Linear Regression.py

#!/usr/bin/env python
# coding: utf-8

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import pandas as pd


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df=pd.read_csv(r'E:\ai projects\file.csv')


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df.head()


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x=df.drop(['KitchenQual','Id','OverallQual','YearRemodAdd','RoofMatl','RoofStyle','MasVnrType','MasVnrArea','BsmtExposure','MSZoning','Alley','Neighborhood','BldgType','LandContour','YearRemodAdd','PoolQC','Utilities','Exterior1st','Exterior2nd','BsmtQual','BsmtFinSF1','BsmtFinType1','BsmtFinSF2','Heating','HeatingQC','CentralAir','1stFlrSF','2ndFlrSF','LowQualFinSF','GrLivArea','BsmtFullBath','BsmtHalfBath','BsmtFinType2','Functional','Fireplaces','GarageCond','PavedDrive','WoodDeckSF','OpenPorchSF','EnclosedPorch','EnclosedPorch','ScreenPorch','PoolArea','MiscFeature','MiscFeature','MoSold','GarageYrBlt','GarageFinish','Fence','SaleType'],axis=1)
x=x.dropna()
y = x['SalePrice']
x = x.drop(['SalePrice'],axis=1)


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x=x.dropna()


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from sklearn.preprocessing import LabelEncoder


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le =LabelEncoder()


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le.fit(x['LotShape'])


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s=le.transform(x['LotShape'])


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x['LotShape']=s


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x


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from sklearn.preprocessing import LabelEncoder


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le =LabelEncoder()


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le.fit(x['Street'])


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s=le.transform(x['Street'])


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x['Street']=s


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x


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from sklearn.preprocessing import LabelEncoder


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le =LabelEncoder()


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le.fit(x['LotConfig'])


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s=le.transform(x['LotConfig'])


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x['LotConfig']=s


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x


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from sklearn.preprocessing import LabelEncoder


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le =LabelEncoder()


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le.fit(x['LandSlope'])


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s=le.transform(x['LandSlope'])


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x['LandSlope']=s


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x


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from sklearn.preprocessing import LabelEncoder


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le =LabelEncoder()


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le.fit(x['Condition1'])


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s=le.transform(x['Condition1'])


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x['Condition1']=s


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x


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from sklearn.preprocessing import LabelEncoder


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le =LabelEncoder()


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le.fit(x['Condition2'])


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s=le.transform(x['Condition2'])


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x['Condition2']=s


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x


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from sklearn.preprocessing import LabelEncoder


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le =LabelEncoder()


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le.fit(x['HouseStyle'])


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s=le.transform(x['HouseStyle'])


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x['HouseStyle']=s


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x


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from sklearn.preprocessing import LabelEncoder


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le =LabelEncoder()


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le.fit(x['GarageQual'])


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s=le.transform(x['GarageQual'])


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x['GarageQual']=s


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x


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from sklearn.preprocessing import LabelEncoder


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le =LabelEncoder()


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le.fit(x['SaleCondition'])


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s=le.transform(x['SaleCondition'])


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x['SaleCondition']=s


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from sklearn.preprocessing import LabelEncoder


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le =LabelEncoder()


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le.fit(x['GarageType'])


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s=le.transform(x['GarageType'])


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x['GarageType']=s


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x


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from sklearn.preprocessing import LabelEncoder


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le =LabelEncoder()


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le.fit(x['FireplaceQu'])


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s=le.transform(x['FireplaceQu'])


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x['FireplaceQu']=s


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x


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from sklearn.preprocessing import LabelEncoder


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le =LabelEncoder()


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le.fit(x['LotConfig'])


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s=le.transform(x['LotConfig'])


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x['LotConfig']=s


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from sklearn.preprocessing import LabelEncoder


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le =LabelEncoder()


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le.fit(x['LandSlope'])


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s=le.transform(x['LandSlope'])


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x['LandSlope']=s


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x


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from sklearn.preprocessing import LabelEncoder


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le =LabelEncoder()


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le.fit(x['ExterQual'])


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s=le.transform(x['ExterQual'])


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x['ExterQual']=s


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x


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from sklearn.preprocessing import LabelEncoder


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le =LabelEncoder()


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le.fit(x['ExterCond'])


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s=le.transform(x['ExterCond'])


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x['ExterCond']=s


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x


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from sklearn.preprocessing import LabelEncoder


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le =LabelEncoder()


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le.fit(x['Foundation'])


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s=le.transform(x['Foundation'])


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x['Foundation']=s


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from sklearn.preprocessing import LabelEncoder


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le =LabelEncoder()


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le.fit(x['BsmtCond'])


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s=le.transform(x['BsmtCond'])


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x['BsmtCond']=s


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x


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from sklearn.preprocessing import LabelEncoder


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le =LabelEncoder()


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le.fit(x['Electrical'])


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s=le.transform(x['Electrical'])


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x['Electrical']=s


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x


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from sklearn.preprocessing import LabelEncoder


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le =LabelEncoder()


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le.fit(x['GarageQual'])


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s=le.transform(x['GarageQual'])


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x['GarageQual']=s


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x


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from sklearn.model_selection import train_test_split


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x_train, x_test, y_train, y_test = train_test_split(
x, y, test_size=0.2, random_state=79)


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from sklearn.linear_model import LinearRegression


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reg = LinearRegression()
reg.fit(x_train, y_train)


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reg.score(x_train, y_train)


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