050000</td>\n      <td>391.440000</td>\n      <td>11.360000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>3.677083</td>\n      <td>12.500000</td>\n      <td>18.100000</td>\n      <td>0.000000</td>\n      <td>0.624000</td>\n      <td>6.623500</td>\n      <td>94.075000</td>\n      <td>5.188425</td>\n      <td>24.000000</td>\n      <td>666.000000</td>\n      <td>20.200000</td>\n      <td>396.225000</td>\n      <td>16.955000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>88.976200</td>\n      <td>100.000000</td>\n      <td>27.740000</td>\n      <td>1.000000</td>\n      <td>0.871000</td>\n      <td>8.780000</td>\n      <td>100.000000</td>\n      <td>12.126500</td>\n      <td>24.000000</td>\n      <td>711.000000</td>\n      <td>22.000000</td>\n      <td>396.900000</td>\n      <td>37.970000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"correlation = df.corr()","metadata":{"execution":{"iopub.status.busy":"2024-02-26T00:06:40.448360Z","iopub.execute_input":"2024-02-26T00:06:40.448791Z","iopub.status.idle":"2024-02-26T00:06:40.455280Z","shell.execute_reply.started":"2024-02-26T00:06:40.448634Z","shell.execute_reply":"2024-02-26T00:06:40.454414Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"markdown","source":"### Construct a heatmap to undestand Correlations with columns\n","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(12,8))\n_ = sns.heatmap(correlation, cbar=True, annot=True, fmt='.1f', annot_kws={'size':8}, cmap='Greens')","metadata":{"execution":{"iopub.status.busy":"2024-02-26T00:06:40.456435Z","iopub.execute_input":"2024-02-26T00:06:40.456648Z","iopub.status.idle":"2024-02-26T00:06:41.531390Z","shell.execute_reply.started":"2024-02-26T00:06:40.456622Z","shell.execute_reply":"2024-02-26T00:06:41.530729Z"},"trusted":true},"execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 864x576 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)","metadata":{"execution":{"iopub.status.busy":"2024-02-26T00:06:41.534001Z","iopub.execute_input":"2024-02-26T00:06:41.534258Z","iopub.status.idle":"2024-02-26T00:06:41.540490Z","shell.execute_reply.started":"2024-02-26T00:06:41.534226Z","shell.execute_reply":"2024-02-26T00:06:41.539876Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"markdown","source":"### Model Training\n\n- **XGBoost Regressor**","metadata":{}},{"cell_type":"code","source":"## Load the Model\nfrom xgboost import XGBRegressor\nxgb_model = XGBRegressor()","metadata":{"execution":{"iopub.status.busy":"2024-02-26T00:06:41.541765Z","iopub.execute_input":"2024-02-26T00:06:41.542312Z","iopub.status.idle":"2024-02-26T00:06:41.648889Z","shell.execute_reply.started":"2024-02-26T00:06:41.542269Z","shell.execute_reply":"2024-02-26T00:06:41.648069Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"## Training the model\nxgb_model.fit(x_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2024-02-26T00:06:41.649988Z","iopub.execute_input":"2024-02-26T00:06:41.650385Z","iopub.status.idle":"2024-02-26T00:06:41.996429Z","shell.execute_reply.started":"2024-02-26T00:06:41.650348Z","shell.execute_reply":"2024-02-26T00:06:41.995198Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None,\n             