This project analyzes a Kaggle dataset related to mental health and builds a machine learning model to predict mental health conditions. The goal is to identify key factors affecting mental health and use predictive analytics to support awareness and decision-making.

📂 Project Overview

Data Source: Kaggle (Mental Health Dataset)

Tools Used: Jupyter Lab, Python (Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn)

Objective: Predict mental health outcomes based on demographic and survey-related features.

ML Approach: Classification models (Logistic Regression, Random Forest, etc.)

🔍 Key Features

Data preprocessing (handling missing values, encoding categorical variables, scaling features)

Exploratory Data Analysis (EDA) with visualizations

Feature selection and engineering

Training and evaluation of multiple machine learning models

Performance comparison using metrics like accuracy, precision, recall, F1-score, and ROC-AUC

Final predictive module for mental health classification