https://github.com/camilajaviera91/prediction-of-housing-prices-using-linear-regression

This project provides tools to search for datasets on Kaggle, download and preprocess them, and perform predictions using a Linear Regression model. It includes interactive text-based user interfaces built with `curses`.
https://github.com/camilajaviera91/prediction-of-housing-prices-using-linear-regression

curses kaggle linear-regression matplotlib-pyplot mean-absolute-error mean-square-error numpy pandas pathlib python scikit-learn train-test-split

Last synced: 7 months ago
JSON representation

This project provides tools to search for datasets on Kaggle, download and preprocess them, and perform predictions using a Linear Regression model. It includes interactive text-based user interfaces built with `curses`.

• Host: GitHub
• URL: https://github.com/camilajaviera91/prediction-of-housing-prices-using-linear-regression
• Owner: CamilaJaviera91
• Created: 2024-12-16T16:07:06.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-12-19T14:07:23.000Z (11 months ago)
• Last Synced: 2025-04-12T20:58:29.310Z (7 months ago)
• Topics: curses, kaggle, linear-regression, matplotlib-pyplot, mean-absolute-error, mean-square-error, numpy, pandas, pathlib, python, scikit-learn, train-test-split
• Language: Python
• Homepage:
• Size: 397 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Kaggle Dataset Fetcher and Predictor

This project provides tools to search for datasets on Kaggle, download and preprocess them, and perform predictions using a Linear Regression model. It includes interactive text-based user interfaces built with `curses`.

## Features

- Search for datasets on Kaggle interactively.

- Download datasets and automatically extract files.

- Load datasets into a pandas DataFrame and preprocess them.

- Train a Linear Regression model and evaluate it using RMSE and MAE.

- Visualize results with scatter plots.

---

## Prerequisites

1. **Python**: Python 3.7 or higher.

2. **Install Required Libraries**:




   

   ```bash

   pip install pandas numpy matplotlib scikit-learn kaggle

   ```

## Set Up Kaggle API:

- Go to Kaggle Account.

- Download the kaggle.json API token.

- Place it in ~/.kaggle/ (Linux/Mac) or %USERPROFILE%\.kaggle\ (Windows).

## File Structure

```bash

.

├── kaggle_connect.py  # Handles dataset search and download via Kaggle API.

├── prediction.py      # Performs data preprocessing, model training, and visualization.

└── README.md          # Documentation for the project.

```

## Usage

### Step 1: Search and Download a Dataset

Run the following command to search, download a kaggle dataset and prediction script:

```bash

python prediction.py

```

Or

```bash

python3 prediction.py

```

Follow the interactive prompts:

1. Enter a search term for datasets (e.g., Boston Housing Dataset).

2. Select a dataset from the list.

3. Specify a folder to store the downloaded files.

### Step 2: Train and Test a Linear Regression Model

The script:

4. Displays descriptive statistics of the data.

5. Splits the data into training and testing sets.

6. Trains a Linear Regression model and evaluates its performance.

7. Displays a scatter plot comparing actual and predicted values.

## Example Output

### Terminal Interface

**Dataset Selection**

![Boston example 1](./images/1.png)

![Boston example 2](./images/2.png)

![Boston example 3](./images/3.png)

![Boston example 4](./images/4.png)

![Boston example 5](./images/5.png)

![Boston example 6](./images/6.png)

![Boston example 7](./images/7.png)




**Model Metrics**

![Boston example 8](./images/8.png)

### Scatter Plot

![Boston example 9](./images/9.png)

## Contributions

Contributions are welcome! Feel free to submit issues or pull requests to enhance the functionality.