<filename>Boosting-on-telecom-data/code.py
# --------------
import pandas as pd
from sklearn.model_selection import train_test_split
#path - Path of file 

# Code starts here
df = pd.read_csv(path)
X = df.drop(['customerID','Churn'], axis=1)
y = df['Churn']
X_train,X_test,y_train,y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)



# --------------
import numpy as np
from sklearn.preprocessing import LabelEncoder

# Code starts here
X_train['TotalCharges'] = X_train['TotalCharges'].replace(r'^\s*$', np.nan, regex=True)
X_test['TotalCharges'] = X_test['TotalCharges'].replace(r'^\s*$', np.nan, regex=True)

X_train['TotalCharges'] = X_train['TotalCharges'].astype(float)
X_test['TotalCharges'] = X_test['TotalCharges'].astype(float)
X_train['TotalCharges'] = X_train['TotalCharges'].fillna(X_train['TotalCharges'].mean())
X_test['TotalCharges'] = X_test['TotalCharges'].fillna(X_test['TotalCharges'].mean())
print(X_train.isnull().sum())
cat_cols = ['gender','Partner','Dependents','PhoneService','MultipleLines','InternetService','OnlineSecurity','OnlineBackup','DeviceProtection','TechSupport','StreamingTV','StreamingMovies','Contract','PaperlessBilling','PaymentMethod']

for col in cat_cols:
    le = LabelEncoder()
    X_train[col] = le.fit_transform(X_train[col])
    X_test[col] = le.fit_transform(X_test[col])

y_train = y_train.apply(lambda x: 0 if x=='No' else 1)
y_test = y_test.apply(lambda x: 0 if x=='No' else 1)
print(X_train.head())
print(y_train.head())
print(X_test.head())
print(y_test.head())




# --------------
from sklearn.ensemble import AdaBoostClassifier
from sklearn.metrics import accuracy_score,classification_report,confusion_matrix

# Code starts here
ada_model = AdaBoostClassifier(random_state=0)
ada_model.fit(X_train, y_train)
y_pred = ada_model.predict(X_test)
ada_score = accuracy_score(y_test, y_pred)
ada_cm = confusion_matrix(y_test, y_pred)
ada_cr = classification_report(y_test, y_pred)

print(ada_score)
print(ada_cm)
print(ada_cr)


# --------------
from xgboost import XGBClassifier
from sklearn.model_selection import GridSearchCV

#Parameter list
parameters={'learning_rate':[0.1,0.15,0.2,0.25,0.3],
            'max_depth':range(1,3)}

# Code starts here
xgb_model = XGBClassifier(random_state=0)
xgb_model.fit(X_train, y_train)
y_pred = xgb_model.predict(X_test)
xgb_score = accuracy_score(y_test, y_pred)
xgb_cm = confusion_matrix(y_test, y_pred)
xgb_cr = classification_report(y_test, y_pred)

clf_model = GridSearchCV(estimator=xgb_model, param_grid=parameters)
clf_model.fit(X_train, y_train)
y_pred = xgb_model.predict(X_test)
clf_score = accuracy_score(y_test, y_pred)
clf_cm = confusion_matrix(y_test, y_pred)
clf_cr = classification_report(y_test, y_pred)



<filename>Boosting-on-telecom-data/code.py
# --------------
import pandas as pd
from sklearn.model_selection import train_test_split
#path - Path of file 

# Code starts here
df = pd.read_csv(path)
X = df.drop(['customerID','Churn'], axis=1)
y = df['Churn']
X_train,X_test,y_train,y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)



# --------------
import numpy as np
from sklearn.preprocessing import LabelEncoder

# Code starts here
X_train['TotalCharges'] = X_train['TotalCharges'].replace(r'^\s*$', np.nan, regex=True)
X_test['TotalCharges'] = X_test['TotalCharges'].replace(r'^\s*$', np.nan, regex=True)

X_train['TotalCharges'] = X_train['TotalCharges'].astype(float)
X_test['TotalCharges'] = X_test['TotalCharges'].astype(float)
X_train['TotalCharges'] = X_train['TotalCharges'].fillna(X_train['TotalCharges'].mean())
X_test['TotalCharges'] = X_test['TotalCharges'].fillna(X_test['TotalCharges'].mean())
print(X_train.isnull().sum())
cat_cols = ['gender','Partner','Dependents','PhoneService','MultipleLines','InternetService','OnlineSecurity','OnlineBackup','DeviceProtection','TechSupport','StreamingTV','StreamingMovies','Contract','PaperlessBilling','PaymentMethod']

for col in cat_cols:
    le = LabelEncoder()
    X_train[col] = le.fit_transform(X_train[col])
    X_test[col] = le.fit_transform(X_test[col])

y_train = y_train.apply(lambda x: 0 if x=='No' else 1)
y_test = y_test.apply(lambda x: 0 if x=='No' else 1)
print(X_train.head())
print(y_train.head())
print(X_test.head())
print(y_test.head())




# --------------
from sklearn.ensemble import AdaBoostClassifier
from sklearn.metrics import accuracy_score,classification_report,confusion_matrix

# Code starts here
ada_model = AdaBoostClassifier(random_state=0)
ada_model.fit(X_train, y_train)
y_pred = ada_model.predict(X_test)
ada_score = accuracy_score(y_test, y_pred)
ada_cm = confusion_matrix(y_test, y_pred)
ada_cr = classification_report(y_test, y_pred)

print(ada_score)
print(ada_cm)
print(ada_cr)


# --------------
from xgboost import XGBClassifier
from sklearn.model_selection import GridSearchCV

#Parameter list
parameters={'learning_rate':[0.1,0.15,0.2,0.25,0.3],
            'max_depth':range(1,3)}

# Code starts here
xgb_model = XGBClassifier(random_state=0)
xgb_model.fit(X_train, y_train)
y_pred = xgb_model.predict(X_test)
xgb_score = accuracy_score(y_test, y_pred)
xgb_cm = confusion_matrix(y_test, y_pred)
xgb_cr = classification_report(y_test, y_pred)

clf_model = GridSearchCV(estimator=xgb_model, param_grid=parameters)
clf_model.fit(X_train, y_train)
y_pred = xgb_model.predict(X_test)
clf_score = accuracy_score(y_test, y_pred)
clf_cm = confusion_matrix(y_test, y_pred)
clf_cr = classification_report(y_test, y_pred)



# -------------- #path - Path of file # Code starts here # -------------- # Code starts here # -------------- # Code starts here # -------------- #Parameter list # Code starts here