Delete main.py

main.py

@@ -1,62 +0,0 @@
-# import all the required libraries
-
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-import seaborn as sns 
-
-
-class cross_sell():
-    def __init__(self,user_data):
-        self.user_data = user_data
-    def prediction(self):
-    # Importing data from csv
-
-        df = pd.read_csv('health_insurance.csv')
-
-        from sklearn.preprocessing import LabelEncoder
-
-        le_gender  = LabelEncoder() 
-        df['Gender'] = le_gender.fit_transform(df['Gender'])
-
-        le_vAge =  LabelEncoder()
-        df['Vehicle_Age'] = le_vAge.fit_transform(df['Vehicle_Age'])
-
-        le_vDamage = LabelEncoder()
-        df['Vehicle_Damage'] = le_vDamage.fit_transform(df['Vehicle_Damage'])
-
-        x = df.drop('Response', axis = 1)
-        y = df['Response']
-
-        #balancing the data for Target column
-        from imblearn.over_sampling import SMOTE
-
-        smt = SMOTE(k_neighbors=8, random_state=10)
-        x_new, y_new = smt.fit_resample(x, y)
-        #x_new.shape, y_new.shape
-
-        #Splitting the data into train and test datasets
-
-        from sklearn.model_selection import train_test_split
-
-        xtrain, xtest, ytrain, ytest = train_test_split(x_new, y_new, test_size =.30, random_state = 0)
-        #xtrain.shape, xtest.shape, ytrain.shape, ytest.shape
-
-
-        from sklearn.preprocessing import StandardScaler
-
-        scaler = StandardScaler()
-        xtrain = scaler.fit_transform(xtrain)
-        xtest = scaler.transform(xtest)
-
-
-        from xgboost import XGBClassifier
-
-        model_xgb = XGBClassifier()
-        model_xgb.fit(xtrain, ytrain)
-
-        response = model_xgb.predict(self.user_data)
-        if response==1:
-            return 'This customer willing to buy a vehicle insurance'
-        else:
-            return 'This customer will not buy a vehicle insurance'