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This project focuses on analyzing and classifying football players based on their performance metrics. It involves importing and preprocessing data, conducting exploratory data analysis, and building predictive models like Random Forest and K-Nearest Neighbors (KNN) to classify players. The insights gained can support decision-making

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jeffreykwakye/Football-Players-Analysis-and-Classification

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Data Analysis of Football Player Attributes

Project Overview:

In this project, I conducted a comprehensive data analysis of football player attributes using Python and the Pandas library. The dataset, sourced from a Google Sheets link, contains detailed information about various football players, including their nationalities, ages, preferred positions, and specific skill ratings.

Key Objectives:

  1. Data Loading and Inspection: o Loaded the dataset from a Google Sheets link using Pandas. o Inspected the dataset to understand its structure and identify key columns for analysis.
  2. Numpy and Pandas Proficiency: o Demonstrated proficiency in using Numpy arrays and Pandas DataFrames for data manipulation and analysis. o Answered theoretical questions related to Numpy arrays and Pandas DataFrames.
  3. Data Analysis and Insights: o Identified the country with the oldest player. o Determined the attribute with the lowest average value for goalkeepers. o Analyzed the dataset to find the preferred position type with the most entries. o Identified the highest-rated player from Portugal under the age of 25.

Technical Skills Demonstrated:

• Python: Utilized Python for data loading, manipulation, and analysis. • Pandas: Leveraged Pandas for DataFrame operations, filtering, and aggregation. • SQL: Formulated and executed SQL queries for data retrieval and manipulation. • Data Visualization: (Optional) Created visualizations using Matplotlib or Seaborn to present key findings.

Conclusion:

This project showcases my ability to work with real-world datasets, perform detailed data analysis, and derive meaningful insights. It highlights my proficiency in Python, Pandas, and SQL, as well as my capability to communicate findings effectively.

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This project focuses on analyzing and classifying football players based on their performance metrics. It involves importing and preprocessing data, conducting exploratory data analysis, and building predictive models like Random Forest and K-Nearest Neighbors (KNN) to classify players. The insights gained can support decision-making

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