⚽ FIFA Player Analytics Dashboard

An interactive and modular Streamlit dashboard for exploring, visualizing, and predicting FIFA player statistics.
This project showcases advanced skills in data visualization, caching, state management, and AI model integration, following professional engineering standards and a clean modular architecture.

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🌟 Overview

This dashboard allows users to:

• Load and clean FIFA player data from local or remote sources.
• Explore the dataset through interactive filters and Plotly visualizations.
• Analyze trends in player performance, value, and potential.
• Predict player potential using a pre-trained Machine Learning model.

Built with a focus on:

• Modular Streamlit architecture
• Optimized caching and session state management
• Professional UI/UX principles
• Deployment-ready structure

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🧩 Project Structure

proyecto_fifa/ │ ├── app.py # Main Streamlit app controller │ ├── modules/ # Modularized page components │ ├── page_intro.py # Page 1 - Data loading & cleaning │ ├── page_data_viz.py # Page 2 - Data visualization & filters │ └── page_model_inference.py # Page 3 - Predictive model inference │ ├── assets/ │ ├── dataset_description.html # HTML dataset dictionary (from Kaggle) │ └── model_fifa.pkl # Pre-trained model (generated locally) │ ├── data/ │ └── players_21.csv # Local FIFA dataset │ ├── create_dummy_model.py # Script to train and save a regression model ├── requirements.txt # Dependencies for reproducibility └── README.md # Project documentation

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⚙️ Installation & Setup

1️⃣ Clone the repository

git clone https://github.com/superTay/fifa-player-analytics.git
cd fifa-player-analytics


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## ⚙️ Installation & Setup

### 1️⃣ Clone the repository
```bash
git clone https://github.com/superTay/fifa-player-analytics.git
cd fifa-player-analytics

2️⃣ Install dependencies
pip install -r requirements.txt

3️⃣ Generate the model (if not already created)
python create_dummy_model.py

4️⃣ Run the Streamlit app
streamlit run app.py

🧠 Architecture & Functionality

🏁 Page 1 — Introduction

Loads and cleans the dataset with @st.cache_data.

Displays sample data and metadata using st.expander.

Shows dataset dictionary embedded via HTML.

Initializes st.session_state['df'] for global access.

📊 Page 2 — Data Visualization

Includes interactive filters (multiselects, sliders).

Uses @st.cache_data for unique value caching.

Stores filtered DataFrame in st.session_state['df_fil'].

Generates responsive Plotly charts (scatter, histogram, box, bar).

Displays results dynamically based on user filters.

🤖 Page 3 — Predictive Model

Loads a pre-trained regression model (LinearRegression) via joblib.

Allows users to input player attributes (age, overall, value_eur, etc.).

Predicts future potential rating interactively.

Uses @st.cache_resource for efficient model loading.

💾 Caching & State Management

Purpose	Implementation
Dataset caching	@st.cache_data
Model caching	@st.cache_resource
Session control	st.session_state['df'], st.session_state['df_fil']
Filtering	Persistent across interactions
Performance	Optimized data loading and resource reuse

🎨 UI/UX Principles

Dark theme aesthetic for data visualization.

Clear visual hierarchy with emoji section titles.

Sidebar-based navigation for intuitive flow.

Use of st.expander, st.columns, and st.form for a clean layout.

Responsive design for desktop and wide-screen displays.

📈 Tech Stack

Area	Technology
Frontend	Streamlit

Data	Pandas, NumPy
Visualization	Plotly Express
Machine Learning	Scikit-learn (Linear Regression)
Model Handling	Joblib
Deployment	Streamlit Cloud / Render / Docker-ready

🧮 Model Overview

A lightweight Linear Regression model trained with:

y ~ overall + age + value_eur


Predicts a player’s potential rating (potential).

Demonstrates clean separation between training (in create_dummy_model.py) and inference (in page_model_inference.py).

Cached and reusable for future sessions.

🚀 Future Enhancements

Replace dummy model with an advanced Random Forest or XGBoost.

Integrate real-time data via an API (e.g. sofifa.com).

Add KPIs (average overall, mean market value, etc.).

Extend the model for player clustering or similarity search.

Include authentication for personalized dashboards.

🧠 Key Learning Outcomes

This project demonstrates proficiency in:

🧩 Streamlit architecture design (multi-page modularization)

⚙️ Session state & caching optimization

🎨 Data visualization and UI/UX

🧠 Model inference integration

🧾 Professional software documentation & Git version control

🧑‍💻 Author

Christian Marzal Della Rovere
🎓 Full Stack AI Developer (in progress)
🌐 LinkedIn
 • GitHub

🏷️ GitHub Topics

#streamlit #data-visualization #machine-learning #fifa21
#dashboard #ai #python #fullstack

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