Introduction

Getting started with Google Colab

Step 1 Importing the necessary libraries

Step 2 Downloading, Decompressing, and Preparing the ESPN Soccer Data

Step 3 Displaying the ESPN database schema

Step 4 Loading fixtures.csv, teamStats.csv, standings.csv, and leagues.csv

Step 5 Exploratory Analysis

Overall analysis of fixture results

Overall analysis of the results of teamStats

Overall analysis of standings

Distribution analysis for leagues

Step 6 Checking for inconsistencies in Standings.csv

Step 7 Checking for inconsistencies in teamStats.csv

Step 8 Checking for inconsistencies in leagues.csv

Step 9 Checking for inconsistencies in fixtures.csv

Step 10 Merging and Joins - Consolidating Data for Modeling

Step 11 Handling Missing Values (NaN) and Optimizing Data Quality

Imputation Using Bayesian Linear Regression

Validation and Cleaning of Team Standings Data

Removing Non-Relevant Competitive Context Columns

Removing Non-Relevant Update-Related Columns

Final Data Integrity Check

Step 12 Data Enrichment with Derived Variables and Performance Indicators

Step 13 Transforming Categorical Variables

Step 14 – Standardizing Numerical Data

Step 15 Analyzing Variable Importance and Feature Selection

Step 16 Training and Validating the Score Prediction Model

Evaluating the Prediction Model's Performance

Step 17 – Adapting and Re-training the Model Based on the Football API Data

Step 18 Storing on Drive

Installing PyCharm

Web Application Structure

API.football.com

Back-end integration

Front-end integration

Launching the application