Linear_Regression_Model.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt


dataset = pd.read_csv('Salary Data.csv')

x = dataset['YearsExperience']
y = dataset['Salary']


class LinearRegression:
    def __init__(self):
        self.w = 0
        self.b = 0

    def cost_function(self,x,y,w,b):
        n = len(x)
        cost_sum = 0

        for i in range(n):
            f = w * x[i] + b
            cost = (f-y[i])**2
            cost_sum += cost

        return (1/(2*n)) * cost_sum

    def gradient_funciton(self,x,y,w,b):
        n = len(x)

        dc_dw = dc_db = 0

        for i in range(n):
            f = w*x[i] + b
            dc_dw += (f - y[i]) * x[i]
            dc_db += (f - y[i])

        dc_dw = (1/n) * dc_dw
        dc_db = (1/n) * dc_db

        return dc_dw, dc_db

    def gradient_descent(self,x,y,alpha = 0.01,iterations = 10000):

        w = b = 0

        for i in range(iterations):
            dc_dw, dc_db = self.gradient_funciton(x,y,w,b)

            w = w - alpha * dc_dw
            b = b - alpha * dc_db

        return w,b

    def fit(self, x, y):
        self.w, self.b =  self.gradient_descent(x,y)
    
    def predict(self,x):
        return self.w * x + self.b
    

model = LinearRegression()
model.fit(x,y)
print(model.predict(4))

plt.plot(x, (x*(model.w) + (model.b)))
plt.scatter(x,y)

plt.show()