Python
3 posts11 projects
- ResearchPilotA multi-agent research workflow that creates analyst personas, runs parallel interview loops with web search, and synthesizes the results into a cited report using LangGraph, LangChain, Groq, and Tavily.Project
- Understanding a Python Configuration Class From Environment Variables to Frozen DataclassesDeep dive into building robust, type-safe, immutable application configuration classes in modern Python.Post
- Understanding dataclass from first principlesUnderstanding how Python dataclasses work under the hood, why they exist, and how they reduce boilerplate.Post
- Why from __future__ import annotations Exists?Explaining deferred type evaluation in Python, PEP 563, and why from __future__ import annotations is essential.Post
- QuizlyAI MCQ Quiz GeneratorProject
- TripMateAIMulti-Agent Travel System with LangGraph, MCP, and Human-in-the-Loop OrchestrationProject
- PostDraftMulti-agent Linkedin Post Generator using LangGraphProject
- CodeBuddyAn AI-powered coding assistant built with LangGraph. It works like a multi-agent development team that can take a natural language request and transform it into a working project.Project
- Next word Prediction using LSTM and GRUDeveloped a deep learning model for predicting the next word in a given sequence of words, built using LSTM and GRU networks.Project
- Simple RNN for IMDb Sentiment AnalysisDeveloped an end-to-end deep learning project using a simple RNN on the IMDb movie reviews dataset.Project
- Kindle Review Sentiment AnalysisSentiment analysis on Amazon Kindle reviews using various NLP techniques: Implemented multiple text vectorization methods (BOW, TF-IDF, Word2Vec), built and compared different classification models, processed and analyzed the Kindle Reviews dataset.Project
- SMS Spam ClassificationA comprehensive spam detection system implementing multiple text classification approaches including Bag of Words (BOW), TF-IDF Vectorization with Multinomial Naive Bayes, Word2Vec with Random Forest ClassifierProject
- HR Insights at Salifort MotorsAnalyzed HR data to understand attrition drivers at Salifort Motors. Used EDA and ML (Random Forest, Decision Tree) for prediction. Achieved 96.2% accuracy, 93.8% AUC. Delivered actionable retention insights.Project
- Student Performance PredictionPredicted student academic outcomes using demographic and educational data. Applied EDA, preprocessing, and classification models. Helped identify at-risk students early.Project
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