Simple-linear-regression-scikit-learn

A simple implementation of Linear Regression using Scikit-Learn with synthetic data. The project demonstrates model training, prediction, visualization, and evaluation using MSE and R² metrics.

Simple Linear Regression with Scikit-Learn

This repository contains a simple example of Linear Regression using Scikit-Learn.
The model is trained on synthetic data, and predictions are visualized along with performance metrics.

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📌 Features

• Generate synthetic dataset
• Train a Linear Regression model
• Visualize actual vs. predicted values
• Evaluate model using MSE and R² score

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📂 Project Structure

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

Generate synthetic data

np.random.seed(42) X = np.random.rand(100, 1) * 100 y = 3 * X + np.random.randn(100, 1) * 2

Split Data

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Fit Linear Regression

model = LinearRegression() model.fit(X_train, y_train)

Make Predictions

y_pred = model.predict(X_test)

Print coefficients

print("Slope : ", model.coef_[0][0]) print("Intercept : ", model.intercept_[0])

plt.scatter(X_test, y_test, color="blue", label="Actual") plt.plot(X_test, y_pred, color="red", label="Predicted") plt.title("Linear Regression Model") plt.xlabel("X") plt.ylabel("y") plt.legend() plt.show()

Evaluate performance

mse = mean_squared_error(y_test, y_pred) r2 = r2_score(y_test, y_pred) print("MSE: ", mse) print("R-Squared: ", r2)