This project focuses on analyzing two datasets, namely 'results' and 'stats,' covering the Premier League seasons from 2006-07 to 2017-18. The primary objective is to gain insights into team performance, statistics, and notable achievements during this period.

• Pandas: Employed for data manipulation tasks, such as filtering, grouping, and aggregating data, facilitating a detailed analysis.

• NumPy: Used for numerical operations and array manipulations, providing a foundation for efficient data processing.

• Matplotlib: Used for creating visualizations to illustrate key findings and trends, including team performance, goal statistics, and other relevant metrics.

The analysis places a particular emphasis on the 2007-08 season. This season was chosen due to the noteworthy achievement of my favorite team winning the double (both the Premier League and Champions League).

1. Data Import:

   • Imported 'results' and 'stats' datasets in CSV format.
   • Utilized Pandas to create DataFrames for easy data manipulation.

2. 2007-08 Season Analysis:

   • Filtered data to focus specifically on the 2007-08 season.

3. Pandas Analysis:

   • Utilized Pandas for in-depth data manipulation, including filtering, grouping, and aggregation.

4. Data Processing with NumPy:

   • Utilized NumPy for numerical operations and array manipulations, enhancing efficiency in data processing.

5. Data Visualization:

   • Leveraged Matplotlib to create informative visualizations highlighting key aspects of the Premier League data.

Further details and insights can be found in the project notebook.