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Chinmay0303 · Oct 2, 2024 · 5c94c91 · 5c94c91
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 This project focuses on predicting the market values of football players. It is based on data scraped from the SOFIFA website and involves multiple stages including data collection, cleaning, feature engineering, and model creation.
 
 ## Project Overview
-Data Collection: Scraped player data from the SOFIFA website using Beautiful Soup.
-Data Cleaning & EDA: Used Pandas and NumPy for data manipulation and performed exploratory data analysis (EDA) with Matplotlib and Seaborn.
-Feature Engineering: Applied feature scaling and selection to prepare the data for the model.
-Model Creation: Built a Multiple Linear Regression model to predict football player market values.
+#1 Data Collection: Scraped player data from the SOFIFA website using Beautiful Soup.
+#2 Data Cleaning & EDA: Used Pandas and NumPy for data manipulation and performed exploratory data analysis (EDA) with Matplotlib and Seaborn.
+#3 Feature Engineering: Applied feature scaling and selection to prepare the data for the model.
+#4 Model Creation: Built a Multiple Linear Regression model to predict football player market values.