# Simple Linear Regression Example — Predict Exam Score from Hours Studied

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression

# Step 1: Load dataset from online CSV
url = "https://raw.githubusercontent.com/pranavshinde36/Student-Study-Hours-vs-Exam-Scores/main/Student_Score.csv"
data = pd.read_csv(url)

# Step 2: Prepare data
X = data[['Hours']]  # input feature
y = data['Scores']   # output / target

# Step 3: Create and train the model
model = LinearRegression()
model.fit(X, y)

# Step 4: Print model parameters
print(f"Slope (m): {model.coef_[0]:.2f}")
print(f"Intercept (b): {model.intercept_:.2f}")

# Step 5: Make predictions
data['Predicted_Score'] = model.predict(X)

# Step 6: Plot original data and regression line
plt.scatter(X, y, color='blue', label='Actual Scores')
plt.plot(X, data['Predicted_Score'], color='red', label='Regression Line')
plt.xlabel('Hours Studied')
plt.ylabel('Exam Score')
plt.title('Hours vs Exam Score')
plt.legend()
plt.show()

# Step 7: Predict score for new study hours
new_hours = [[5], [8]]
predicted_scores = model.predict(new_hours)
for hours, score in zip(new_hours, predicted_scores):
    print(f"Predicted score for studying {hours[0]} hours: {score:.2f}")

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