ipl-fods-model.ipynb

{"cells":[{"metadata":{"trusted":true},"cell_type":"code","source":"import pandas as pd\nimport numpy as np","execution_count":1,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df = pd.read_csv(\"../input/ipl-fods/final.csv\")\nprint(df.shape)\ndf.head()","execution_count":2,"outputs":[{"output_type":"stream","text":"(92742, 11)\n","name":"stdout"},{"output_type":"execute_result","execution_count":2,"data":{"text/plain":"   match_id  inning         batting_team                 bowling_team  overs  \\\n0         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore    0.1   \n1         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore    0.2   \n2         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore    0.3   \n3         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore    0.4   \n4         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore    0.5   \n\n   runs  wickets                                      venue  total_runs_y  \\\n0     0        0  Rajiv Gandhi International Stadium, Uppal           207   \n1     0        0  Rajiv Gandhi International Stadium, Uppal           207   \n2     4        0  Rajiv Gandhi International Stadium, Uppal           207   \n3     4        0  Rajiv Gandhi International Stadium, Uppal           207   \n4     6        0  Rajiv Gandhi International Stadium, Uppal           207   \n\n   runs_last_5  wickets_last_5  \n0          0.0             0.0  \n1          0.0             0.0  \n2          4.0             0.0  \n3          4.0             0.0  \n4          6.0             0.0  ","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>match_id</th>\n      <th>inning</th>\n      <th>batting_team</th>\n      <th>bowling_team</th>\n      <th>overs</th>\n      <th>runs</th>\n      <th>wickets</th>\n      <th>venue</th>\n      <th>total_runs_y</th>\n      <th>runs_last_5</th>\n      <th>wickets_last_5</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Sunrisers Hyderabad</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>0.1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Rajiv Gandhi International Stadium, Uppal</td>\n      <td>207</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Sunrisers Hyderabad</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>0.2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Rajiv Gandhi International Stadium, Uppal</td>\n      <td>207</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Sunrisers Hyderabad</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>0.3</td>\n      <td>4</td>\n      <td>0</td>\n      <td>Rajiv Gandhi International Stadium, Uppal</td>\n      <td>207</td>\n      <td>4.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Sunrisers Hyderabad</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>0.4</td>\n      <td>4</td>\n      <td>0</td>\n      <td>Rajiv Gandhi International Stadium, Uppal</td>\n      <td>207</td>\n      <td>4.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Sunrisers Hyderabad</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>0.5</td>\n      <td>6</td>\n      <td>0</td>\n      <td>Rajiv Gandhi International Stadium, Uppal</td>\n      <td>207</td>\n      <td>6.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df = df[df['batting_team'].isin(['Sunrisers Hyderabad', 'Royal Challengers Bangalore',\n       'Mumbai Indians','Kolkata Knight Riders', 'Kings XI Punjab', 'Delhi Capitals',\n       'Chennai Super Kings', 'Rajasthan Royals']) & df['bowling_team'].isin(['Sunrisers Hyderabad', 'Royal Challengers Bangalore',\n       'Mumbai Indians','Kolkata Knight Riders', 'Kings XI Punjab', 'Delhi Capitals',\n       'Chennai Super Kings', 'Rajasthan Royals'])]","execution_count":3,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.shape","execution_count":4,"outputs":[{"output_type":"execute_result","execution_count":4,"data":{"text/plain":"(78672, 11)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# first let's remove unnecessary variables\ndf.drop(columns = [\"match_id\",\"inning\"],inplace=True)\ndf.head(10)","execution_count":5,"outputs":[{"output_type":"execute_result","execution_count":5,"data":{"text/plain":"          batting_team                 bowling_team  overs  runs  wickets  \\\n0  Sunrisers Hyderabad  Royal Challengers Bangalore    0.1     0        0   \n1  Sunrisers Hyderabad  Royal