Data Scraping and Analysis for [Knowing Euro 2024 Winner]

• Introduction
• Dataset
• Requirements
• Installation
• Usage
• Steps
  • Data Scraping
  • Data Analysis
• Results
• Authors

This project involves scraping data from [specific website or source] and analyzing it to extract meaningful insights. The goal is to [briefly describe the goal of the analysis].

The dataset is obtained by scraping [specific website or source]. It includes features such as [list some key features].

• Python 3.7 or above
• Jupyter Notebook
• Requests
• BeautifulSoup4
• Pandas
• Matplotlib
• Seaborn

1. Clone the repository:

   git clone https://github.com/yourusername/project.git
   cd project

2. Create a virtual environment:

   python -m venv venv

3. Activate the virtual environment:

   • On Windows:

     venv\Scripts\activate

   • On macOS and Linux:

     source venv/bin/activate

4. Install the required packages:

   pip install -r requirements.txt

1. Open the Jupyter Notebook:

   jupyter notebook

2. Load the provided notebooks in the following order:

   • 1 - Data Scraping.ipynb
   • 2 - Data Analysis.ipynb

3. Run the cells sequentially to perform the data scraping and analysis.

1. Setup: Import necessary libraries and set up configurations.
2. Fetching Data: Use the Requests library to fetch data from [specific website or source].
3. Parsing Data: Use BeautifulSoup4 to parse the HTML content and extract relevant information.
4. Storing Data: Store the extracted data in a structured format using Pandas.

1. Loading the Dataset: Load the scraped dataset into a Pandas DataFrame.
2. Data Cleaning: Handle missing values, correct data types, and perform any necessary data transformations.
3. Exploratory Data Analysis (EDA): Generate various plots to visualize the data.
   • Distribution of [key feature]
   • Relationships between [key features]
4. Feature Engineering: Create new features if needed.
5. Answering Specific Questions: Perform targeted analyses to answer specific questions related to your topic.

• Key Insight 1: Describe the first key insight from the analysis.
• Key Insight 2: Describe the second key insight from the analysis.
• Additional Insights: List any additional insights or findings.

• El-Ouardi Oualid