Back

Machine Learning Projects

A collection of end-to-end machine learning projects demonstrating expertise in predictive modeling, deep learning, and data engineering. Each project includes interactive demos with real data.

PROJECT 01

Data Flow Hub.AI

Built a comprehensive enterprise ML platform featuring: (1) FRED API Integration - TypeScript client enabling search and import of 800,000+ Federal Reserve economic datasets with advanced filtering, auto-conversion to platform format, and one-click import workflow. (2) Enhanced AI Chat - Context-aware GPT-4 assistant that adapts responses based on user profile (industry, use case, objectives), provides personalized data science guidance, and offers intelligent dataset-specific analysis. (3) PostgreSQL Data Warehouse - Dual-database OLTP/OLAP architecture with dimensional modeling (star schema), ETL pipelines via Supabase Edge Functions, and BI-ready analytics queries. Full-stack implementation with React/TypeScript frontend, FastAPI backend, Docker Compose orchestration (15+ services), and Supabase for auth/storage.

FRED APIGPT-4PostgreSQLData WarehouseReactTypeScriptFastAPIDockerSupabaseLangChain

Performance Metrics

800K+

Economic Datasets

<2s

AI Response Time

15+

Docker Services

10x

Query Optimization

Dataset: FRED Economic Data + Enterprise Analytics Warehouse

View Live Project View Code
PROJECT 02

FRED Economic Data Integration

Engineered a comprehensive FRED (Federal Reserve Economic Data) integration for enterprise ML platforms. Built a complete TypeScript API client featuring fuzzy search across 800,000+ economic series, advanced filtering (date range, frequency, units transformations), and automatic data conversion to platform format. Implemented professional search UI with autocomplete, popular indicator quick-access (GDP, unemployment, CPI, interest rates), expandable series details with full descriptions, and one-click import workflow. Features robust error handling, rate limiting compliance (120 req/min), and metadata extraction for automated dataset tagging. Enables same-level data access as Bloomberg/FactSet for economic analysis, financial modeling, and market research.

TypeScriptREST APIReactFederal ReserveEconomic DataData IntegrationFinancial APIs

Performance Metrics

800K+

Datasets Available

<3s

Import Speed

15+

Indicators

100%

API Compliance

Dataset: Federal Reserve Economic Data (FRED)

View Live Project View Code
PROJECT 03

Context-Aware AI Data Assistant

Developed an intelligent AI chat assistant that provides personalized data science guidance by leveraging user context. The system loads user profiles from Supabase (use case, profession, industry, objectives) and dynamically generates contextual system prompts for GPT-4. Features industry-specific guidance for 10+ use cases (business analytics, fraud detection, healthcare, finance, marketing, supply chain, etc.), dataset-aware responses that analyze user's uploaded data, sample questions that adapt based on selected dataset, and beautiful gradient UI with real-time streaming. Built with React, OpenAI API integration, and Supabase for persistent user context storage.

GPT-4OpenAI APIReactContext-Aware AINLPSupabaseUser Personalization

Performance Metrics

10+

Use Cases

<2s

Response Time

4 layers

Context Depth

95%

Satisfaction

Dataset: User Datasets + Profile Context

View Live Project View Code
PROJECT 04

PostgreSQL Data Warehouse Architecture

Architected a production-grade PostgreSQL data warehouse implementing industry-standard dimensional modeling. Designed dual-database strategy separating operational (Supabase OLTP) from analytical (PostgreSQL OLAP) workloads. Created star schema with dimension tables (dim_datasets, dim_users) and fact tables (fact_analysis_events, fact_dataset_health, fact_model_performance). Implemented ETL pipelines using Supabase Edge Functions with Foreign Data Wrapper (FDW) for cross-database queries. Built pre-computed aggregation tables (agg_daily_usage) for dashboard performance. Features include Docker Compose deployment, pgAdmin management UI, and comprehensive BI query library for user engagement, dataset quality trends, and model performance analytics.

PostgreSQLData WarehouseStar SchemaETLDockerSupabaseAnalyticsBI

Performance Metrics

10x faster

Query Speed

6+

Tables

Auto

Daily Aggregations

99.9%

Uptime

Dataset: Analytics Data Warehouse (Event-driven)

View Live Project View Code
PROJECT 05

Bitcoin Whale Tracker

Developed enterprise-grade cryptocurrency analytics platform featuring real-time whale transaction monitoring, TensorFlow.js ML models for price forecasting (78% accuracy), and NLP-powered sentiment analysis. Integrated 11+ external APIs (CoinGecko, FRED, NewsAPI, CryptoCompare) with intelligent rate limiting. Built WebSocket streaming architecture processing 1000+ data points per minute, automated pattern detection algorithms with 85%+ confidence scoring, and Docker microservices deployment.

TensorFlow.jsWebSocketsNode.jsReactPostgreSQLDockerNLPReal-time

Performance Metrics

78%

ML Accuracy

1000+

Data Points/Min

11+

API Integrations

<200ms

Response Time

Dataset: Bitcoin Blockchain & Multi-source Market Data

View Live Project View Code
PROJECT 06

Anomaly Detection System

Designed and deployed a distributed anomaly detection system using Apache Spark and Python ML libraries. The system processes IoT sensor data streams in real-time, identifies anomalies using Random Forest and feature importance analysis, and provides immediate alerts. Deployed on AWS with auto-scaling capabilities.

Apache SparkRandom ForestFeature EngineeringAWSIoT

Performance Metrics

94%

Accuracy

<100ms

Latency

10K+

Sensors

5M+

Events/day

Dataset: IoT Sensor Network (10K+ devices)

View Live Project View Code
PROJECT 07

Customer Churn Prediction

Built a comprehensive churn prediction system using Random Forest and XGBoost classifiers. Implemented SMOTE for handling class imbalance and performed extensive feature engineering including RFM analysis, customer lifetime value calculations, and behavioral pattern extraction.

Random ForestXGBoostSMOTEFeature Engineering

Performance Metrics

89%

Accuracy

87%

Precision

91%

Recall

0.89

F1 Score

Dataset: Telecom Customer Dataset (7,043 records)

Try Interactive Demo View Code
PROJECT 08

Stock Price Forecasting

Developed a deep learning model using LSTM networks to forecast stock prices. The system integrates real-time market data, performs technical indicator calculations, and provides multi-day ahead predictions with confidence intervals.

LSTMTime SeriesDeep LearningYahoo Finance API

Performance Metrics

2.34

RMSE

1.87

MAE

3.2%

MAPE

0.94

R² Score

Dataset: S&P 500 Historical Data (5 years)

Try Interactive Demo View Code
PROJECT 09

Sentiment Analysis Dashboard

Created an end-to-end sentiment analysis pipeline using BERT transformers for text classification. The system processes social media posts in real-time, extracts sentiment scores, identifies trending topics, and visualizes insights through an interactive dashboard.

BERTNLPTransformersTwitter API

Performance Metrics

92%

Accuracy

1000/min

Processing Speed

5

Languages

3

Sentiment Classes

Dataset: Twitter Dataset (100K+ tweets)

Try Interactive Demo View Code

Interested in collaborating?

I'm always open to discussing new projects, creative ideas, or opportunities to be part of your vision.

Get in Touch