https://github.com/mohammed-majid/housing-price-prediction
Housing Price Prediction using a sample Dataset from Kaggle
https://github.com/mohammed-majid/housing-price-prediction
fullstack kaggle linear-regression machine-learning
Last synced: 3 months ago
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Housing Price Prediction using a sample Dataset from Kaggle
- Host: GitHub
- URL: https://github.com/mohammed-majid/housing-price-prediction
- Owner: Mohammed-Majid
- Created: 2024-07-12T16:15:11.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-07-13T21:46:21.000Z (11 months ago)
- Last Synced: 2025-01-17T05:08:30.492Z (5 months ago)
- Topics: fullstack, kaggle, linear-regression, machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 255 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Housing Price Prediction Using Kaggle Dataset
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This repository contains code for a regression model that predicts housing prices. The Project also contains code for a frontend (streamlit).
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## Overview
The Project involves the following
- Pre-processing: EDA, Feature engineering and Outlier Removal.
- Modeling: K-Fold Cross-Validation, Grid Search (Multiple models) and building a predictive model.
- Evaluation: Testing the Model.
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## Usage
- Clone the Repository
- Install Dependencies: Make sure you have the necessary dependencies installed. You can install them using pip:
```
pip install numpy pandas matplotlib scikit-learn pickle
```
- Run app.py: Execute the script to launch a streamlit app on local host
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## Requirements
- Python 3.x
- numpy
- pandas
- Pickle
- Scikit-Learn
- matplotlib
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## Project Structure
- app.py: streamlit application
- model.ipynb: Regression Model
- model.pickle: Persisted Model used in streamlit application
- util.py: Main Functions used to import and run model
- house.csv: Dataset
- columns.json: Feature Vector