linear model.py

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

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import pandas as pd
import numpy as np
##Pandas is a library for data analysis and manipulation 


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df =pd.read_csv(r"C:\Users\88016.DESKTOP-4LE338V\Documents\dataset.csv")
##Location and File name to load the data into pandas 


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df
#Datafram are ready 


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import math 
#import for math model 


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median_bedroom=math.floor(df.bedroom.median())


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median_bedroom
##Find the median of all bedroom


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df.bedroom=df.bedroom.fillna(median_bedroom)
##There will no NaN value ,, The value is add into the datafram 


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df
#Check the datafram


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from sklearn import linear_model
##For import the Linear_model


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reg=linear_model.LinearRegression()


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y=df['price']


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x=df.drop(['age','price'],axis=1)
x=x.dropna()


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x


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y


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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=10)


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reg.fit(x_train,y_train)


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reg.predict([[3000,4]])


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