All Projects
A complete collection of my work in AI, ML, and web development
LCEL Chatbot
Why This Project: Created to explore the potential of LangChain's Expression Language in building sophisticated conversational AI systems that can maintain context and provide meaningful responses.
Project Overview
This chatbot leverages the power of LangChain Expression Language to create flexible conversation flows with memory capabilities and context management. The implementation uses Streamlit for an intuitive user interface that handles real-time interactions.
Technical Implementation
Key Features
- Context-aware conversations with memory retention
- Custom prompt templates optimized for natural dialogue
- Seamless integration with multiple LLM providers
- Real-time response generation with streaming capability
Challenges & Solutions
The primary challenges included designing effective prompt engineering strategies, managing conversation context over multiple turns, and creating a responsive UI for real-time interactions.
These were addressed through implementation of buffer memory systems, custom prompt templates, and optimized Streamlit components for chat experiences.
BERT Visualization
Why This Project: Developed to bridge the gap between complex transformer architectures and human understanding, making AI model interpretability accessible to researchers and students.
Project Overview
This project aims to demystify BERT's black-box nature by providing visualizations of attention patterns, token embeddings, and layer representations. The tool helps researchers and practitioners understand how BERT processes and interprets language.
Technical Implementation
Key Features
- Multi-layer attention visualization with heat maps
- Token embedding space projection using t-SNE/UMAP
- Interactive exploration of self-attention mechanisms
- Contextual word embedding analysis
Challenges & Solutions
Extracting meaningful data from BERT's complex architecture required careful implementation to access intermediate layers and attention mechanisms. The visualization interface needed to be both informative and intuitive for users without compromising performance.
Customer Churn Analysis
Why This Project: Developed to provide businesses with actionable insights into customer retention, leveraging machine learning to predict and analyze churn behavior.
Project Overview
This project utilizes advanced machine learning algorithms to identify patterns in customer behavior that indicate potential churn. The analysis includes data preprocessing, feature engineering, model selection, and evaluation with actionable insights for business stakeholders.
Technical Implementation
Key Features
- Multi-model comparison (Random Forest, XGBoost, Logistic Regression)
- Feature importance analysis for business insights
- Customer segmentation based on churn risk
- Interactive visualizations for pattern discovery
- ROC curve and confusion matrix evaluation
Challenges & Solutions
Handling imbalanced class distribution in churn data required implementing SMOTE for synthetic sampling. Feature engineering involved creating complex behavioral indicators from raw transactional data. The model selection process required extensive hyperparameter tuning to maximize both precision and recall.
Results
The final model achieved 89% accuracy with 0.92 AUC, identifying ~78% of customers at risk of churning, allowing for targeted retention strategies that reduced churn by an estimated 23% in validation tests.
YouTube Summarizer
Why This Project: Built to demonstrate the capabilities of generative AI in processing and summarizing video content, enhancing information accessibility.
Project Overview
This project focuses on building a summarization tool that can process YouTube video content and generate concise summaries, aiding in quick content consumption.
Technical Implementation
Key Features
- Extracts video transcripts and generates summaries
- Supports multiple languages and video formats
- Customizable summary length and detail level
- Integration with Notion for saving summaries
Challenges & Solutions
Key challenges included handling various video formats, ensuring accurate transcript extraction, and generating coherent summaries. These were addressed by leveraging robust APIs and implementing effective error handling and data validation mechanisms.
NLP Resume Parser
Why This Project: Created to automate the extraction of key information from resumes, streamlining the recruitment process and improving candidate screening efficiency.
Project Overview
This project aims to automate the extraction of key information from resumes, such as skills, experience, and education, to facilitate efficient candidate screening.
Technical Implementation
Key Features
- Extracts and categorizes resume information
- Supports multiple resume formats (PDF, DOCX, etc.)
- Customizable extraction templates
- Integration with HR systems for seamless workflow
Challenges & Solutions
Challenges included dealing with the variability in resume formats and structures, and ensuring high accuracy in information extraction. These were tackled by using a combination of rule-based and machine learning approaches for robust parsing capabilities.
