Beginner Projects
House Price Prediction with Regression & Streamlit Deployment
Build an end-to-end regression pipeline to predict residential house prices using features like location, area, bedrooms, and bathrooms. Includes EDA, feature engineering, model comparison, and a live Streamlit web app for real-time predictions — a clean, complete beginner project.
Customer Churn Prediction for Telecom (with Flask API)
Predict whether a telecom customer will churn using classification algorithms like Random Forest, XGBoost, and Gradient Boosting. Covers SMOTE for class imbalance, SHAP for explainability, hyperparameter tuning, and deployment via Flask — a recruiter-tested portfolio project.
Movie Recommendation System with Collaborative Filtering
Build a personalized movie recommendation engine using collaborative filtering algorithms (SVD, NMF, KNNBasic) on the MovieLens dataset. Evaluates performance with RMSE, generates top-N user recommendations, and prepares the model for web deployment — a classic resume project with real-world value.
Twitter Sentiment Analysis with BERT & Streamlit
Fine-tune a BERT/DistilBERT model on Twitter data to classify tweets into positive, negative, and neutral sentiments. Features full preprocessing, model evaluation, and a live Streamlit UI for real-time sentiment prediction — demonstrates strong NLP fundamentals.
Customer Segmentation with K-Means Clustering (RFM + Streamlit)
Apply K-Means and Agglomerative clustering to segment customers based on RFM (Recency, Frequency, Monetary) analysis. Uses PCA for visualization, elbow method for optimal clusters, and wraps everything in a Streamlit app — shows strong unsupervised ML skills for e-commerce roles.
Stock Price Prediction using LSTM Neural Networks
Predict future stock closing prices using a multi-layer LSTM network trained on historical OHLCV data from Yahoo Finance. Covers sliding-window preprocessing, early stopping, RMSE/MAE/MAPE evaluation, and training/prediction visualizations — a strong time series portfolio project.
Multiple Disease Prediction Web App (Diabetes, Heart, Parkinson's)
A deployed Streamlit web application that predicts the likelihood of diabetes, heart disease, and Parkinson's disease using supervised ML models trained on curated medical datasets. Demonstrates multi-model deployment and healthcare ML fundamentals — a visually impressive beginner project.
Image Classification with CNN using PyTorch (CIFAR-10)
Build and train a Convolutional Neural Network on the CIFAR-10 dataset for 10-class image classification. Covers data augmentation, batch normalization, learning rate scheduling, and TensorBoard visualization — a foundational computer vision project demonstrating deep learning proficiency.
Credit Card Fraud Detection with XGBoost & LightGBM
Build a fraud detection system on a highly imbalanced Kaggle dataset (0.17% fraud rate) by comparing XGBoost, LightGBM, CatBoost, and Random Forest. Uses PR-AUC as the primary metric, cross-validation, and hyperparameter tuning — achieves 97.71% AUC while handling class imbalance properly.
Intermediate Projects
End-to-End MLOps Pipeline with FastAPI, MLflow & Docker
A production-grade MLOps pipeline with CI/CD, continuous training, and monitoring — trains models via MLflow, deploys via FastAPI, containerizes with Docker, orchestrates with Kubernetes, and tracks metrics with Prometheus and Grafana. A standout DevOps-for-ML portfolio piece.
Sales & Demand Forecasting with ARIMA, Prophet & LSTM
Compare and evaluate ARIMA, Facebook Prophet, XGBoost, and LSTM models for retail sales forecasting on the Kaggle store-item demand dataset. Covers stationarity tests, seasonality decomposition, rolling forecasts, and error benchmarking — essential for supply chain and data science roles.
NLP Text Summarization with HuggingFace Pegasus & FastAPI
Fine-tune Google's Pegasus transformer model on the SAMSum dialogue dataset to generate abstractive conversation summaries. Implements a full MLOps pipeline with modular architecture, comprehensive logging, ROUGE metric evaluation, and a FastAPI + Docker deployment — production-quality NLP engineering.
Real-Time Object Detection with YOLOv8 & OpenCV
Deploy a real-time object detection system using YOLOv8 on webcam or video input. Covers single-stage detection theory, confidence thresholding, bounding box rendering, and frame-by-frame inference — demonstrates the practical computer vision skills most CV job roles require.
Named Entity Recognition (NER) System with SpaCy
Build a production-style custom NER system trained on a custom-annotated dataset using spaCy — extracts structured entities (names, locations, organizations, medical terms) from raw text with displaCy HTML visualization and API-ready inference output.
