machinelearning
🚀 Built a House Price Prediction Model using Python & Machine Learning
I built a House Price Prediction project as part of my Data Science Internship at Oasis Infobyte . 📌 Project Overview The goal of this project is to predict house prices based on different property-related features using Machine Learning . 🔄 Project Workflow Dataset → Data Cleaning → EDA → Feature Selection → Model Training → Evaluation → Price Prediction 🧹 Data Preparation Loaded and explored the dataset using Pandas Handled missing and inconsistent data Prepared the dataset for machine learning 📊 Exploratory Data Analysis Analyzed relationships between different features Created visualizations to identify patterns and trends Studied factors affecting house prices 🤖 Machine Learning Selected relevant features Split the dataset into training and testing sets Trained a regression model Evaluated the model using appropriate performance metrics 🎯 Prediction The trained model takes property-related features as input and generates an estimated house price . 🛠️ Tech Stack Python Pandas NumPy Matplotlib Scikit-learn Jupyter Notebook 💡 Key Learning This project gave me hands-on experience with the complete machine learning workflow — from data preprocessing and visualization to model training, evaluation, and prediction . 🔗 GitHub Repository 👉https://github.com/pallavisagar07/OIBSIP/tree/ec73e94f0a9d2ea3a3fe3e65c1676d8c0af17219/DataAnalytics-L2-House-Price-Prediction-Linear-Regression Python MachineLearning DataScience DataAnalytics ScikitLearn OasisInfobyte ProjectShowcase
🚀 Built a Basic RAG and an Advanced RAG System from Scratch — Without LangChain
To understand how RAG works internally, I first built a Basic RAG Pipeline: Basic RAG PDF → Extraction → Chunking → Embeddings → FAISS Search → Top-K Chunks → Prompt → LLM → Answer It works well for simple questions, but struggles with exact keywords, IDs, and complex queries. So I built an Advanced RAG Pipeline: Advanced RAG PDF → Extraction → Chunking → Embeddings → Dense + Sparse Index → Query Understanding → Dense Retrieval + Sparse Retrieval → Reciprocal Rank Fusion (RRF) → Cross-Encoder Reranking → Noise Filtering → Context Fusion → Prompt → LLM → Answer Key Improvements ✅ Semantic Search (FAISS) ✅ Exact Keyword Search (BM25) ✅ Hybrid Retrieval ✅ Cross-Encoder Reranking ✅ Duplicate Removal ✅ Better Context Selection ✅ Reduced Hallucinations One of the biggest lessons from this project: A better RAG system isn't always about using a better LLM. It's often about building a better retrieval pipeline. Tech Stack: Python • Streamlit • FAISS • Sentence Transformers • BM25 • Cross-Encoder Reranker • Groq RAG GenAI LLM AIEngineering MachineLearning Python FAISS BuildInPublic
HeritageAI
Building HeritageAI 🎙️🤖 — a voice-controlled PowerPoint assistant that lets presenters navigate slides hands-free using natural voice commands. Currently improving it with smarter speech interpretation and AI-powered presentation features. 🚀 🔗 https://bit.ly/4Aj9LP0 AI MachineLearning VoiceAI Python HeritageAI