ML-LAB-VI-SEM

Exercise 03

Implementation of Simple Linear Regression in Python

Aim

To implement Simple Linear Regression using Python to predict values based on a given dataset.

Procedure/Program

import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

# sample dataset (Experience vs Salary)
experience = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).reshape(-1, 1)
salary = np.array([30000, 35000, 40000, 45000, 50000, 55000, 60000, 65000, 70000, 75000])

# data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(experience, salary, test_size=0.2, random_state=42)

# Linear Regression model
model = LinearRegression()
model.fit(X_train, y_train)

# predicting values
y_pred = model.predict(X_test)

# model evaluation
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print("Model Coefficient (Slope):", model.coef_[0])
print("Model Intercept:", model.intercept_)
print("Mean Squared Error:", mse)
print("R-squared Score:", r2)

# regression line
plt.scatter(X_train, y_train, color='blue', label="Training Data")
plt.scatter(X_test, y_test, color='red', label="Testing Data")
plt.plot(X_test, y_pred, color='green', linewidth=2, label="Regression Line")
plt.xlabel("Years of Experience")
plt.ylabel("Salary")
plt.title("Simple Linear Regression - Experience vs Salary")
plt.legend()
plt.show()

Output/Explanation

• Output:

  

  Model Coefficient (Slope): 4999.999999999999
  Model Intercept: 25000.000000000004
  Mean Squared Error: 0.0
  R-squared Score: 1.0
  

  The program trains a Simple Linear Regression model and predicts salaries based on years of experience. Expected output includes:

  • Model parameters (Slope & Intercept)
  • Evaluation metrics like Mean Squared Error (MSE) and R-squared score (R²)
  • A scatter plot with training and testing data along with the regression line.

• Explanation:

  • The dataset contains Years of Experience as input (X) and Salary as output (y).
  • The data is split into training and testing sets using train_test_split().
  • A Linear Regression model is created using LinearRegression() and trained on the dataset.
  • The model predicts salaries for the test set and evaluates performance using MSE and R² score.
  • A regression line is plotted to visualize the model’s predictions.