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n             early_stopping_rounds=None, enable_categorical=False,\n             eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',\n             importance_type=None, interaction_constraints='',\n             learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n             missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=0,\n             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,\n             reg_lambda=1, ...)"},"metadata":{}}]},{"cell_type":"markdown","source":"## Evaluation Accuracy","metadata":{}},{"cell_type":"markdown","source":"### **Evaluation for the Train data**","metadata":{}},{"cell_type":"code","source":"## Accuracy for prediction on training data\npred_data = xgb_model.predict(x_train)","metadata":{"execution":{"iopub.status.busy":"2024-02-26T00:06:41.997639Z","iopub.execute_input":"2024-02-26T00:06:41.997917Z","iopub.status.idle":"2024-02-26T00:06:42.009796Z","shell.execute_reply.started":"2024-02-26T00:06:41.997877Z","shell.execute_reply":"2024-02-26T00:06:42.008730Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"# R squared Error \nscore_1 = metrics.r2_score(y_train, pred_data)\n\n# MEA - mean absolute error\nscore_2 = metrics.mean_absolute_error(y_train, pred_data)\n\n# MSE - Mean squared error\nscore_3 = metrics.mean_squared_error(y_train, pred_data)\n\n# RMSE - Root mean squared error\nscore_4 = np.sqrt(metrics.mean_squared_error(y_train, pred_data))\n\nprint(\"R squared Error\", score_1)\nprint(\"Mean Absolute Error\", score_2)\nprint('MSE:', score_3)\nprint('RMSE:', score_4)","metadata":{"execution":{"iopub.status.busy":"2024-02-26T00:06:42.013796Z","iopub.execute_input":"2024-02-26T00:06:42.014072Z","iopub.status.idle":"2024-02-26T00:06:42.024300Z","shell.execute_reply.started":"2024-02-26T00:06:42.014042Z","shell.execute_reply":"2024-02-26T00:06:42.023343Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"R squared Error 0.9999953622164942\nMean Absolute Error 0.013965251658222466\nMSE: 0.0004029000393923251\nRMSE: 0.02007237004920757\n","output_type":"stream"}]},{"cell_type":"code","source":"_ = plt.scatter(y_train, pred_data)\nplt.xlabel('Actual price')\nplt.ylabel('Predicted price')\nplt.title('Actual Price vs Predicted Data')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-02-26T00:06:42.025304Z","iopub.execute_input":"2024-02-26T00:06:42.025509Z","iopub.status.idle":"2024-02-26T00:06:42.209432Z","shell.execute_reply.started":"2024-02-26T00:06:42.025482Z","shell.execute_reply":"2024-02-26T00:06:42.208503Z"},"trusted":true},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"### **Evaluation for the Test Data**","metadata":{}},{"cell_type":"code","source":"#Predicting Test data with the model\npred_data = xgb_model.predict(x_test)","metadata":{"execution":{"iopub.status.busy":"2024-02-26T00:06:42.211197Z","iopub.execute_input":"2024-02-26T00:06:42.211544Z","iopub.status.idle":"2024-02-26T00:06:42.221551Z","shell.execute_reply.started":"2024-02-26T00:06:42.211500Z","shell.execute_reply":"2024-02-26T00:06:42.220926Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"# R squared Error \nscore_1 = metrics.r2_score(y_test, pred_data)\n\n# MEA - mean absolute error\nscore_2 = metrics.mean_absolute_error(y_test, pred_data)\n\n# MSE - Mean squared error\nscore_3 = metrics.mean_squared_error(y_test, pred_data)\n\n# RMSE - Root mean squared error\nscore_4 = np.sqrt(metrics.mean_squared_error(y_test, pred_data))\n\nprint(\"R squared Error\", score_1)\nprint(\"Mean Absolute Error\", score_2)\nprint('MSE:', score_3)\nprint('RMSE:', score_4)","metadata":{"execution":{"iopub.status.busy":"2024-02-26T00:06:42.222666Z","iopub.execute_input":"2024-02-26T00:06:42.223038Z","iopub.status.idle":"2024-02-26T00:06:42.236059Z","shell.execute_reply.started":"2024-02-26T00:06:42.223007Z","shell.execute_reply":"2024-02-26T00:06:42.235206Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"R squared Error 0.9105388132305845\nMean Absolute Error 1.9574996602301502\nMSE: 6.560527271813469\nRMSE: 2.561352625433185\n","output_type":"stream"}]},{"cell_type":"code","source":"_ = plt.scatter(y_test, pred_data)\nplt.xlabel('Actual price')\nplt.ylabel('Predicted price')\nplt.title('Actual Price vs Predicted Data')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-02-26T00:06:42.237307Z","iopub.execute_input":"2024-02-26T00:06:42.237548Z","iopub.status.idle":"2024-02-26T00:06:42.427710Z","shell.execute_reply.started":"2024-02-26T00:06:42.237519Z","shell.execute_reply":"2024-02-26T00:06:42.426857Z"},"trusted":true},"execution_count":18,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAX4AAAEWCAYAAABhffzLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAo0klEQVR4nO3deZhcdZ3v8fcnTQMdQJslMtAQwgxOMIgkmotIXAAXXFgirgyOuCLXZUQxCs5ciQuPmQcVueMdnbjBCMMiYEQcRSSg4oKTkLAJjLiwNFsYaFmMkOV7/zi/SqortZzq7lPV1efzep5+Umf/1en0t059z+98f4oIzMysPKZ1uwFmZtZZDvxmZiXjwG9mVjIO/GZmJePAb2ZWMg78ZmYl48BvhZO0WNK5E7Sv4yT9aCL21UsknS3pM+n1iyTd3qHjhqR9OnEs6xwH/hKQdI2kRyRtk3P9t0m6tuh2pWMdImmjpMclPSbpdklvb7R+RJwXEa/oRNvaJemPktam9/JACtbbT/RxIuJnETE7R3sK/T2m/1d/Sb+3RyWtlHRK3v9naR/+YOkCB/4pTtIs4EVAAEd1tzUN3RsR2wNPAz4GfFXSnNqVJG3V8Za178j0Xp4LzAf+qXaFHnkfeb0/InYAdgNOBt4M/KckdbdZ1owD/9T3VuBXwNnA8dULJO0p6VJJayT9j6QvSXoW8BXgBenKdSSte42kd1VtO+pqUtJZku6uuvJ7UbsNjcwy4BFgTjrGzyWdKel/gMV1jrufpCslPZyusj+e5k9LV5+/S+/tIkk71TuupFslHVE1vVU6J8+VtK2kc9M+RiT9l6Rdc7yXYeAHwLPTPkPS+yT9FvhtmneEpNVpv7+Q9JyqNsyTdH26mr4Q2LZq2SGS7qmabuf3uI2kz0m6K52vr0gaqNrXIkn3SbpX0jtavc+q9/tERFxDdnHxAuA1aX8HSvpleo/3pbZtnZb9NG1+Q2rjmyTtKOny9F4eSa/3yNsOy8eBf+p7K3Be+jm8ErQk9QGXA3cCs4Ah4IKIuBU4EfhlRGwfEYM5j/NfwFxgJ+A/gG9L2rbpFjVSsH4tMAjclGY/H/g9sCtwes36OwA/Bn4I7A7sA1yVFn8AWAi8JC17BPh/DQ59PnBs1fThwEMRcT3Zh+XTgT2BncnOzdoc72VP4NXAqqrZC9P7mSNpHvAN4D1pv/8GXJYC89bAMuBbZOfz28DrGhyn3d/jEuBvyX5X+6T1P5H29UrgI8DLgWcCL2v1PmtFxF3ACrJvmQAbgA8Bu5B9ILwUeG9a98VpnQNSGy8ki0nfBPYCZpKd6y+12w5rISL8M0V/gBcC64Bd0vRtwIfS6xcAa4Ct6mz3NuDamnnXAO9qtk7N+o+Q/UEDLAbObbDeIcBGYAR4GFgNvLnqGHc1ahtZsF7VYL+3Ai+tmt4tnYt673cf4DFgepo+