https://github.com/sameem97/python-analysis-project
https://github.com/sameem97/python-analysis-project
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/sameem97/python-analysis-project
- Owner: sameem97
- Created: 2025-04-16T11:17:43.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-16T13:49:56.000Z (about 1 year ago)
- Last Synced: 2025-05-17T21:08:11.938Z (about 1 year ago)
- Language: Python
- Size: 249 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Python Analysis Project
## Project Overview
This project is designed to analyse housing data in London. It includes data processing, analysis, and visualisation to extract meaningful insights from the dataset provided.
## Project Structure
```plaintext
python-analysis-project/
├── analysis.py # Main script for data analysis
├── Python Analysis Project.pdf # Project documentation (if applicable)
├── README.md # Project overview and instructions
├── requirements.txt # File listing project dependencies
├── data/ # Directory containing data files
│ ├── houses-in-london.zip # Compressed dataset
│ └── london_houses.csv # CSV file with housing data
```
## Prerequisites
- Python 3.8 or higher
- Recommended: A virtual environment to manage dependencies
## Setup Instructions
1. Clone the repository:
```bash
git clone
cd python-analysis-project
```
2. Create and activate a virtual environment:
```bash
python3 -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Unzip the dataset (if not already unzipped):
```bash
unzip data/houses-in-london.zip -d data/
```
## Usage
Run the analysis script:
```bash
python analysis.py
```
Choose the CLI option for the metric you want to analyse. A corresponding visualisation will be generated and displayed.
## CLI Options
When you run the script, you will be presented with the following options in the CLI:
1. **Show Price Distribution**: Displays a histogram of house prices in London.
2. **Show Average Price by Neighborhood**: Displays a bar plot of average house prices by neighborhood.
3. **Show Price vs Square Meters**: Displays a scatter plot showing the relationship between property size and price.
4. **Show Correlation Heatmap**: Displays a heatmap of correlations between numerical features in the dataset.
5. **Exit**: Exits the CLI application.
## Data
The dataset (`london_houses.csv`) contains information about housing in London. Ensure the data is properly formatted and located in the `data/` directory.
## Credits
The dataset used in this project is sourced from Kaggle: [Houses in London](https://www.kaggle.com/datasets/oktayrdeki/houses-in-london/data?select=london_houses.csv).