🎓 Age & Gender Prediction — IIT Madras Capstone Project

Capstone project for the Post Graduate Certificate in Advanced Machine Learning & Cloud — Indian Institute of Technology, Madras.

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📌 Problem Statement

Predict a person's age (regression) and gender (classification) from facial image data. The project covers the full ML lifecycle — from exploratory analysis and feature engineering to model building, evaluation, and Flask-based deployment.

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🔍 What's Inside

Notebook / Folder	Description
EDA_and_Advanced_Visualization.ipynb	Exploratory data analysis, distribution plots, correlation analysis
Feature_Engg_and_Data_Preparation.ipynb	Feature engineering, preprocessing, train-test split
S1_Age_Prediction_Regression_model.ipynb	Sprint 1 — Baseline regression models for age prediction
S1_Gender_Prediction_Classification_model.ipynb	Sprint 1 — Baseline classification models for gender prediction
S2_Age_Prediction_Regression_model.ipynb	Sprint 2 — Improved regression with tuning & advanced models
S2_Gender_Prediction_Classification_model.ipynb	Sprint 2 — Improved classification with tuning & advanced models
Age_Prediction_Flask/	Flask web app for age prediction inference
Gender_Prediction_Flask/	Flask web app for gender prediction inference

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🛠️ Tech Stack

• ML Models: Regression (Age) — Linear Regression, Random Forest, Gradient Boosting | Classification (Gender) — Logistic Regression, Random Forest, XGBoost
• Deployment: Flask REST API for both models
• Evaluation: RMSE, MAE (Age) | Accuracy, F1-Score, ROC-AUC (Gender)

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🚀 How to Run

1. Clone the repo

git clone https://github.com/krishnaramadas/IITM_Capstone_Project.git
cd IITM_Capstone_Project

2. Install dependencies

pip install -r requirements.txt

3. Run the Flask app (Age Prediction)

cd Age_Prediction_Flask
python app.py

4. Run the Flask app (Gender Prediction)

cd Gender_Prediction_Flask
python app.py

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📊 Project Approach

Raw Data
   ↓
EDA & Visualization (distributions, outliers, correlations)
   ↓
Feature Engineering & Preprocessing
   ↓
Sprint 1: Baseline Models (Age Regression + Gender Classification)
   ↓
Sprint 2: Hyperparameter Tuning + Advanced Models
   ↓
Flask Deployment (REST API for inference)

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👥 Contributors

• Krishna Ramadas — @krishnaramadas
• Haridhranath Reddy — @harindranathreddy

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🏫 Institution

Indian Institute of Technology, Madras Post Graduate Certificate — Advanced Machine Learning & Cloud

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📄 License

This project was developed for academic purposes as part of the IIT Madras PG programme.