Student Addiction Analysis

This project analyzes and predicts student addiction levels using machine learning and clustering.

Features :

-- Loads and preprocesses the dataset

-- Trains a KNN classifier

-- Shows accuracy, precision, recall, and F1-score

-- Generates a correlation heatmap

Applies clustering:

--> K-Means

--> Agglomerative Clustering

--> DBSCAN

--> Displays PCA-based cluster visualizations

--> Creates a results table for all clustering models

Files :

• main.py – main script

• preprocessing.py – data cleaning and encoding

• mergingCSVs.py – dataset merging

• processed_dataset.csv – cleaned dataset

• combined_survey_data.csv – raw merged dataset

• README.md

How to Run

Install dependencies:

pip install pandas scikit-learn matplotlib seaborn

Run the project:

python main.py

Models:

~~ KNN classification

~~ K-Means clustering

~~ Agglomerative clustering

~~ DBSCAN clustering