D/hEev0O4BfAc3Kc7z8Cj6f3cifwr8BAWhbAYVXrfhn4dM32t5N9UL0YuBdQ1bJfAJ+pOmf3tPt7BAQ8AfxN1bwXAH9Ir78BLKla9rep3fs0eL+j/k9Uzb8A+GqDbU4CvlM13XD/aflc4JFu/h1NxZ+plGu0LR0P/CgiHkrT/5HmnUl2BXtnRKyfiANJ+gjwTrKr6yDL1++Sc/N7I6LR1/m7m2y3J/C7Bsv2Ar4jaWPVvA1k3xyGq1eMiDsk3QocKel7ZOmKeWnxt9JxLpA0CJwL/GNErGtw3IUR8eMc72Uv4HhJH6iatzWbz99wpMiX3Nlgn+38HmcA04GVVSl4AX3p9e7AyhzHbGWI7IMKSX8LfIHsfsd0YKuaY4wiaTrZ/89XAjum2TtI6ouIDWNsj9VwqmeKSnnbNwIvkXS/pPvJvnIfIOkAsiA0s8GNxnolW58g+8Ot+KuqY70I+Gg63o6RpRX+RBZUxqtZ+di7gb9usuxVETFY9bNtZLn3eirpnqOB30TEHQARsS4iPhkRc4CDgSPI0mdjUf1e7gZOr2nf9Ig4H7gPGKq5QTqzyfvM+3t8iCx1sl/VMZ8e2c1o0nH3zHHMhlKK63nAz9KsL5N903xmRDwN+DjN/1+cDMwGnp/Wr6SDfLN4AjnwT10Lya5w55B9XZ4LPIvsD/KtwK/J/tCXSNou3cRckLZ9ANijchMuWQ0cI2m6su5376xatgOwnpRykPQJsiv+ol0O7CbppJQb30HS89OyrwCnS9oLQNIMSUc32dcFwCuA/032zYi03aGS9k+59EfJ0kUb6++iLV8FTpT0fGW2k/SadN/il2Tn8x8k9Us6BjiwwX5y/x4jYmM67pmSnpHe35Ckw9P6FwFvkzQnXXmflvfNpP8XLwG+m9r0n2nRDmTn7XFJ+5Kd32oPMPrDeweyD6cRZTfjc7fB8nPgn7qOB74ZEXdFxP2VH7IbZceRXUEdSZbfvgu4B3hT2nY5cAtwv6RKmuhM4CmyP9RzyPLgFVeQ3WD9b7L0wF9onqKZEBHxGNmNyCOB+8l6yxyaFp8FXAb8SNJjZD2bnl9vP2lf95EF3IOBC6sW/RVwMVnwuhX4CVn6Z7xtXwG8m+z38QhwB1lOnoh4CjgmTT9M9nu5tMF+NtDe7/Fj6Vi/kvQo2c3x2WlfPwC+mLa7I/3bypfS+X0gbXsJ8Mr0IQPZzeK/I7uH8lVGn1vI7v+ck3r9vDHtY4Ds28mvyP5f2QTT6DSimZlNdb7iNzMrGQd+M7OSceA3MysZB34zs5LpiQe4dtlll5g1a1a3m2Fm1lNWrlz5UETMqJ3fE4F/1qxZrFixotvNMDPrKZLqPn3tVI+ZWck48JuZlYwDv5lZyTjwm5mVjAO/mVnJ9ESvHjOzqWDZqmHOuOJ27h1Zy+6DAyw6fDYL5w11vB0O/GZmHbBs1TCnXnoTa9dl48kMj6zl1EuzEUY7Hfyd6jEz64Azrrh9U9CvWLtuA2dccXvH2+IrfjMrhW6nWe4dWdvW/CL5it/MprxKmmV4ZG02oHFKsyxb1Wgkzom3++BAW/OL5MBvZlPeZEizLDp8NgP9faPmDfT3sejw2XXXX7ZqmAVLlrP3Kd9nwZLlE/oh5VSPmU15kyHNUkkr5Uk3FX0j2IHfzKa83QcHGK4T5DudZlk4byhX4G72DWUiAn/hqR5JfZJWSbo8TZ8t6Q+SVqefuUW3wczKrd00S7cV/Q2lE1f8HwRuBZ5WNW9RRFzcgWObmbWVZpkMiv6GUmjgl7QH8BrgdODDRR7LzKyZvGmWyWDR4bNH5fhhYr+hFJ3q+SLwUWBjzfzTJd0o6UxJ29TbUNIJklZIWrFmzZqCm2lmNnksnDfEZ4/Zn6HBAQQMDQ7w2WP2n7APLkXEhOxoix1LRwCvjoj3SjoE+EhEHCFpN+B+YGtgKfC7iPhUs33Nnz8/PAKXmVl7JK2MiPm184u84l8AHCXpj8AFwGGSzo2I+yLzJPBN4MAC22BmZjUKC/wRcWpE7BERs4A3A8sj4i3pih9JAhYCNxfVBjMz21I3+vGfJ2kGIGA1cGIX2mBmVlodCfwRcQ1wTXp9WCeOaWbWy4osKucnd83MJpmiSza4SJuZ2SRTdFE5B34zs0mm6JINDvxmZpNM0bX7HfjNzCaZoovK+eaumdkkU3RROQd+M7NJqMiick71mJmVjAO/mVnJOPCbmZWMA7+ZWck48JuZlYwDv5lZybg7p5nZJOTqnGZmJeLqnGZmJdPz1Tkl9UlaJenyNL23pOsk3SHpQklbF90GM7NeMhWqc34QuLVq+p+BMyNiH+AR4J0daIOZWc/o6eqckvYAXgN8LU0LOAy4OK1yDtmA62ZmlvR6dc4vAh8FdkjTOwMjEbE+Td8D1L1TIekE4ASAmTNnFttKM7NJpGerc0o6AngwIlZKOqTd7SNiKbAUYP78+TGxrTMzm9yKrM5Z5BX/AuAoSa8GtgWeBpwFDEraKl317wEMF9gGMzOrUViOPyJOjYg9ImIW8GZgeUQcB1wNvD6tdjzw3aLaYGZmW+pGP/6PAR+WdAdZzv/rXWiDmVlpdeTJ3Yi4Brgmvf49cGAnjmtmZlvyk7tmZiXjwG9mVjIO/GZmJePAb2ZWMg78ZmYl48BvZlYyDvxmZiXjwG9mVjIO/GZmJePAb2ZWMg78ZmYl48BvZlYyDvxmZiXjwG9mVjIO/GZmJVPkmLvbAj8FtknHuTgiTpN0NvAS4E9p1bdFxOqi2mFm+S1bNVzYAN82eRQ5EMuTwGER8bikfuBaST9IyxZFxMUFHtvM2rRs1TCnXnoTa9dtAGB4ZC2nXnoTgIP/FFPkmLsREY+nyf70E0Udz8zG54wrbt8U9CvWrtvAGVfc3qUWWVEKzfFL6pO0GngQuDIirkuLTpd0o6QzJW3TYNsTJK2QtGLNmjVFNtPMgHtH1rY133pXoYE/IjZExFxgD+BASc8GTgX2Bf4XsBPZ4Ov1tl0aEfMjYv6MGTOKbKaZAbsPDrQ133pXR3r1RMQIcDXwyoi4L6WBngS+iQdeN5sUDt23/gVWo/nWuwoL