Student Performance Prediction | Machine Learning Project

**Student Performance Prediction | Machine Learning Project**

Developed a machine learning solution using Python to predict student pass/fail outcomes based on academic data.

**Technical Contributions:**

- Cleaned and preprocessed data by handling missing values and duplicate records.

- Encoded categorical features and applied feature scaling to prepare the dataset for machine learning.

- Built and trained a Logistic Regression classification model using Scikit-learn.

- Addressed class imbalance using class weighting.

- Evaluated model performance using accuracy, precision, recall, F1-score, and a confusion matrix.

**Technologies Used:** Python, Pandas, NumPy, Scikit-learn, Jupyter Notebook.

This project demonstrates practical experience in data preprocessing, classification modeling, and machine learning model evaluation.