linear_reg.py

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

# Load the Iris dataset
iris = load_iris()
X = iris.data # Features: Sepal length, Sepal width, Petal length, Petal width
y = iris.target # Target: Species

# Convert the target to a regression-friendly format (use one feature for demonstration)
# Here, we predict the Petal length based on other features
X = X[:, :3] # Using Sepal length, Sepal width, Petal length as features
y = X[:, 2] # Target is Petal length

#X = iris.data[:, [0]]  # Only Sepal length as feature
#y = iris.data[:, 2]    # Petal length as target


# Split the dataset 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 a Linear Regression model
model = LinearRegression()

# Train the model on the training data
model.fit(X_train, y_train)

# Make predictions on the test data
y_pred = model.predict(X_test)

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

# Print the results
print("Model Coefficients:", model.coef_)
print("Model Intercept:", model.intercept_)
print("Mean Squared Error (MSE):", mse)
print("R-squared Score:", r2)

# Example prediction ,sample_data = [[5.1]] 
sample_data = [[5.1, 3.5, 1.4]] # Example input: Sepal length, Sepal width, Petal length
predicted_petal_length = model.predict(sample_data)
print("Predicted Petal Length:", predicted_petal_length[0])