RegressioNest-Pakistan-HomePricer

This repository contains the code for building a real estate price prediction website. The project is divided into three main components:

1. Model Building: Using the Banglore home prices dataset from Kaggle.com, we construct a predictive model using Scikit-learn and linear regression.

2. Flask Server: A Python Flask server is developed to utilize the trained model and serve HTTP requests.

3. Website: The front-end interface is built using HTML, CSS, and JavaScript, allowing users to input home square footage, number of bedrooms, etc., and obtain the predicted price by calling the Flask server.

Key Features and Concepts Covered

• Data loading and cleaning
• Outlier detection and removal
• Feature engineering
• Dimensionality reduction
• GridSearchCV for hyperparameter tuning
• K-fold cross-validation

Technologies and Tools Used

1. Python
2. Numpy and Pandas for data cleaning
3. Matplotlib for data visualization
4. Scikit-learn for model building
5. Jupyter Notebook
6. Python Flask for HTTP server

Feel free to explore the code and adapt it for your own real estate prediction projects!