Alzar AI 🎮🏆

This project is for my Artificial Intelligence class. It’s a GOTY (Game of the Year) predictor designed to guess the next award-winning game using AI.

"Alzar AI" is named after Alzara, the fortune-teller character from the 2025 game Blue Prince.

📁 Project Structure

├── Alzar-AI/
│   ├── data/
│   │   ├── Alzar_AI_Base_de_Dados.csv
│   │   └── Alzar_AI_Base_de_Dados.xlsx
│   ├── models/
│   │   ├── melhor_modelo_goty.pkl
│   │   └── scaler_goty.pkl
│   ├── scripts/
│   │   └── alzar_ai.py
│   ├── results/
│   │   └── resultados_previsao_goty.csv
│   ├── .gitignore
│   ├── README.md (this one that you are reading right now)
│   ├── requirements.txt

🚀 Usage & Installation

Follow the steps to configure and execute the project on your own computer.

Pre-requisites

Certify to have Python 3.8+ installed.

1. Clone the repo

git clone https://github.com/laosioor/Alzar-AI.git
cd Alzar-AI

2. Install dependencies

Install all the libraries required that are listed on requirements.txt:

pip install -r requirements.txt

3. Preparing data

Make sure Alzar_AI_Base_de_Dados.csv is located on data/.

4. Executing...

Run alzar_ai.py to train the models, evaluate their perfomance, select the best one, and save the generated artifacts (trained model, scaler, and prediction results CSV).

python scripts/alzar_ai.py

This script will print progress and classification reports to the console. Upon completion, melhor_modelo_goty.pkl and scaler_goty.pkl will be saved in the models/ folder, and resultados_previsao_goty.csv in the results/ folder.

📊 Analysis and Results

The alzar_ai.py executes the following steps:

• Data preps: Reads the CSV, handles null values, and prepares the columns.
• Model Comparison: Trains and evaluates Logistic Regression, Random Forest, and Gradient Boosting. For each model, the following are displayed:
  • Accuracy
  • Classification Report (Precision, Recall, F1-Score)
  • Confusion Matrix (visualization)
• Best Model Selection and Saving: The model with the highest accuracy on the test set is selected and saved as models/melhor_modelo_goty.pkl, along with models/scaler_goty.pkl.
• Prediction on the Entire Dataset: The saved model is loaded and used to predict the probability of winning for each game in the complete dataset. The results are displayed in the console and saved in results/resultados_previsao_goty.csv.

🤝 Contributions

Feel free to open issues or pull requests if you have suggestions, improvements, or encounter any problems.

Made by: Aloísio & Clarisse.