https://github.com/hk151109/house-price-prediction_using-linear-regression
This repository contains the implementation of a Linear Regression model to predict house prices based on features such as square footage, number of bedrooms, and bathrooms.
https://github.com/hk151109/house-price-prediction_using-linear-regression
datapreprocessing house-price-prediction linear-regression machine-learning
Last synced: 1 day ago
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This repository contains the implementation of a Linear Regression model to predict house prices based on features such as square footage, number of bedrooms, and bathrooms.
- Host: GitHub
- URL: https://github.com/hk151109/house-price-prediction_using-linear-regression
- Owner: hk151109
- Created: 2024-11-23T19:28:04.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-12-17T18:09:29.000Z (10 months ago)
- Last Synced: 2025-10-07T12:52:07.647Z (1 day ago)
- Topics: datapreprocessing, house-price-prediction, linear-regression, machine-learning
- Language: Python
- Homepage:
- Size: 8.59 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Linear Regression Model for House Price Prediction
This repository contains the implementation of a **Linear Regression model** to predict house prices based on various features, including square footage, the number of bedrooms, and bathrooms.
## Index
1. [Project Overview](#project-overview)
2. [Features](#features)
3. [File Structure](#file-structure)
4. [Steps to Run](#steps-to-run)
- [1. Data Cleaning](#1-data-cleaning)
- [2. Model Training and Prediction](#2-model-training-and-prediction)
5. [Visualization](#visualization)
6. [Dependencies](#dependencies)
7. [Results](#results)
8. [Dataset](#dataset)
9. [License](#license)
## Project Overview
The project includes:
- Preprocessing raw data for cleaning and feature preparation.
- Building a Linear Regression model enhanced with **Polynomial Features** to capture non-linear relationships.
- Evaluating the model's performance using metrics like **Mean Squared Error (MSE)** and **R-squared (R²)**.
- Visualizing model accuracy with **scatter plots**, **residual plots**, and **histograms**.
- Saving predictions to a well-structured CSV file.
## Features
- **Data Preprocessing**:
- Handling missing values and outliers.
- Log-transforming skewed data for better model performance.
- Encoding categorical features and scaling numerical features.
- **Model Training**:
- Using **Linear Regression** with Polynomial Features for non-linear relationships.
- **Visualization**:
- Plots for evaluating model performance and residual errors.
- **Predictions**:
- Final house price predictions saved in a CSV file with essential columns.
## File Structure
```
│ data_cleaning.py
│ PredictedPrice.csv
│ price_prediction.py
│
├───cleaned_dataset
│ test_cleaned.csv
│ train_cleaned.csv
│
└───house-prices_dataset
data_description.txt
test.csv
train.csv
```
## Steps to Run
### 1. Data Cleaning
Run the `data_cleaning.py` script to preprocess the raw datasets:
```bash
python data_cleaning.py
```
This will generate cleaned datasets (`train_cleaned.csv` and `test_cleaned.csv`) in the `cleaned_dataset` folder.
### 2. Model Training and Prediction
Run the `price_prediction.py` script to train the Linear Regression model and generate predictions:
```bash
python price_prediction.py
```
The predicted prices will be saved in `PredictedPrice.csv`.
## Visualization
The project includes visualizations to analyze model accuracy:
- **Scatter Plot**: Actual vs. Predicted values.
- **Residual Plot**: Residual errors.
- **Histogram**: Distribution of residuals.
These visualizations are automatically generated when running the `price_prediction.py` script.
## Dependencies
The following Python libraries are required:
- `numpy`
- `pandas`
- `scikit-learn`
- `matplotlib`
Ensure these libraries are installed in your Python environment:
```bash
pip install numpy pandas scikit-learn matplotlib
```
## Results
- The model achieved an R-squared score of `0.6208` on the training data (replace with your result).
- The predictions are saved in `PredictedPrice.csv`, containing only essential columns (`Id` and `SalePrice`).
## Dataset
Kaggle: https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/data.
The dataset is available in the `house-prices_dataset` folder, including:
- `train.csv`: Raw training data.
- `test.csv`: Raw test data.
- `data_description.txt`: Dataset documentation.
## License
This project is licensed under the MIT License.