Data_Science_Project

A small end‑to‑end data science project demonstrating:

1. Data Collection via web scraping (BeautifulSoup, Scrapy, Selenium).
2. Data Storage of raw and processed files in data/.
3. Exploratory Data Analysis and visualization in a Jupyter notebook.

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Table of Contents

• Project Structure

• Prerequisites

• Installation

• Usage

  • Ad‑hoc Python Scrapers
  • Scrapy Spiders
  • Exploratory Data Analysis

• Directory Layout

• Contributing

• License

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Project Structure

Data_Science_Project/
├── data/                   # Raw and processed datasets (CSV, JSON, etc.)
├── scraping/               # Standalone Python scripts (BeautifulSoup, Selenium)
├── spider/                 # Scrapy project and spider definitions
├── data_analysis.ipynb     # Jupyter notebook for EDA & visualization
└── README.md               # Project overview and instructions

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Prerequisites

• Python 3.8 or higher
• Git (to clone this repository)

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Installation

1. Clone the repository

   git clone https://github.com/soupond/Data_Science_Project.git
   cd Data_Science_Project

   2. Create and activate a virtual environment (recommended) bashbash git clone https://github.com/soupond/Data_Science_Project.git cd Data_Science_Project

2. Create and activate a virtual environment (recommended)

   python3 -m venv venv
   source venv/bin/activate    # Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

   If a requirements.txt is not present, install manually:

   pip install pandas numpy matplotlib jupyter scrapy beautifulsoup4 requests selenium

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Usage

Ad‑hoc Python Scrapers

Standalone scripts using BeautifulSoup or Selenium are located in scraping/. To run one:

python scraping/bs4_scraper.py

The script will save output files under data/ (e.g., data/raw_listings.csv).

Scrapy Spiders

The Scrapy project lives in the spider/ directory. To crawl and export data:

Exploratory Data Analysis

Launch Jupyter Notebook and open the analysis notebook:

jupyter notebook data_analysis.ipynb

Inside, you’ll find:

• Data loading and cleaning steps
• Descriptive statistics and data summaries
• Visualizations (histograms, scatter plots, heatmaps)
• Key insights and recommendations

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Requirements

A requirements.txt file lists all Python package dependencies for this project. Install them with:

pip install -r requirements.txt

Contributing

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/YourFeature).
3. Make your changes and commit with a clear message.
4. Push to your fork and open a Pull Request.

Please ensure:

• Code follows PEP8 style guidelines.
• Dependencies are updated in requirements.txt.
• README is kept up to date with any new scripts or features.

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