Predicting Student Dropout Using Machine Learning

This project analyzes student dropout patterns using a dataset of student records. The workflow includes:

Data Preprocessing: Handling missing values, encoding categorical variables, and standardizing features using StandardScaler.

Feature Engineering: Selecting relevant features and preparing data for training.

Machine Learning Models: Training and evaluating classification models such as Logistic Regression, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM).

Model Evaluation: Measuring model performance using accuracy and classification reports.

The goal is to develop a predictive model that can help educational institutions identify students at risk of dropping out and take proactive measures.