ml.py

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()