dataanalytics

🚀 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