OpenAI Chatbot
Why This Project: Developed to showcase the capabilities of OpenAI's language models in creating interactive and intelligent chatbot applications.
Project Overview
This project involves creating a chatbot application that utilizes OpenAI's language models to engage in human-like conversations, answer questions, and provide information on various topics.
Technical Implementation
Key Features
- Natural language understanding and generation
- Contextual awareness for follow-up questions
- Multi-turn conversation handling
- Customizable response templates
Challenges & Solutions
Integrating with OpenAI's API and managing the conversation context were the main challenges. These were addressed by implementing a robust backend in Flask to handle API requests and session management.
Ollama LLM Chatbot
Why This Project: Created to explore the use of local LLMs for building chatbots that prioritize user privacy and data security.
Project Overview
This project focuses on building a chatbot that runs locally using Ollama, ensuring privacy and data security for sensitive conversations.
Technical Implementation
Key Features
- Local deployment for enhanced privacy
- Customizable conversation flows
- Integration with local data sources
Challenges & Solutions
Optimizing the LLM for local performance and ensuring seamless user interactions were the key challenges. These were addressed by fine-tuning the model and optimizing the Flask application for local server deployment.
Apple Website Clone
Why This Project: Developed to master advanced frontend techniques by replicating one of the most sophisticated and visually appealing websites, focusing on animations, responsive design, and user experience.
Project Overview
This frontend project recreates Apple's homepage with meticulous attention to detail, implementing smooth scrolling animations, responsive layouts, and interactive elements using modern web technologies without any backend dependencies.
Technical Implementation
Key Features
- Pixel-perfect responsive design across all devices
- Smooth scroll animations and transitions using GSAP
- Interactive product showcases and hover effects
- Optimized performance with lazy loading
- Cross-browser compatibility and accessibility
Challenges & Solutions
Replicating Apple's sophisticated visual effects and ensuring smooth performance across devices were major challenges. These were solved through advanced CSS techniques, optimized JavaScript animations, and careful performance profiling to maintain 60fps animations.
YouTube Clone
Why This Project: Built to demonstrate proficiency in React development and API integration by recreating one of the world's most popular video platforms with modern frontend technologies.
Project Overview
This frontend application recreates YouTube's core interface and functionality using React, featuring video search, playback, and responsive design while integrating with YouTube's official API for real video content.
Technical Implementation
Key Features
- Real-time video search with YouTube API integration
- Responsive video player with custom controls
- Dynamic video recommendations and suggestions
- Mobile-first responsive design
- Component-based architecture for scalability
Challenges & Solutions
Managing API rate limits, implementing smooth video playback, and creating a responsive layout that works across all devices were key challenges. Solutions included efficient API call optimization, custom video player implementation, and flexible CSS Grid/Flexbox layouts.
IMDB Sentiment Analysis
Why This Project: Developed to explore sentiment analysis techniques and their application in understanding public opinion and improving movie recommendation systems.
Project Overview
This project implements a sentiment analysis pipeline that processes IMDB movie reviews, extracting features and training models to predict sentiment polarity.
Technical Implementation
Key Features
- Text preprocessing and feature extraction
- Model training and evaluation (SVM, Naive Bayes, LSTM)
- Sentiment score visualization
- Integration with Flask for web demo
Challenges & Solutions
Challenges included handling imbalanced data, selecting appropriate features, and tuning model hyperparameters for optimal performance. These were addressed through careful data preprocessing, feature engineering, and extensive model evaluation.
Next Word Predictor
Why This Project: Created to demonstrate the capabilities of NLP in understanding context and generating human-like text, aiding writers and content creators.
Project Overview
This project focuses on building a next-word prediction model that suggests the most likely next word in a given text context, enhancing writing efficiency and creativity.