Challengers Bangalore    0.2     0        0   \n2  Sunrisers Hyderabad  Royal Challengers Bangalore    0.3     4        0   \n3  Sunrisers Hyderabad  Royal Challengers Bangalore    0.4     4        0   \n4  Sunrisers Hyderabad  Royal Challengers Bangalore    0.5     6        0   \n5  Sunrisers Hyderabad  Royal Challengers Bangalore    0.6     6        0   \n6  Sunrisers Hyderabad  Royal Challengers Bangalore    0.7     7        0   \n7  Sunrisers Hyderabad  Royal Challengers Bangalore    1.1     8        0   \n8  Sunrisers Hyderabad  Royal Challengers Bangalore    1.2    12        0   \n9  Sunrisers Hyderabad  Royal Challengers Bangalore    1.3    13        0   \n\n                                       venue  total_runs_y  runs_last_5  \\\n0  Rajiv Gandhi International Stadium, Uppal           207          0.0   \n1  Rajiv Gandhi International Stadium, Uppal           207          0.0   \n2  Rajiv Gandhi International Stadium, Uppal           207          4.0   \n3  Rajiv Gandhi International Stadium, Uppal           207          4.0   \n4  Rajiv Gandhi International Stadium, Uppal           207          6.0   \n5  Rajiv Gandhi International Stadium, Uppal           207          6.0   \n6  Rajiv Gandhi International Stadium, Uppal           207          7.0   \n7  Rajiv Gandhi International Stadium, Uppal           207          8.0   \n8  Rajiv Gandhi International Stadium, Uppal           207         12.0   \n9  Rajiv Gandhi International Stadium, Uppal           207         13.0   \n\n   wickets_last_5  \n0             0.0  \n1             0.0  \n2             0.0  \n3             0.0  \n4             0.0  \n5             0.0  \n6             0.0  \n7             0.0  \n8             0.0  \n9             0.0  ","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>batting_team</th>\n      <th>bowling_team</th>\n      <th>overs</th>\n      <th>runs</th>\n      <th>wickets</th>\n      <th>venue</th>\n      <th>total_runs_y</th>\n      <th>runs_last_5</th>\n      <th>wickets_last_5</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sunrisers Hyderabad</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>0.1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Rajiv Gandhi International Stadium, Uppal</td>\n      <td>207</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Sunrisers Hyderabad</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>0.2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Rajiv Gandhi International Stadium, Uppal</td>\n      <td>207</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Sunrisers Hyderabad</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>0.3</td>\n      <td>4</td>\n      <td>0</td>\n      <td>Rajiv Gandhi International Stadium, Uppal</td>\n      <td>207</td>\n      <td>4.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Sunrisers Hyderabad</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>0.4</td>\n      <td>4</td>\n      <td>0</td>\n      <td>Rajiv Gandhi International Stadium, Uppal</td>\n      <td>207</td>\n      <td>4.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Sunrisers Hyderabad</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>0.5</td>\n      <td>6</td>\n      <td>0</td>\n      <td>Rajiv Gandhi International Stadium, Uppal</td>\n      <td>207</td>\n      <td>6.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Sunrisers Hyderabad</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>0.6</td>\n      <td>6</td>\n      <td>0</td>\n      <td>Rajiv Gandhi International Stadium, Uppal</td>\n      <td>207</td>\n      <td>6.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Sunrisers Hyderabad</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>0.7</td>\n      <td>7</td>\n      <td>0</td>\n      <td>Rajiv Gandhi International Stadium, Uppal</td>\n      <td>207</td>\n      <td>7.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Sunrisers Hyderabad</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>1.1</td>\n      <td>8</td>\n      <td>0</td>\n      <td>Rajiv Gandhi International Stadium, Uppal</td>\n      <td>207</td>\n      <td>8.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Sunrisers Hyderabad</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>1.2</td>\n      <td>12</td>\n      <td>0</td>\n      <td>Rajiv Gandhi International Stadium, Uppal</td>\n      <td>207</td>\n      <td>12.