CGI Prediction Model – Dynamic Virtual Machine Forecasting

This project implements a system for predicting the number of virtual machines (VMs) to deploy based on the day of the week and the hour of the day.

It trains multiple regression models, evaluates their performance, and automatically selects the best model using a PROMETHEE-like multi‑criteria decision method.

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✨ Key Features

• Loads dataset containing: day ; hour ; nb_machine
• Trains multiple regression models:
  • Linear Regression
  • Ridge Regression
  • Lasso Regression
  • Random Forest
  • Support Vector Regressor (SVR)
  • Gradient Boosting Regressor
• Evaluates models using RMSE, MAE, R²
• Selects the best model through a PROMETHEE-like scoring method
• Saves the best model as best_model.joblib
• Generates predictions for 7×24 hours → out_prediction_one_week.csv

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📦 Installation

git clone https://github.com/CGI-FR/fogSLAAntillas.git
cd fogSLAAntillas
pip install -r requirements.txt

Requires:
pandas, scikit-learn, joblib

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📊 Input CSV Format

day	hour	nb_machine
0	13	5
0	14	7
...	...	...

Separator: ;

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🚀 Usage

python CGI_Prediction_Model.py data.csv

Outputs:

• best_model.joblib
• out_prediction_one_week.csv

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🧠 How It Works

1. Loads dataset from CSV
2. Splits training/testing sets
3. Trains all regression models
4. Computes RMSE, MAE, R²
5. Selects best model with PROMETHEE-like method
6. Saves best model
7. Generates one-week hourly predictions

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🧪 Manual Prediction Example

from joblib import load
import CGI_Prediction_Model as cgi

model = load("best_model.joblib")
prediction = cgi.predict_vms(2, 15, model)
print("Predicted VMs:", prediction[0])

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📈 Example output: out_prediction_one_week.csv

day	hour	nb_machine
0	0	4
0	1	4
...	...	...

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📄 License

BSD-3 License