reg.py

 
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn import datasets, linear_model

def get_data(file_name):
    data = pd.read_csv(file_name)
    x_parameter = []
    y_parameter = []
    for single_square_feet ,single_price_value in zip(data['x'],data['y']):
        x_parameter.append([single_square_feet])
        y_parameter.append(single_price_value)
    return x_parameter,y_parameter


# Function for Fitting data to Linear model
def linear_model_main(X_parameters,Y_parameters,predict_value):
 
    # Create linear regression object
    regr = linear_model.LinearRegression()
    regr.fit(X_parameters, Y_parameters)
    predict_outcome = regr.predict(predict_value)
    predictions = {}
    predictions['intercept'] = regr.intercept_
    predictions['coefficient'] = regr.coef_
    predictions['predicted_value'] = predict_outcome
    return predictions

def show_linear_line(X_parameters,Y_parameters):
    # Create linear regression object
    regr = linear_model.LinearRegression()
    regr.fit(X_parameters, Y_parameters)
    plt.scatter(X_parameters,Y_parameters,color='blue')
    plt.plot(X_parameters,regr.predict(X_parameters),color='red',linewidth=4)
    plt.xticks(())
    plt.yticks(())
    plt.show()

x,y = get_data('data.csv')
predict_value =0
result = linear_model_main(x,y,predict_value)
print "Intercept value " , result['intercept']
print "coefficient" , result['coefficient']
print "Predicted value: ",result['predicted_value']
show_linear_line(x,y)