Upload ml.py

ml.py

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+import pandas as pd
+
+train = pd.read_csv('train_ctrUa4K.csv')
+train.head()
+train['Gender']= train['Gender'].map({'Male':0, 'Female':1})
+train['Married']= train['Married'].map({'No':0, 'Yes':1})
+train['Loan_Status']= train['Loan_Status'].map({'N':0, 'Y':1})
+train.isnull().sum()
+train = train.dropna()
+train.isnull().sum()
+X = train[['Gender', 'Married', 'ApplicantIncome', 'LoanAmount', 'Credit_History']]
+y = train.Loan_Status
+X.shape, y.shape
+from sklearn.model_selection import train_test_split
+x_train, x_cv, y_train, y_cv = train_test_split(X,y, test_size = 0.2, random_state = 10)
+
+from sklearn.ensemble import RandomForestClassifier
+model = RandomForestClassifier(max_depth=4, random_state = 10)
+model.fit(x_train, y_train)
+from sklearn.metrics import accuracy_score
+pred_cv = model.predict(x_cv)
+accuracy_score(y_cv,pred_cv)
+pred_train = model.predict(x_train)
+accuracy_score(y_train,pred_train)
+pred_train = model.predict(x_train)
+accuracy_score(y_train,pred_train)
+import pickle5
+pickle_out = open("classifier.pkl", mode = "wb")
+pickle5.dump(model, pickle_out)
+pickle_out.close()