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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 Columns Related to Future Matches

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