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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.

Data Flow Hub.AI

Built a comprehensive ML platform that uses Vertex AI and LangChain to interpret and answer questions about complex datasets. The system automates data cleaning, transformation, and ML preparation workflows, helping end users quickly find and access relevant datasets. Deployed with React frontend and containerized backend using Docker and Kubernetes.

Vertex AILangChainAutoMLTensorFlowReactDockerKubernetes

Performance Metrics

95%

Automation

<2s

Query Speed

1000+

Datasets

50+

Daily Users

Dataset: Enterprise Data Catalog (1000+ datasets)

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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)

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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)

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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)

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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)

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