Very Simple Linear Regression Programs

Very Simple  Linear Regression Programs to predict the Study Hours vs Marks Prediction

Step 1: Import Required Libraries

👉 Libraries are ready-made tools

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

Explain:

• pandas → handles data

• matplotlib → shows graph

• LinearRegression → regression model

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Step 2: Create Data

👉 This is our learning data

data = {
    'Hours': [1, 2, 3, 4, 5],
    'Marks': [20, 35, 50, 65, 80]
}

df = pd.DataFrame(data)

Explain:

• Hours → Input (X)

• Marks → Output (Y)

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Step 3: Separate Input and Output

X = df[['Hours']]
y = df['Marks']

Explain:

• Machine learns X → Y relation

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Step 4: Train the Regression Model

model = LinearRegression()
model.fit(X, y)

Explain:

• fit() → Machine learns pattern

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Step 5: Predict Marks

new_data = pd.DataFrame({'Hours': [6]})
predicted_marks = model.predict(new_data)
print("Predicted Marks:", predicted_marks)

Explain:

• If student studies 6 hours, marks are predicted

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Step 6: Show Graph

plt.scatter(X, y)
plt.plot(X, model.predict(X))
plt.xlabel("Study Hours")
plt.ylabel("Marks")
plt.show()

Explain:

• Dots → Actual data

• Line → Prediction line