#2 Data Preparation | Football Player Performance Prediction

Summary

In this video, Vishwajit continues from a previous video, explaining how he tackled a machine learning project. He demonstrates loading data, preprocessing it, and handling missing values in a dataset of player match statistics. Using SQLite for data storage, he describes identifying numerical and categorical columns, merging data for individual players, and creating a new dataset. Additionally, he showcases the implementation of a custom class to streamline data processing and sets up the groundwork for future steps like train-test splitting and model building, which will be covered in the next video.

Takeaways

• 🔧 The video continues from the previous one, focusing on explaining code related to loading, visualizing, and processing data.
• 📊 The dataset used is in SQLite format, containing around 1,83,978 records with 42 columns, representing player match data.
• 🧮 The host checks data types and identifies both numerical and categorical attributes in the dataset.
• 🚫 Null values are addressed by identifying columns with missing data and removing rows with extreme levels of null values.
• 👥 The dataset contains multiple entries for each player, and the objective is to merge data for each player and predict their overall rating.
• 🧠 The host created a class to merge data using the mean for numerical values and mode for categorical values, resulting in compressed data for each unique player.
• 🔄 The clean and merge process is designed to prepare the data for both training and deployment phases, ensuring the same transformations are applied to test data.
• 🎯 The target variable is the player's overall rating, which is calculated by taking the mean of multiple entries for each player.
• 🚀 After initial preprocessing, the next step is to perform a train-test split to further prepare the data for machine learning.
• 📽️ The video ends with the promise of another video focusing on the machine learning process, particularly the train-test split and model training.

Q & A

What is the main focus of the video?

The video focuses on explaining how the creator approached a machine learning project, including loading, cleaning, and visualizing the data, as well as preparing it for a predictive model.

What type of data is used in the project?

The data used is stored in SQLite format and contains information about player match statistics, including numerical, categorical, and target variables related to player performance.

How does the creator handle missing or null values in the dataset?

The creator identifies columns with null values and drops rows that have too many null values. Additionally, they use imputation strategies, such as using the mean for numerical columns and the mode for categorical columns.

What is the purpose of the 'clean and merge' class?

The 'clean and merge' class is used to merge multiple entries for the same player into a single row. It uses the mean for numerical attributes and the mode for categorical attributes to combine the data effectively.

What challenge does the creator face with merging data, and how is it addressed?

The challenge is to combine data from multiple rows that represent the same player's different matches. This is addressed by calculating the mean for numerical values and the mode for categorical values, which results in one row per player.

Why does the creator use a class-based approach for data cleaning?

The creator uses a class-based approach to make the code reusable and modular. This allows them to apply the same cleaning process during training and later when transforming test data.

How many unique players are in the dataset after the merging process?

After merging the data, the creator has 11,062 unique players in the dataset, which was previously spread across 183,978 rows.

What is the target variable in this project, and how is it handled?

The target variable is the 'overall rating' of each player. The creator merges the target variable using the mean for each player across different matches.

How does the creator prepare the data for modeling?

The creator preprocesses the data by handling null values, merging data, and splitting it into training and testing sets. They also ensure that both the features and the target are sequentially aligned for each player.

What is the next step in the project after data preprocessing?

The next step, as mentioned by the creator, is to perform a train-test split and apply machine learning models. This will be covered in the upcoming video.