/JJmSBpMrweAlwO3SdotzROwELi5qDaYWX5X31Y/pdpovvWuInv17AacI6mP7APmooi4XNJySTMAAauBEwtsg5nl5Bx/eRQW+CPiRmBenfmHFXVMMxu73QcHGK4T5J3jn3pypXokvVDS29PrGZL2LrZZZtZpiw6fzUB/36h5A/19LDp8dpdaZEVpGfglnUbW8+bUNKsfOLfIRplZ8ZatGmbBkuXsfcr3WbBkOQCfPWZ/hgYHEDA0OMBnj9nfD29NQXlSPa8lS9lcDxAR90raodBWmfWwXih70Ogp3c8esz8/P8XZ2KkuT6rnqYgI0lO3krYrtklmvasSUIdH1hJsDqjLVg13u2mj+CndcssT+C+S9G/AoKR3Az8Gvlpss8x6U68EVPfgKbeWqZ6I+JyklwOPArOBT0TElYW3zKwH9UpAdQ+ecstzc3dv4GcRsSgiPkJWZXNW4S0z60G9UvbAPXjKLU+q59vAxqrpDWme2aRU21ulk/n1XgmoC+cNuQdPieXp1bNVRDxVmYiIpyRtXWCbzMas2zXlK8eY7L16IGvrZGyXFS9P4F8j6aiIuAxA0tHAQ8U2y2xsmt1cLTrI1XbjPPNNcx1YbVLKE/hPBM6T9CWy+jp3A28ttFVmY9Stm6vd/qZh1o6WOf6I+F1EHATMAZ4VEQdHxB3FN82sfd26udor3TjNoMkVv6S3RMS5kj5cMx+AiPhCwW0za9uiw2ePuvKGztxc7ZVunGbQPNVTeULX5RmsZ3Tr5qr7xVsvaRj4I+LfUi39RyPizA62yWxcutFbpVvfNMzGommOPyI2AMd2qC1mPcv94q2X5OnV8/PUo+dC4InKzIi4vtlGkrYFfgpsk45zcUSclp4EvgDYGVgJ/H31cwJmvarT3zR6oQqoTU55Av/c9O+nquYF0Kp265PAYRHxuKR+slIPPwA+DJwZERdI+grwTuDL7TXbrNzcfdTGI0+RtkPHsuNUyvnxNNmffiofGH+X5p8DLMaB36wt3XxQzXpfniJtO0v6v5Kul7RS0lmSds6zc0l9klYDDwJXAr8DRiJifVrlHqDu/1JJJ0haIWnFmjVrcr0Zs7Jw91EbjzxF2i4A1gCvA16fXl+YZ+cRsSEi5gJ7AAcC++ZtWEQsjYj5ETF/xowZeTczK4VeqQJqk1OewL9bRHw6Iv6Qfj4D7NrOQSJiBLgaeAHZgC6VFNMewOQamsisB/RKFVCbnPIE/h9JerOkaennjcAVrTaSNEPSYHo9ALwcuJXsA+D1abXjge+OqeVmJebuozYeyu7BNllBeozsKd7KnaQ+NnfrjIh4WoPtnkN287aP7APmooj4lKS/Jksf7QSsAt4SEU82a8P8+fNjxYoV+d6R2RTjbps2VpJWRsT82vl5evWMqWRDRNwIzKsz//dk+X4za8HdNq0IeVI9ZtYlrvppRXDgN5vE3G3TiuDAbzaJudumFaFh4Je0U7OfTjbSrKzcbdOK0Ozm7kqyEgsCZgKPpNeDwF3A3kU3zqzXjaVHTu02r3veEFfftsa9emzCNKvHvzeApK8C34mI/0zTrwIWdqR1ZuPQ7W6QY+mRU2+bS1YOu4++Tag8Of6DKkEfICJ+ABxcXJPMxq8SQIdH1hJsDrrLVnXuQfGx9MhxLx7rhDxlme+V9E/AuWn6OODe4ppkNn6ToXplsx45jb6NuBePdUKewH8scBrwHbKc/0/xqFw2yeUJoEWnghqNwzs4vb9hCshj91ontEz1RMTDEfFB4IUR8dyIOCkiHu5A28zGrFU3yE6kghr1yImg4bcR9+KxTshTj/9gSb8hK7CGpAMk/WvhLTMbh1YBtBO59EaF1P60dl3d9e8dWevia9YReVI9ZwKHA5cBRMQNkl5caKvMxmnhvCFW3Pkw5193Nxsi6JN43fM2j4nbqVx6vXF4z7ji9qbpnE6P3Wvlk+vJ3Yi4u2bWhrormk0Sy1YNc8nKYTak6rMbIrhk5fCmVE43n4h1Ose6LU/gv1vSwUBI6pf0EVLax2yyapXK6WbwdTrHui1PqudE4CyysXGHgR8B7y2yUWbjlacr5dp1G+iT2BDBUIcf8HI6x7opT+CfHRHHVc+QtAD4ebONJO0J/DvZMI0BLI2IsyQtBt5NNnYvwMerHxAzmwiNukU+fWB0V8oNEZuu9OsF4m4//WtWhDypnn/JOa/WeuDkiJgDHAS8T9KctOzMiJibfhz0bcI1SuVIjbtS1pqoLp/LVg2zYMly9j7l+yxYsryjTw+b1dPwil/SC8hKM8yQ9OGqRU8jG06xqYi4D7gvvX5M0q1k6SKzwlWuymuv1j904eq669dLDU3E078eQcsmo2apnq2B7dM61cMvPsrmwdJzkTSLbBjG64AFwPslvRVYQfat4JF29meWx1i6UlabiC6fk6F0hFmtZtU5fwL8RNLZEXHnWA8gaXvgEuCkiHhU0peBT5Pl/T8NfB54R53tTgBOAJg5c+ZYD289qMi8+qLDZ4+6AofGvXkmonyCa+/YZJQnx/81SYOVCUk7Sroiz84l9ZMF/fMi4lKAiHggIjZExEbgqzQYeD0ilkbE/IiYP2PGjDyHsymg6FIKC+cN8brnDdEnAWzxYFe1Zl0+8+btPYKWTUZ5Av8uETFSmUhpmWe02kiSgK8Dt0bEF6rm71a12muBm3O31qa8okspLFs1zIW/vnvUg10X/vruuoG7UX97IPeHkx/WsskoT3fOjZJmRsRdAJL2IkvTtLIA+HvgJkmr07yPA8dKmpv28UfgPW222aawolMjiy+7hXUbR//3XbcxWHzZLXWv+uvdJ1iwZHnuvH2jm8zO71s35Qn8/whcK+knZEMvvoiUe28mIq5N69dy901rqOiyxCMNCqQ1ml9Pux9OfljLJpuWgT8ifijpuWR98SG7SftQsc2ysmrn5mu9m8BQ/NW1a+Zbr2vWj3/fiLgtBX3YPOrWzJT6ub745lnZ5E2N1Osfv+jiGyDYlMqp12d+u637eOKpLWsM7ji9P3cb2/lwMpuMml3xn0xWWuHzdZYFcFghLbLSy5MaqXcTeN2GLW89Vefel60a