This project involves developing a Linear regression model for the given Bengaluru housing price dataset (Link to the dataset: https://www.kaggle.com/amitabhajoy/bengaluru-house-price-data). The model is further saved as a .pickle file and deployed as a web app using Flask. HTML and CSS were used for web development tasks.
Tech Stack: Python, Linear Regression, Pandas, Scikit-learn, Flask, HTML, CSS.
- app : This folder contains files needed for web development.
- app.css: CSS code to design webpage.
- app.html: HTML code for developing webpage.
- house_bg.jpg: background image for the webapge.
- Bengaluru Housing Price Data Processed.csv: Dataset obtained after pre-processing stage.
- Bengaluru Housing Price Data.csv: Dataset in CSV format.
- Bengaluru_Housing_Price_Predictor.pickle: Model saved as .pickle file.
- Group2_Mishael,Saurav.ipynb: This notbook contains the model developed, trained and tested using Google Colab.
- columns.json: Column names in dataset in JSON format.
- server.py: This python file contains the code to set up a local server using Flask.
- util.py: This python file utilizes the model and predicts the housing price.
| Algorithm | Training Data Score (%) | Testing Data Score (%) |
|---|---|---|
| Linear Regression | 80.599 | 82.353 |
| Random Forest | 84.982 | 77.995 |
| Decision Tree | 83.756 | 77.356 |
