Predictive Analytics - (LAB PROGRAMS)

Aim:

☛ Simple Linear Regression

Solution :

Library Installation:

To install required library files, Open Command Prompt or Terminal and execute the following commands


$ pip install scikit-learn

$ pip install numpy

$ pip install matplotlib

PROGRAM: (Simple_Linear_Regression.py)

 
# Import required libraries
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score
# Sample dataset (you can replace with your own data)
X = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).reshape(-1, 1)
Y = np.array([2, 4, 5, 4, 5, 7, 8, 9, 10, 12])
# Split data into training and testing sets
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)
# Create and train the model
model = LinearRegression()
model.fit(X_train, Y_train)
# Make predictions
Y_pred = model.predict(X_test)
# Print model parameters
print(f"Coefficient (slope): {model.coef_}")
print(f"Intercept: {model.intercept_}")
# Calculate performance metrics
mse = mean_squared_error(Y_test, Y_pred)
r2 = r2_score(Y_test, Y_pred)
print(f"Mean Squared Error: {mse}")
print(f"R-squared: {r2}")

# Plot the results
plt.scatter(X_test, Y_test, color='blue', label='Actual data')
plt.plot(X_test, Y_pred, color='red', linewidth=2, label='Regression Line')
plt.title("Simple Linear Regression")
plt.xlabel("Independent Variable (X)")
plt.ylabel("Dependent Variable (Y)")
plt.legend()
plt.show()

OUTPUT:

 
Coefficient (slope): [1.06896552]
Intercept: 0.6206896551724128
Mean Squared Error: 0.7996432818073731
R-squared: 0.9111507464658475

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Predictive Analytics - (LAB PROGRAMS)

Aim:

☛ Simple Linear Regression

Solution :

Library Installation:

PROGRAM: (Simple_Linear_Regression.py)

OUTPUT:

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