🎓 Student Performance Prediction

This is my first ever machine learning project (and yes, I'm excited 😄). It uses a simple linear regression model to predict student scores based on study hours.

What started out as a curiosity about "how AI learns stuff" turned into a hands-on project that taught me the full pipeline — from loading data to building and evaluating a model.

📌 Project Highlights

• 🔍 Explored the relationship between hours studied and scores
• 📈 Trained a Linear Regression model using scikit-learn
• 📊 Visualized data and the regression line using seaborn & matplotlib
• 📤 Made predictions and evaluated the model’s performance
• 🧪 Tested model with custom inputs (e.g., predicting scores for 9.25 hours)

🛠️ Tools Used

• Python (Jupyter Notebook via Anaconda)
• pandas
• scikit-learn
• matplotlib
• seaborn

🚀 How to Run

1. Clone this repository:

   git clone https://github.com/debb-major/student-performance-linear-regression
   

2. Install dependencies:

    pip install -r requirements.txt
   

3. Open the notebook:

    jupyter notebook notebook/student_scores_analysis.ipynb
   
   

💬 Final Thoughts I had a lot of fun doing this project and learned so much about how models learn from data. Can't wait to take on more challenges!

Thanks for checking it out 💙