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🚀 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 #ProjectShowcase1 / 4
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