5qn1G7dYp2+aOO3I/dpqX6UNzttbL1JEnvu03TV//vxYsWJFt5thk8jep3w/Vw+DCgHTUkG2WoMD/aw+7RUT1jazyULSyoiYXzu/WarnmGY7rPTLt3KrzbMfuu8Mrr5tTeFXwo3y7I0E1A36QMMRscymqmapniPTv88gq9mzPE0fCvwCcOCfwvI8PVsvz37ur+7atLxZXZrxfmDUy7OPlW/KWtk0K9nwdgBJPwLmpKJrlQewzu5I66wr8hYWq5dnr1Wvf/t4PjAq2+c5dh6+KWtllOfJ3T0rQT95AHDxnCks79OzeR+qql0v7wfGyRfdsMXTsNUlHcZqmvDIV1ZqeR7guirV5jk/Tb8J+HFxTbJuy/uAUt48e20qJe8HxoaILa78J+JKv0/ijDce4IBvpdXyij8i3g98BTgg/SyNiA8U3TDrnryFxerVoalVm0pZtmqYaar3QHd9td80mn1otGpLxbqNMWG1f8x6UZ4rfoDrgcci4seSpkvaISIeK7Jh1j15H1Cq15+93k1ayOrbDI+sReQr9FStOtg3+pZRGTP3k9+7hUf+3LqXjssiW5m1DPyS3k1Wm2cn4G/IRtH6CvDSYptm3dLOA0qtHraqvZE7lqdGBqueqm32oVRpS3WPoUZ9992Tx8oszxX/+8hq5l8HEBG/ldSyLLP1tokqLDYROfnauL3NVtM27XPH6f2cduR+o9pa3fbaDx5wTx6zPIH/yYh4SikvK2krxnbhZlNUsz7/E5FSGVm7jgVLlnPovjO4ZOXwqCD+l3VblmCo5vIKZlvKE/h/IunjwICklwPvBb5XbLOsV7Tq89/uE7aNDI+s5bxf3bXFFUejOvhFDt9o1uta1upJI2m9C3gFWffnK4CvRQeL/LhWz+SSJ4c+NDjAz085rG6qpQhDgwOjbjDXfjMY6O9zn30rnbZr9aSN+oBbImJfsvFx2zngnsC/A7uSpYaWRsRZknYCLgRmkY3A9cY0nKP1gNpA3qj+TSXFU51qmYgr/3oEm/bd7jcDszJq2o8/IjYAt0say5O664GTI2IO2SAu75M0BzgFuCoinglclaatR+S9WVvda2bhvCF+fsphfPFNc+nvy9+Hv57aret1D230VdRdOM0yeXL8OwK3SPo18ERlZkQc1WyjVObhvvT6MUm3knUFPRo4JK12DnAN8LF2G27dkSd4VveaqU4LDU7vZ6tp2lQ3f8fp/bzmObtx/nV3N/zmUE3AcQfNHPWcQDvfItyF0yyTJ/D/n/EeRNIsYB5Zl9Bdq2r/3E+WCqq3zQmksX1nznRpoMmiUbDtk9gYMepGam1aqPbBqr+s28j8vXZi/l47tbwPUAn6n1m4/6YPk2ZBv/abgLtwmm3W8OaupG2BE4F9gJuAr0fE+rYPIG0P/AQ4PSIulTQSEYNVyx+JiB2b7cM3dyePRv3iKzdO89z4rVZ9EzhPmeY8N4vrfTNwrx4ro7Hc3D0HWAf8DHgVMAf4YJsH7QcuAc6rGrjlAUm7RcR9qcTzg+3s07qrWb/4vDd+q1XfBM4TmPPcYwjgMwv3b7kvs7JqFvjnRMT+AJK+Dvy6nR2nbqBfB26NiC9ULboMOB5Ykv79blstto6r1yf+56dsOeTyWJ7SbTfvnucew5Bz+WZNNevVsykhO5YUD7AA+HvgMEmr08+ryQL+yyX9FnhZmrZJqrr+fbD5Aa3aOvlA2901BW3n3Vt9UDiXb9Zasyv+AyQ9ml6L7MndR9PriIinNdtxRFzLlr3vKlzgrcPG+iRrs0FZarfvy5HTrxbUH2GrmXpF2io3coecyzfLpdnQi/mKm9ukl3coxXoapVbqXd23E/RhbCkZ194xG7+89fith7Vz1V6rUfdNkX2gVG8/1Ea/+v5pGnNKZqIqh5qVVZ4xd63HtRpKcdmqYRYsWc7ep3yfBUuWj8rfLzp8dt18XcAWo1jVG5Grf5qY3j/6v9ngQD9nvMFDH5p1i6/4S6DRVfvugwMt00AL5w1x0oWr6+639gPFaRiz3uDAXwLNRq3KkwZqlMKp18PGaRizyc+pnhJYOG+Izx6zP0ODA4gskFeetG2VBoL6KRx3mzTrXb7iL4lGV+LN0kCwuRvo2nUbNnXXHG+3SQ+SYtZdvuIvuWZX89UPb0HWXbN6YPOxaOeBMDMrhgN/yTVLAzXL/1dr1iuoVt59mllxnOqxhmmgPPn/dh8Oy7NPMyuWA3+JtMqt1y4fnN6/RQ19GN2bp92Hw1rdUzCz4jnVUxKtcuv1lj/+l/VbDJVY25un3St49xAy6z4H/pJolVuvt3zdxmC7rbeqm/+vaHSl3mh+s3sKZtYZTvX0uOphCJt1t2x1Zd5o+Z/WrmP1aa9oePxGD4cduu8MFixZvkW7Go2sZWad48DfI+rl54G6I14Nj6zlpAtXs/iyW1h81H4snDfUMrfeqqxDo3sD9co0HLrvDC5ZOVy3Xef+6q5N+26nSqiZTZyGY+5OJmUfc7fROLfbbDWNkbVb3nytVhkPF2g5Vm695a973tCoIF67XT2VK/28KuPumtnEajTmbmE5fknfkPSgpJur5i2WNFwzIpe10Cg/3yroV9ar9LBplltvtPzq29a07Hdf24+/3ZG43JXTrLOKTPWcDXwJ+Pea+WdGxOcKPO6UM97AmHdA83rLP9SiMme9fvyVEbHycldOs84q7Io/In4KPFzU/sukWWBsNLZl3u3Hum1lfr1vI5GzXeCunGbd0I3unO+XdGNKBe3YaCVJJ0haIWnFmjVrOtm+Sade3/eKPEG2Uncnb1mFVseuDtaNvo1UxsCFbCxe0vRbDprprpxmXdbpXj1fBj5NFhc+DXweeEe9FSNiKbAUspu7nWrgZFTdc6Ze/jxoPND5QBr9aqxj7rYaXKVRbyDfsDWbvArt1SNpFnB5RDy7nWW1yt6rp9rep3y/Yf68f5pYt3H00v4+sf02W9UtvTDW4FzdvXNwej+P/2X9qOO26vVjZp3R8V49DRqxW9Xka4GbG61r9TXKuQ8NDrD9tlt+gVu3IeoGfRjbTePa0g6P/HkdKBtH1+kbs95QWKpH0vnAIcAuku4BTgMOkTSXLDvxR+A9RR1/qmo2jGKjHjiNjOWmb93SDhuC7bbZqukTvmY2eRQW+CPi2Dqzv17U8aa66vTK0wf62bZ/GiN/Xjcq597oHsDgQD9Prt9Y98Oi3WM3SjO5L75Z73DJhh5Q21d+ZO06Bvr7OPNNc0elVBp9G1h81H5A4xu07Ry7EffFN+sdDvw9oNGTuydduJozrrh9UxBv1QNnLHn3eseu5b74Zr3Fgb8HNEuj1HbNrPf07XgGN292bIErbJr1IAf+LmknGDfqK1/RbMSrdodGzHts99M3610eiKULWo2GVavZk7sVja7Mxzu4uUfMMpt6HPi7oN1gXF05s5FGN1fHO7i5R8wym3qc6umCsQTjSu6+Ud38RlfgEzG4eauqnmbWW3zF3wXtjlNbrd0rcKdqzKyWr/i7oNnTt3m0cwXeqounmZWPA38BWvXY6XQwdqrGzKo58E+wvN0nHYzNrFuc459g4+0+aWZWNF/xT7A8PXbG8yStmdl4OfBPsIZP2SobROXpA/088dR61m3I6ly2+yStmdl4OdUzwRYdPpv+vi1HwY3IBiEYWbtuU9CvcCrIzDqpsMCfBlN/UNLNVfN2knSlpN+mfxsOtt6rFs4bYrut2/8i5Xr2ZtYpRV7xnw28smbeKcBVEfFM4Ko0PeX8aW39oQ6bcT17M+uUwgJ/RPwUeLhm9tHAOen1OcDCoo7fTe0GcT9Ja2ad1Okc/64RcV96fT+wa4eP3xGtqmn2TxM7Tvfg5GbWHV3r1RMRIanREK5IOgE4AWDmzJkda9dEqH0yd3B6PxFZCsjdN82s2zod+B+QtFtE3CdpN+DBRitGxFJgKcD8+fMbfkBMVn4y18wmq06nei4Djk+vjwe+2+Hjm5mVXmFX/JLOBw4BdpF0D3AasAS4SNI7gTuBNxZ1/LHyU7VmNtUVFvgj4tgGi15a1DHHa9mqYRZdfMOop2oXXXwD4KdqzWzq8JO7VT75vVu2eKp23Ybgk9+7pUstMjObeK7VU+WRP9d/8KrR/HY5jWRmk4EDf4fkrdNvZlY0p3qqDA70tzW/Ha7Tb2aThQN/lcVH7Uf/tNGVNfunicVH7Tfufeep029m1gkO/FUWzhvijDccwNDgwKZyCme84YAJScU0qt/j4mxm1mnO8dco6onbRYfPHpXjBxdnM7PucODvkNr6Pe7VY2bd4sDfQa7fY2aTwZQN/O4zb2ZW35QM/O4zb2bW2JTs1eM+82ZmjU3JwO8+82ZmjU3JwO8+82ZmjU3JwF9vzFv3mTczy0zJm7vuM29m1lhXAr+kPwKPARuA9RExf6KP4T7zZmb1dfOK/9CIeKiLxzczK6UpmeM3M7PGuhX4A/iRpJWSTqi3gqQTJK2QtGLNmjUdbp6Z2dTVrcD/woh4LvAq4H2SXly7QkQsjYj5ETF/xowZnW+hmdkU1ZXAHxHD6d8Hge8AB3ajHWZmZaSI6OwBpe2AaRHxWHp9JfCpiPhhk23WAHd2qo0F2QXwzezNfD4287kYzedjtPGcj70iYouUSTd69ewKfEdS5fj/0SzoA9RreK+RtKKIbqu9yudjM5+L0Xw+RivifHQ88EfE74EDOn1cMzPLuDunmVnJOPB3ztJuN2CS8fnYzOdiNJ+P0Sb8fHT85q6ZmXWXr/jNzErGgd/MrGQc+Asg6RuSHpR0c9W8nSRdKem36d8du9nGTpG0p6SrJf1G0i2SPpjml/V8bCvp15JuSOfjk2n+3pKuk3SHpAslbd3ttnaKpD5JqyRdnqbLfC7+KOkmSaslrUjzJvxvxYG/GGcDr6yZdwpwVUQ8E7gqTZfBeuDkiJgDHERWomMO5T0fTwKHRcQBwFzglZIOAv4ZODMi9gEeAd7ZvSZ23AeBW6umy3wuIKtcPLeq7/6E/6048BcgIn4KPFwz+2jgnPT6HGBhJ9vULRFxX0Rcn14/RvYHPkR5z0dExONpsj/9BHAYcHGaX5rzIWkP4DXA19K0KOm5aGLC/1Yc+Dtn14i4L72+n+wJ5lKRNAuYB1xHic9HSm2sBh4kK1nyO2AkItanVe4h+3Asgy8CHwU2pumdKe+5gPqViyf8b2VKDr042UVESCpVP1pJ2wOXACdFxKOpZAdQvvMRERuAuZIGyYoU7tvdFnWHpCOAByNipaRDutycyeKFETEs6RnAlZJuq144UX8rvuLvnAck7QaQ/n2wy+3pGEn9ZEH/vIi4NM0u7fmoiIgR4GrgBcCgpMqF2B7AcLfa1UELgKPSUKwXkKV4zqKc5wJoWLl4wv9WHPg75zLg+PT6eOC7XWxLx6Sc7deBWyPiC1WLyno+ZqQrfSQNAC8nu+9xNfD6tFopzkdEnBoRe0TELODNwPKIOI4SngvIKhdL2qHyGngFcDMF/K34yd0CSDofOISsnOoDwGnAMuAiYCZZiek3RkTtDeApR9ILgZ8BN7E5j/txsjx/Gc/Hc8hu0PWRXXhdFBGfkvTXZFe9OwGrgLdExJPda2lnpVTPRyLiiLKei/S+v5MmK5WLT5e0MxP8t+LAb2ZWMk71mJmVjAO/mVnJOPCbmZWMA7+ZWck48JuZlYwDv005khZKCkktn4iVdJKk6eM41tskfWms26d9/GI825u1y4HfpqJjgWvTv62cBIw58I9H5enUiDi4G8e38nLgtykl1QR6IVkp3zdXze+T9DlJN0u6UdIHJP0DsDtwtaSr03qPV23zeklnp9dHphrxqyT9WFLTQlmSFkv6lqRfpjrq707zD5H0M0mXAb+pc8yPpXrsN0hakub9jaQfpsJdP8vzTcasGRdps6nmaOCHEfHfkv5H0vMiYiVwAjALmBsR6yXtFBEPS/owWf3zh1rs91rgoFQk611kFSVPbrHNc8jGINgOWCXp+2n+c4FnR8QfqleW9KrU/udHxJ8l7ZQWLQVOjIjfSno+8K9kdW3MxsSB36aaY8kKfUH22P+xwErgZcBXKuV+x/DI+x7AhalI1tbAH1qsD/DdiFgLrE3fKA4ERoBf1wb95GXANyPiz5U2pm8wBwPfrqpouk2bbTcbxYHfpox0hXwYsH