text: # --------------
  import numpy as np
  import pandas as pd
  import seaborn as sns
  import matplotlib.pyplot as plt
  from sklearn.model_selection import train_test_split
  
  # Code starts here
  df=pd.read_csv(path)
  #print(df.dtypes)
  print(df.head())
  columns=['INCOME','HOME_VAL','BLUEBOOK','OLDCLAIM','CLM_AMT']
  for col in columns:
      df[col].replace({'\$': '', ',': ''}, regex=True,inplace=True)
  
  X=df.drop(['CLAIM_FLAG'],axis=1)
  y=df['CLAIM_FLAG']
  count=df['CLAIM_FLAG'].value_counts()
  X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state =6)
  
  
  
  # Code ends here
  
  
  # --------------
  # Code starts here
  for col in ['INCOME','HOME_VAL','BLUEBOOK','OLDCLAIM','CLM_AMT']:
      X_train[col]=X_train[col].astype(float)
      X_test[col]=X_test[col].astype(float)
  
  print(X_train.isnull().sum())
  print(X_test.isnull().sum())
  
  
  # Code ends here
  
  
  # --------------
  # Code starts here
  #print(df.head())
  X_train.dropna(subset=['YOJ','OCCUPATION'],inplace=True)
  
  X_test.dropna(subset=['YOJ','OCCUPATION'],inplace=True)
  
  y_train=y_train[X_train.index]
  y_test=y_test[X_test.index]
  
  X_train['AGE']=X_train['AGE'].fillna(X_train['AGE'].mean,inplace=True)
  X_train['CAR_AGE']=X_train['CAR_AGE'].fillna(X_train['CAR_AGE'].mean,inplace=True)
  X_train['INCOME']=X_train['INCOME'].fillna(X_train['INCOME'].mean,inplace=True)
  X_train['HOME_VAL']=X_train['HOME_VAL'].fillna(X_train['HOME_VAL'].mean,inplace=True)
  
  
  X_test['AGE']=X_test['AGE'].fillna(X_test['AGE'].mean,inplace=True)
  X_test['CAR_AGE']=X_test['CAR_AGE'].fillna(X_test['CAR_AGE'].mean,inplace=True)
  X_test['INCOME']=X_test['INCOME'].fillna(X_test['INCOME'].mean,inplace=True)
  X_test['HOME_VAL']=X_test['HOME_VAL'].fillna(X_test['HOME_VAL'].mean,inplace=True)
  # Code ends here
  
  
  # --------------
  from sklearn.preprocessing import LabelEncoder
  columns = ["PARENT1","MSTATUS","GENDER","EDUCATION","OCCUPATION","CAR_USE","CAR_TYPE","RED_CAR","REVOKED"]
  
  # Code starts here
  for col in columns:
      le=LabelEncoder()
      X_train[col]=le.fit_transform(X_train[col].astype(str))
      X_test[col]=le.fit_transform(X_test[col].astype(str))
  
  
  # Code ends here
  
  
  
  # --------------
  from sklearn.metrics import precision_score 
  from sklearn.metrics import accuracy_score
  from sklearn.linear_model import LogisticRegression
  
  
  
  # code starts here 
  model=LogisticRegression(random_state=6)
  model.fit(X_train,y_train)
  y_pred=model.predict(X_test)
  score=accuracy_score(y_test,y_pred)
  print(score)
  
  # Code ends here
  
  
  # --------------
  from sklearn.preprocessing import StandardScaler
  from imblearn.over_sampling import SMOTE
  
  # code starts here
  smote=SMOTE(random_state=9)
  X_train,y_train=smote.fit_sample(X_train,y_train)
  
  scaler=StandardScaler()
  X_train=scaler.fit_transform(X_train)
  X_test=scaler.transform(X_test)
  # Code ends here
  
  
  # --------------
  # Code Starts here
  model=LogisticRegression()
  model.fit(X_train,y_train)
  y_pred=model.predict(X_test)
  
  score=accuracy_score(y_test,y_pred)
  
  # Code ends here
  
  
  