AI4GOOD

TASK 3 target = 'ATM RATING' from sklearn.ensemble import RandomForestClassifier , GradientBoostingClassifier from sklearn.tree import DecisionTreeClassifier from xgboost import XGBClassifier from lightgbm import LGBMClassifier from sklearn.linear_model import LogisticRegression from sklearn.metrics import roc_auc_score , accuracy_score from sklearn.preprocessing import MinMaxScaler from sklearn.cluster import KMeans from sklearn.decomposition import PCA evaluate_accuracy = make_scorer ( accuracy_score ) log_reg = LogisticRegression () decision_tree_clf = DecisionTreeClassifier () rf_clf = RandomForestClassifier () gbm_clf = GradientBoostingClassifier () xgb_clf = XGBClassifier () lgb_clf = LGBMClassifier () # dataset[FSET],dataset[target] dataset_cat = pd . get_dummies ( dataset [ exploration_dict [ 'possible_categorical_features...

TASK 2 : - BUILDING REGRESSION MODELS TO PREDICT "AMOUNT WITHDRAWN" evaluate_mae = make_scorer ( mean_absolute_error ) # selected_columns_top50 = dataset[selected_columns].corr()[target].dropna().sort_values(ascending = False)[:50].keys().values X = dataset_fin . copy () y = dataset [ target [ 0 ]] X_train , X_test , y_train , y_test = train_test_split ( X , y , test_size = 0.33 , random_state = 42 ) linear_reg = LinearRegression () decision_tree_reg = DecisionTreeRegressor () rf_reg = RandomForestRegressor () gbm_reg = GradientBoostingRegressor () xgb_reg = XGBRegressor () lgb_reg = LGBMRegressor () Approach 1 model_name = 'linear_reg,decision_tree_reg,rf_reg,gbm_reg,xgb_reg,lgb_reg' for m in model_name . split ( ',' ): # try: print ( m ) model = eval ( m ) model . fit ( X_train . values , y_train . valu...