STUDENT MARK PREDICTION USING LINEAR REGRESSION MODEL

This project predicts a student's marks based on the number of study hours using a simple linear regression model built in Python.
It demonstrates basic data cleaning, visualization, model training, and evaluation using popular machine learning libraries.

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Features

• Handles missing and fake NaN values
• Splits dataset into training and testing sets
• Implements a Linear Regression model using scikit-learn
• Evaluates model performance using:
  • Mean Absolute Error (MAE)
  • Mean Squared Error (MSE)
  • R² Score
• Predicts marks for new input hours
• Visualizes regression line vs actual marks using matplotlib

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Libraries Used

• pandas → data manipulation
• numpy → numerical operations
• matplotlib → data visualization
• scikit-learn (sklearn) → ML model building and evaluation

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Dataset

The dataset used is student_info.csv containing two columns:

• study_hours – Number of hours a student studied
• student_marks – Marks scored by the student

Example:

study_hours	student_marks
6.5	75
7.0	80
8.0	85

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Regression Line Equation

After training the model, the regression equation is displayed as: where:

• m → slope (coefficient)
• c → intercept

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Visualization

The model plots a regression line comparing actual vs predicted marks.

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How to Run

1. Clone this repository:

   git clone https://github.com/<your-username>/student-marks-prediction.git
   Install required libraries:
   

pip install pandas numpy matplotlib scikit-learn

2. Run the script:

python student_gradepredictor_lr.py

Output Example: Regression Line Equation: y = 9.68x + 48.75 Mean Absolute Error: 2.43 Mean Squared Error: 8.17 R2 Score: 0.96 Predicted Marks for 7.5 study hours = 79.89