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Sunrisers Hyderabad</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>1.3</td>\n      <td>13</td>\n      <td>0</td>\n      <td>Rajiv Gandhi International Stadium, Uppal</td>\n      <td>207</td>\n      <td>13.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# for prediction we want at least 5 overs data so that's here we train our model on above 5 overs data\ndf = df[df.overs > 5]","execution_count":6,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.shape","execution_count":8,"outputs":[{"output_type":"execute_result","execution_count":8,"data":{"text/plain":"(58624, 9)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# let's convert text data into lower case\ndf.venue = df.venue.str.lower()\ndf.batting_team = df.batting_team.str.lower()\ndf.bowling_team = df.bowling_team.str.lower()","execution_count":7,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Get correlation of all the features of the dataset\ncorr_matrix = df.corr()\ntop_corr_features = corr_matrix.index\n\n# Plotting the heatmap\nplt.figure(figsize=(13,10))\ng = sns.heatmap(data=df[top_corr_features].corr(), annot=True, cmap='RdYlGn')\n","execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 936x720 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.venue.nunique()","execution_count":13,"outputs":[{"output_type":"execute_result","execution_count":13,"data":{"text/plain":"30"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"encoded_df = pd.get_dummies(data=df, columns=['batting_team', 'bowling_team'],drop_first=True)\nencoded_df.drop('venue',axis=1,inplace=True)\nencoded_df.columns","execution_count":16,"outputs":[{"output_type":"execute_result","execution_count":16,"data":{"text/plain":"Index(['overs', 'runs', 'wickets', 'total_runs_y', 'runs_last_5',\n       'wickets_last_5', 'batting_team_delhi capitals',\n       'batting_team_kings xi punjab', 'batting_team_kolkata knight riders',\n       'batting_team_mumbai indians', 'batting_team_rajasthan royals',\n       'batting_team_royal challengers bangalore',\n       'batting_team_sunrisers hyderabad', 'bowling_team_delhi capitals',\n       'bowling_team_kings xi punjab', 'bowling_team_kolkata knight riders',\n       'bowling_team_mumbai indians', 'bowling_team_rajasthan royals',\n       'bowling_team_royal challengers bangalore',\n       'bowling_team_sunrisers hyderabad'],\n      dtype='object')"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(encoded_df.shape)\nencoded_df.head()","execution_count":18,"outputs":[{"output_type":"stream","text":"(58624, 20)\n","name":"stdout"},{"output_type":"execute_result","execution_count":18,"data":{"text/plain":"    overs  runs  wickets  total_runs_y  runs_last_5  wickets_last_5  \\\n32    5.1    46        1           207         42.0             1.0   \n33    5.2    50        1           207         46.0             1.0   \n34    5.3    50        1           207         44.0             1.0   \n35    5.4    54        1           207         48.0             1.0   \n36    5.5    58        1           207         51.0             1.0   \n\n    batting_team_delhi capitals  batting_team_kings xi punjab  \\\n32                            0                             0   \n33                            0                             0   \n34                            0                             0   \n35                            0                             0   \n36                            0                             0   \n\n    batting_team_kolkata knight riders  batting_team_mumbai indians  \\\n32                                   0                            0   \n33                                   0                            0   \n34                                   0                            0   \n35                                   0                            0   \n36                                   0                            0   \n\n    batting_team_rajasthan royals  batting_team_royal challengers bangalore  \\\n32                              0                                         0   \n33                              0                                         0   \n34                              0                                         0   \n35                              0                                         0   \n36                              