House Price Prediction.py

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

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# Import Libaries 
import numpy as np

import pandas as pd

import matplotlib.pyplot as plt
import seaborn as sns

from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn import metrics


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#loading DataFrame
data = pd.read_csv('house.csv')


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# Checking first 10 Records 
data.head(10)


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data.shape


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data.describe()


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# Drawing Plot
data.plot( x = 'SquareFeet', y = "SalePrice", style = "*")
plt.title("Square Feet vs Sale Price")
plt.xlabel("Square Feet")
plt.ylabel("Sale Price")
plt.show()


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# Preparing Data for prediction

X = data.iloc[:,:-1].values
y = data.iloc[:,1].values


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# Train and Test Split


X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.2, random_state = 0)


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# Train the model
lr = LinearRegression()
lr.fit(X_train,y_train)


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# Intercept : y = mx+c : m is intercept 
lr.intercept_


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# Co-efficient y = mx+ c : c is co-efficient 
lr.coef_


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# Predicted Values
y_pred = lr.predict(X_test)


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# Actual Vs Predicted Values 
df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})
df.head(20)


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