Predictive Maintenance for Industrial Equipment (IoT + ML + MLOps)
An end-to-end MLOps system that ingests industrial sensor data, trains Random Forest and XGBoost failure prediction models, tracks experiments with MLflow, and serves predictions via FastAPI — containerized with Docker, CI/CD-ready via GitHub Actions, and cloud-deployable on AWS ECS. Achieves 92% accuracy.
RAG-Based LLM Document Chatbot with LangChain & Qdrant
Build a Retrieval-Augmented Generation (RAG) chatbot that answers user questions from uploaded PDFs using Llama 3.2 (via Ollama), BGE embeddings, and Qdrant as the vector database — orchestrated with LangChain and deployed as a Streamlit web app inside Docker.
Fraud Detection with FastAPI, Streamlit & MLflow Tracking
A professional ML pipeline for credit card fraud detection using XGBoost with SHAP explainability, Streamlit for interactive fraud predictions, MLflow for experiment tracking, and Azure/GitHub Actions for CI/CD. Combines MLOps tooling with business-critical use case — a top-tier intermediate project.
AI-Powered Demand Forecasting Dashboard with Prophet & Streamlit
An AI-powered demand forecasting system combining ARIMA and Facebook Prophet with adaptive seasonality decomposition and inventory/revenue optimization. Ships with an interactive Streamlit dashboard featuring production-ready metrics — ideal for supply chain analytics portfolios.
Advanced Projects
End-to-End MLflow + FastAPI + MinIO ML Deployment Platform
A production-ready ML deployment platform integrating MLflow for experiment tracking and model registry, FastAPI for model serving, and MinIO (S3-compatible) for artifact storage — all orchestrated with Docker Compose. Demonstrates the full ML lifecycle from training to versioned API serving.
Neural Collaborative Filtering Movie Recommendation Engine
Implement a deep learning-based recommendation engine using Neural Collaborative Filtering (NCF) with PyTorch — combines user and item embeddings, trains on the MovieLens dataset, and evaluates with Hit Rate and NDCG. A more advanced recommendation system beyond classical matrix factorization.
Time Series Anomaly Detection with LSTM & Isolation Forest
Detect anomalies in multivariate time series (network traffic / server metrics) using a spectrum of methods — from statistical IQR baseline to LSTM-based forecasting reconstruction error. Benchmarks across multiple detectors for a rigorous, interview-ready ML project.
End-to-End Text Summarization MLOps Pipeline (AWS + CI/CD)
A complete text summarization system using the HuggingFace Transformers BERT model deployed on AWS with S3 and EC2 — covering full training pipeline, ROUGE score evaluation, FastAPI serving, and Streamlit deployment. Demonstrates cloud ML engineering with automated CI/CD for AWS-focused roles.
IoT-Based Predictive Maintenance System with Kafka & Spark
A full-stack IoT predictive maintenance system that streams sensor data via Apache Kafka, processes it in real time with Spark, connects to AWS IoT Core, and runs ML failure prediction models tracked with MLflow. Demonstrates advanced data engineering + ML for IIoT roles.
RAG Chatbot with Multi-LLM Support (OpenAI, Gemini, HuggingFace)
A production-ready RAG chatbot powered by LangChain supporting multiple LLM backends — OpenAI GPT-4, Google Gemini Pro, and HuggingFace Mistral. Users upload documents (PDF, CSV, DOCX), which are embedded into Chroma vector store and queried with conversational retrieval — deployed via Streamlit.
Full MLOps Pipeline: Customer Churn (Airflow + DVC + Kubernetes)
A fully automated, production-grade ML pipeline for telecom customer churn prediction orchestrated with Apache Airflow — covering data ingestion, validation, feature engineering, model training, and monitoring with DVC-based data versioning for complete reproducibility. Deploys on Kubernetes for scale.
Tips for Building Projects That Get You Hired
- 1
Use real datasets from Kaggle, UCI ML Repository, or government open data — not toy datasets.
- 2
Always deploy — a live Streamlit or FastAPI URL shows you can ship, not just train models.
- 3
Quantify everything in your resume — "94% F1-score on 50,000 samples" beats "built a classifier."
- 4
Add model explainability using SHAP or LIME — interviewers always ask about this.
- 5
Write a proper README with problem statement, approach, results, and a demo GIF.
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