Student Performance Data Cleaning & Visualization

This project performs data cleaning + feature engineering + visualization on the Students Performance in Exams dataset.

📂 Dataset

Source: Kaggle (Students Performance in Exams) Columns include:

Gender

Race/Ethnicity

Parental Education

Math Score

Reading Score

Writing Score

Test Preparation Course

📊 Tasks Performed in This Project ✔ Data Cleaning

Removed duplicates

Cleaned column names

Checked null values

Converted columns into proper formats

✔ Feature Engineering

Created average_score

Created pass_math (1 = pass, 0 = fail)

Created performance category (Low / Medium / High)

📈 Visualizations

Using Matplotlib, created:

Bar Chart: Average Math Score by Gender

Histogram: Distribution of Average Scores

Scatter Plot: Math vs Reading Score

Pie Chart: Test Preparation Course Completion

All graphs are saved inside the images/ folder.

🛠 Tools Used

Python

Pandas

NumPy

Matplotlib

Jupyter Notebook

📁 Project Structure student-performance-analysis/ │── data/ │── images/ │── notebooks/ │── README.md │── requirements.txt

📦 How to Run pip install -r requirements.txt jupyter notebook