0                                         0   \n\n    batting_team_sunrisers hyderabad  bowling_team_delhi capitals  \\\n32                                 1                            0   \n33                                 1                            0   \n34                                 1                            0   \n35                                 1                            0   \n36                                 1                            0   \n\n    bowling_team_kings xi punjab  bowling_team_kolkata knight riders  \\\n32                             0                                   0   \n33                             0                                   0   \n34                             0                                   0   \n35                             0                                   0   \n36                             0                                   0   \n\n    bowling_team_mumbai indians  bowling_team_rajasthan royals  \\\n32                            0                              0   \n33                            0                              0   \n34                            0                              0   \n35                            0                              0   \n36                            0                              0   \n\n    bowling_team_royal challengers bangalore  bowling_team_sunrisers hyderabad  \n32                                         1                                 0  \n33                                         1                                 0  \n34                                         1                                 0  \n35                                         1                                 0  \n36                                         1                                 0  ","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>overs</th>\n      <th>runs</th>\n      <th>wickets</th>\n      <th>total_runs_y</th>\n      <th>runs_last_5</th>\n      <th>wickets_last_5</th>\n      <th>batting_team_delhi capitals</th>\n      <th>batting_team_kings xi punjab</th>\n      <th>batting_team_kolkata knight riders</th>\n      <th>batting_team_mumbai indians</th>\n      <th>batting_team_rajasthan royals</th>\n      <th>batting_team_royal challengers bangalore</th>\n      <th>batting_team_sunrisers hyderabad</th>\n      <th>bowling_team_delhi capitals</th>\n      <th>bowling_team_kings xi punjab</th>\n      <th>bowling_team_kolkata knight riders</th>\n      <th>bowling_team_mumbai indians</th>\n      <th>bowling_team_rajasthan royals</th>\n      <th>bowling_team_royal challengers bangalore</th>\n      <th>bowling_team_sunrisers hyderabad</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>32</th>\n      <td>5.1</td>\n      <td>46</td>\n      <td>1</td>\n      <td>207</td>\n      <td>42.0</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>5.2</td>\n      <td>50</td>\n      <td>1</td>\n      <td>207</td>\n      <td>46.0</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>5.3</td>\n      <td>50</td>\n      <td>1</td>\n      <td>207</td>\n      <td>44.0</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>5.4</td>\n      <td>54</td>\n      <td>1</td>\n      <td>207</td>\n      <td>48.0</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>5.5</td>\n      <td>58</td>\n      <td>1</td>\n      <td>207</td>\n      <td>51.0</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.preprocessing import StandardScaler\nscaler = StandardScaler()\ndf_scaled = scaler.fit_transform(encoded_df)","execution_count":19,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X = encoded_df.drop(columns = \"total_runs_y\")\ny = encoded_df.total_runs_y","execution_count":22,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# let's divide our data into training and testing data set\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)","execution_count":23,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Linear Regression\n\nfrom sklearn.linear_model import LinearRegression\n\nlinear_regressor = LinearRegression()\nlinear_regressor.fit(X_train, y_train)","execution_count":41,"outputs":[{"output_type":"execute_result","execution_count":41,"data":{"text/plain":"LinearRegression()"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.metrics import mean_squared_error as mse\n\ny_pred_lr = linear_regressor.predict(X_test)\n\nprint(\"---- Linear Regression - Model Evaluation ----\")\nprint(\"Root Mean Squared Error (RMSE): {}\".format(np.sqrt(mse(y_test, y_pred_lr))))\nprint(\"R-Square:\",linear_regressor.score(X_test, y_test))","execution_count":42,"outputs":[{"output_type":"stream","text":"---- Linear Regression - Model Evaluation ----\nRoot Mean Squared Error (RMSE): 18.193890193075656\nR-Square: 0.6257814435741789\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.tree import DecisionTreeRegressor\ndecision_regressor = DecisionTreeRegressor()\ndecision_regressor.fit(X_train,y_train)","execution_count":43,"outputs":[{"output_type":"execute_result","execution_count":43,"data":{"text/plain":"DecisionTreeRegressor()"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_pred_dt = decision_regressor.predict(X_test)\n\nprint(\"---- Decision Tree Regression - Model Evaluation ----\")\nprint(\"Root Mean Squared Error (RMSE): {}\".format(np.sqrt(mse(y_test, y_pred_dt))))","execution_count":44,"outputs":[{"output_type":"stream","text":"---- Decision Tree Regression - Model Evaluation ----\nRoot Mean Squared Error (RMSE): 13.410951671431654\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Random Forest Regression Model\nfrom sklearn.ensemble import RandomForestRegressor\nrandom_regressor = RandomForestRegressor()\nrandom_regressor.fit(X_train,y_train)","execution_count":45,"outputs":[{"output_type":"execute_result","execution_count":45,"data":{"text/plain":"RandomForestRegressor()"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_pred_rf = random_regressor.predict(X_test)\n\nprint(\"---- Random Forest Regression - Model Evaluation ----\")\nprint(\"Root Mean Squared Error (RMSE): {}\".format(np.sqrt(mse(y_test, y_pred_rf))))","execution_count":47,"outputs":[{"output_type":"stream","text":"---- Random Forest Regression - Model Evaluation ----\nRoot Mean Squared Error (RMSE): 9.201397463325474\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# AdaBoost Model using Random Forest Regressor as the base learner\nfrom sklearn.ensemble import AdaBoostRegressor\nadb_regressor = AdaBoostRegressor(base_estimator=random_regressor, n_estimators=100)\nadb_regressor.fit(X_train, y_train)","execution_count":48,"outputs":[{"output_type":"execute_result","execution_count":48,"data":{"text/plain":"AdaBoostRegressor(base_estimator=RandomForestRegressor(), n_estimators=100)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_pred_adb = adb_regressor.predict(X_test)\n\nprint(\"---- AdaBoost Regression - Model Evaluation ----\")\nprint(\"Root Mean Squared Error (RMSE): {}\".format(np.sqrt(mse(y_test, y_pred_adb))))","execution_count":49,"outputs":[{"output_type":"stream","text":"---- AdaBoost Regression - Model Evaluation ----\nRoot Mean Squared Error (RMSE): 11.976026865063771\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"![image.png](attachment:image.png)","attachments":{"image.png":{"image/png":"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"}}},{"metadata":{"trusted":true},"cell_type":"code","source":"def predict_score(batting_team='Chennai Super Kings', bowling_team='Mumbai Indians', overs=5.1, runs=50, wickets=0, runs_in_prev_5=50, wickets_in_prev_5=0):\n    temp_array = list()\n\n  # Batting Team\n    if batting_team == 'Chennai Super Kings':\n        temp_array = temp_array + [0,0,0,0,0,0,0]\n    elif batting_team == 'Delhi Capitals':\n        temp_array = temp_array + [1,0,0,0,0,0,0]\n    elif batting_team == 'Kings XI Punjab':\n        temp_array = temp_array + [0,1,0,0,0,0,0]\n    elif batting_team == 'Kolkata Knight Riders':\n        temp_array = temp_array + [0,0,1,0,0,0,0]\n    elif batting_team == 'Mumbai Indians':\n        temp_array = temp_array + [0,0,0,1,0,0,0]\n    elif batting_team == 'Rajasthan Royals':\n        temp_array = temp_array + [0,0,0,0,1,0,0]\n    elif batting_team == 'Royal Challengers Bangalore':\n        temp_array = temp_array + [0,0,0,0,0,1,0]\n    elif batting_team == 'Sunrisers Hyderabad':\n        temp_array = temp_array + [0,0,0,0,0,0,1]\n\n  # Bowling Team\n    if bowling_team == 'Chennai Super Kings':\n        temp_array = temp_array + [0,0,0,0,0,0,0]\n    elif bowling_team == 'Delhi Capitals':\n        temp_array = temp_array + [1,0,0,0,0,0,0]\n    elif bowling_team == 'Kings XI Punjab':\n        temp_array = temp_array + [0,1,0,0,0,0,0]\n    elif