Technical Implementation
Key Features
- Contextual word prediction using LSTM/GRU networks
- Customizable model training with user data
- Integration with text editors and IDEs
- Web demo for real-time predictions
Challenges & Solutions
Key challenges included training the model to understand context over long text spans and optimizing the prediction speed. These were addressed by using advanced RNN architectures and optimizing the model inference pipeline.
mathGPT
Why This Project: Created to demonstrate the power of specialized AI models in solving mathematical problems, making advanced math assistance accessible to students and professionals.
Project Overview
mathGPT is an intelligent mathematical assistant that leverages Google's Gemma transformer model to solve complex mathematical problems across various domains including algebra, calculus, statistics, and more.
Technical Implementation
Key Features
- Solves complex mathematical equations and problems
- Step-by-step solution explanations
- Support for multiple mathematical domains
- Interactive problem-solving interface
Challenges & Solutions
Fine-tuning the Gemma model for mathematical reasoning and ensuring accurate problem-solving across different mathematical concepts were key challenges. These were addressed through specialized prompt engineering and model optimization techniques.
searchBOT-AI
Why This Project: Developed to create an intelligent search assistant that can process and retrieve information from diverse data sources, enhancing research and information discovery workflows.
Project Overview
searchBOT-AI is an advanced search assistant built with Streamlit that combines natural language processing with intelligent information retrieval capabilities across multiple data formats and sources.
Technical Implementation
Key Features
- Multi-format data source integration
- Intelligent query understanding and processing
- Real-time search and retrieval capabilities
- Interactive web interface with Streamlit
- Contextual response generation
Challenges & Solutions
Integrating multiple data sources and ensuring accurate information retrieval across different formats presented significant challenges. These were addressed through implementation of robust data preprocessing pipelines and advanced search algorithms.
SQL DB Agent
Why This Project: Created to bridge the gap between natural language and database queries, making database interaction accessible to non-technical users through intelligent query translation.
Project Overview
This SQL agent intelligently converts natural language questions into SQL queries, executes them against a database, and returns human-readable results, democratizing database access for business users.
Technical Implementation
Key Features
- Natural language to SQL query conversion
- Intelligent query validation and optimization
- Support for complex database operations
- Error handling and query refinement
- Interactive query interface
Challenges & Solutions
Understanding natural language intent and mapping it to correct SQL syntax while handling database schema complexity were major challenges. These were solved through advanced NLP techniques, schema analysis, and iterative query refinement processes.
Deployed Chatbot
Why This Project: Developed to showcase end-to-end chatbot deployment capabilities, from development to production, emphasizing scalability and real-world performance optimization.
Project Overview
This project demonstrates a complete chatbot deployment pipeline using Groq API, featuring production-ready architecture with monitoring, scaling, and maintenance considerations for enterprise-level applications.
Technical Implementation
Key Features
- High-performance inference with Groq API
- Scalable deployment architecture
- Real-time conversation capabilities
- Production monitoring and logging
- Error handling and fallback mechanisms
Challenges & Solutions
Ensuring reliable performance under varying loads and implementing proper error handling for production environments were key challenges. Solutions included implementing robust API rate limiting, caching strategies, and comprehensive monitoring systems.
Website & YouTube Summarizer
Why This Project: Built to address information overload by providing intelligent summarization of both video and web content, enabling efficient content consumption and knowledge extraction.
Project Overview
This dual-purpose summarization tool leverages LangChain and Groq APIs to extract, process, and summarize content from both YouTube videos and websites, making information consumption more efficient and accessible.
Technical Implementation
Key Features
- Multi-source content processing (YouTube + Web)
- Intelligent text extraction and preprocessing
- Customizable summarization length and style
- Batch processing capabilities for multiple sources
- Export summaries in various formats
Challenges & Solutions
Handling diverse content formats, ensuring accurate transcript extraction from videos, and maintaining summary quality across different content types were primary challenges. These were addressed through robust preprocessing pipelines, advanced chunking strategies, and optimized prompt engineering.