Showing Relationship between X and Y Values using Linear Regression Line in Python

Importing Libraries for this project

import numpy as np

import pandas as pd from sklearn.linear_

model import LinearRegression

import matplotlib.pyplot as plt

Importing Data file into python code

filepath = 'PythonRegressionPracticeWorkbook.csv'

data = pd.read_csv(filepath)

print(data)

Assigning Input / Independent Column to X and assigning dependent / Output Column to Y variables

x = data[['Square Feet']]

y = data['Price']

Training the X and Y variables using Linear Regression Model to find how X affects Y

model = LinearRegression()

model.fit(x,y)

Checking Slope, Intercept values from the learned Model

slope = model.coef_[0]

intercept = model.intercept_

print("Slope:", slope)

print("Intercept:",intercept)

Predicting the output value / Y value for each X value / input value

y_pred = model.predict(x)

Plotting Scatter plot with Regression Line to show both Original X and Y values, Predicted Y values

plt.figure(figsize=(12,8))

plt.scatter(x,y, color='blue', label='Actual Data') plt.plot(x,y_pred, color='red', label='Regression Line')

for i in range(len(x)):

plt.text(x.iloc[i,0],y.iloc[i]+10,f"({x.iloc[i,0]},{y.iloc[i]})",color='blue')

for i in range(len(x)):

plt.text(x.iloc[i,0], y_pred[i] - 10, f"({x.iloc[i,0]},{round(y_pred[i],1)})", color='red')

plt.xlabel('Square feet')

plt.ylabel('Price')

plt.legend()

plt.show()

R Squared value tells how well the regression line fits the data, 1 - good fit, 0 - poor fit

r2 = model.score(x,y)

print("r_squared:",r2)

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