8qXdsHhKRFbeymuobJtlWv/wX4QkRclurKLG5zX9XTT7TRnmlk9enntrGNWVPO8dtU8nrgWxGxV0TMiog9ya7MX0Q24Ml7KjdUq9IojwE7VO3jAUnPkjQNeG3V/KezuTzw8eRztLIxdncmK9r3Xy3WvxJ4e6WXUUpHPQr8QdIb0jxJOiDn8c3qcuC3qeRYNlc3rLgkzf8acBdwo6QbgL9Ly5cCP6zc3CUbz/Ry4BfAfVX7WUyWblkJtLofUHEjWYnhXwGfjoh7m60cET8kK8G7Io3Q9ZG06Djgnandt5DdBzAbM1fnNCuApMXA4xHxuW63xayWr/jNzErGV/xmZiXjK34zs5Jx4DczKxkHfjOzknHgNzMrGQd+M7OS+f9s/UuSGaC9xgAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"pred_data","metadata":{"execution":{"iopub.status.busy":"2024-02-26T00:06:42.428828Z","iopub.execute_input":"2024-02-26T00:06:42.429126Z","iopub.status.idle":"2024-02-26T00:06:42.435573Z","shell.execute_reply.started":"2024-02-26T00:06:42.429091Z","shell.execute_reply":"2024-02-26T00:06:42.434987Z"},"trusted":true},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"array([23.25328  , 30.024755 , 15.632249 , 23.313478 , 17.775118 ,\n       21.142563 , 20.19583  , 15.010124 , 21.23614  , 22.242369 ,\n       20.457346 , 19.209145 ,  8.551788 , 21.210636 , 20.696491 ,\n       26.74365  , 18.824339 , 10.525872 , 45.68885  , 14.116162 ,\n       26.618996 , 24.94542  , 13.3510275, 20.87231  , 15.400073 ,\n       15.636547 , 22.324673 , 12.777009 , 20.726126 , 22.56401  ,\n       20.346395 , 22.303246 , 18.523277 , 21.764612 , 15.568828 ,\n       15.683646 , 33.073547 , 19.115112 , 21.955132 , 22.399914 ,\n       18.998787 , 31.328337 , 43.464993 , 18.20766  , 22.09233  ,\n       14.353467 , 14.607512 , 22.716745 , 19.700527 , 27.072327 ,\n       22.579268 , 35.133675 , 16.241447 , 25.214682 , 46.013332 ,\n       21.89786  , 15.043295 , 32.93268  , 20.53731  , 16.568089 ,\n       24.07178  , 34.34796  , 28.542194 , 16.977676 , 25.867334 ,\n       15.649837 , 13.039615 , 23.00082  , 27.26897  , 15.414835 ,\n       21.546648 , 31.72919  , 10.665012 , 20.770847 , 21.848396 ,\n        6.475782 , 20.939093 , 46.59454  , 12.456056 ,  8.739085 ,\n       22.215406 , 13.390212 , 20.454681 , 10.45914  , 19.722834 ,\n       27.327946 , 16.254663 , 23.860172 , 25.414312 , 17.06042  ,\n       22.9362   ,  8.106883 , 19.001764 , 18.869307 , 24.129864 ,\n       19.66075  , 40.517284 , 13.981451 , 11.416717 , 15.428753 ,\n       19.41982  , 24.281776 ], dtype=float32)"},"metadata":{}}]},{"cell_type":"code","source":"data = {\n    'price' : pred_data\n}\nsubmission = pd.DataFrame(data)","metadata":{"execution":{"iopub.status.busy":"2024-02-26T00:06:42.436569Z","iopub.execute_input":"2024-02-26T00:06:42.437390Z","iopub.status.idle":"2024-02-26T00:06:42.446285Z","shell.execute_reply.started":"2024-02-26T00:06:42.437347Z","shell.execute_reply":"2024-02-26T00:06:42.445413Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"submission.to_csv('submission_xgb.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2024-02-26T00:06:42.447527Z","iopub.execute_input":"2024-02-26T00:06:42.447757Z","iopub.status.idle":"2024-02-26T00:06:42.459089Z","shell.execute_reply.started":"2024-02-26T00:06:42.447728Z","shell.execute_reply":"2024-02-26T00:06:42.458098Z"},"trusted":true},"execution_count":21,"outputs":[]}]}