bowling_team == 'Kolkata Knight Riders':\n        temp_array = temp_array + [0,0,1,0,0,0,0]\n    elif bowling_team == 'Mumbai Indians':\n        temp_array = temp_array + [0,0,0,1,0,0,0]\n    elif bowling_team == 'Rajasthan Royals':\n        temp_array = temp_array + [0,0,0,0,1,0,0]\n    elif bowling_team == 'Royal Challengers Bangalore':\n        temp_array = temp_array + [0,0,0,0,0,1,0]\n    elif bowling_team == 'Sunrisers Hyderabad':\n        temp_array = temp_array + [0,0,0,0,0,0,1]\n\n  # Overs, Runs, Wickets, Runs_in_prev_5, Wickets_in_prev_5\n    temp_array = [overs, runs, wickets, runs_in_prev_5, wickets_in_prev_5]+temp_array\n\n  # Converting into numpy array\n    temp_array = np.array([temp_array])\n\n  # Prediction\n    return list([int(linear_regressor.predict(temp_array)[0]),int(random_regressor.predict(temp_array)[0]),int(adb_regressor.predict(temp_array)[0])])","execution_count":76,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Prediction 1**\n- Teams: Sunrisers Hyderabad vs. Delhi Capitals\n- First Innings final score: 155/7"},{"metadata":{"trusted":true},"cell_type":"code","source":"final_score = predict_score(batting_team='Delhi Capitals', bowling_team='Sunrisers Hyderabad', overs=11.5, runs=98, wickets=3, runs_in_prev_5=41, wickets_in_prev_5=1)\n# print(\"The final predicted score (range): {} to {}\".format(final_score-10, final_score+5))\nregressors = [\"Linear\",\"Random Forest\",\"Adaboost\"]\nfor i in range(len(final_score)):\n    print(regressors[i],\"--> b/w\",final_score[i]-10,\"to\",final_score[i]+5)","execution_count":82,"outputs":[{"output_type":"stream","text":"Linear --> b/w 161 to 176\nRandom Forest --> b/w 157 to 172\nAdaboost --> b/w 159 to 174\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"**Prediction 2**\n- Teams: Delhi Capitals vs. Chennai Super Kings\n- First Innings final score: 147/9"},{"metadata":{"trusted":true},"cell_type":"code","source":"final_score = predict_score(batting_team='Delhi Capitals', bowling_team='Chennai Super Kings', overs=10.2, runs=68, wickets=3, runs_in_prev_5=29, wickets_in_prev_5=1)\n# print(\"The final predicted score (range): {} to {}\".format(final_score-10, final_score+5))\nregressors = [\"Linear\",\"Random Forest\",\"Adaboost\"]\nfor i in range(len(final_score)):\n    print(regressors[i],\"--> b/w\",final_score[i]-10,\"to\",final_score[i]+5)","execution_count":84,"outputs":[{"output_type":"stream","text":"Linear --> b/w 139 to 154\nRandom Forest --> b/w 146 to 161\nAdaboost --> b/w 145 to 160\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"**Prediction 3**\n- Teams: Rajasthan Royals vs. Chennai Super Kings\n- First Innings final score: 151/7"},{"metadata":{"trusted":true},"cell_type":"code","source":"final_score = predict_score(batting_team='Rajasthan Royals', bowling_team='Chennai Super Kings', overs=13.3, runs=92, wickets=5, runs_in_prev_5=27, wickets_in_prev_5=2)\nregressors = [\"Linear\",\"Random Forest\",\"Adaboost\"]\nfor i in range(len(final_score)):\n    print(regressors[i],\"--> b/w\",final_score[i]-10,\"to\",final_score[i]+5)","execution_count":85,"outputs":[{"output_type":"stream","text":"Linear --> b/w 129 to 144\nRandom Forest --> b/w 134 to 149\nAdaboost --> b/w 132 to 147\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"**Prediction 4**\n- Teams: Mumbai Indians vs. Kings XI Punjab\n- First Innings final score: 176/7"},{"metadata":{"trusted":true},"cell_type":"code","source":"final_score = predict_score(batting_team='Mumbai Indians', bowling_team='Kings XI Punjab', overs=12.3, runs=113, wickets=2, runs_in_prev_5=55, wickets_in_prev_5=0)\nregressors = [\"Linear\",\"Random Forest\",\"Adaboost\"]\nfor i in range(len(final_score)):\n    print(regressors[i],\"--> b/w\",final_score[i]-10,\"to\",final_score[i]+5)","execution_count":86,"outputs":[{"output_type":"stream","text":"Linear --> b/w 181 to 196\nRandom Forest --> b/w 191 to 206\nAdaboost --> b/w 185 to 200\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"import pickle\n\nwith open(\"ipl.pickle\", \"wb\")as f:\n    pickle.dump(final_model, f)","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"import json\n\nfeatures = {\"columns\": X.columns.to_list(), \"team\": list(team), \"venue\": list(venue)}\n\nwith open(\"features.json\", \"w\") as f:\n    json.dump(features, f)","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":4}