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KawgKawgKawg/
FXTimeSeries-models

Name: FXTimeSeries-models
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💱 FXTimeSeries - Exchange Rate Forecasting

This project provides a complete pipeline for forecasting foreign exchange (FX) rates using machine learning. It includes data preprocessing, feature engineering, model training with hyperparameter tuning, and deployment via a Streamlit web application.

📦 Features

✅ Full ML pipeline: ETL, feature engineering, model training, evaluation
🧠 Trained models: Random Forest, XGBoost, SVR
📈 Time series techniques: lag features, rolling stats, date-based features
🧪 Model selection using GridSearchCV
📊 Evaluation metrics: RMSE, MAE, R²
🚀 Streamlit app for predictions on new data
💾 Models available as split ZIP archive

📁 Repository Structure

FXTimeSeries/ ├── app.py # Streamlit application ├── exchange_rate_pipeline.py # Complete training pipeline ├── models_split.zip # Main model zip file (part of archive) ├── models_split.z01-z07 # Split model zip parts ├── requirements.txt # Python dependencies ├── README.md # This file └── modelcard.json # Hugging Face model card

🧠 Models Trained

Model	Description
Random Forest Regressor	Ensemble model using decision trees
XGBoost Regressor	Gradient boosting algorithm
Support Vector Regressor	Kernel-based regression model

Each model was optimized using GridSearchCV over parameters like n_estimators, max_depth, C, kernel, and learning_rate.

🧪 Evaluation Metrics

Evaluation was done on a test set using:

RMSE (Root Mean Squared Error)
MAE (Mean Absolute Error)
R² Score

The best models from grid search were saved and exported.

🚀 Running the App

1. Install Dependencies

bash

pip install -r requirements.txt
2. Start the Streamlit App
bash
Copy
Edit
streamlit run app.py
3. Upload CSV File
Upload a preprocessed CSV that contains the same features used during training.

📥 Downloading the Models
The model files are provided as a multi-part split archive:


models_split.zip
models_split.z01
models_split.z02
...
models_split.z07
To extract:

On Linux/macOS:
bash
Copy
Edit
cat models_split.z* models_split.zip > combined_models.zip
unzip combined_models.zip
On Windows:
Use 7-Zip or WinRAR to extract starting from models_split.zip.

📜 License
This project is licensed under the MIT License.

🙋‍♂️ Author
Voltsy
GitHub: @heubert-69


## 📘 `modelcard.json`

```json
{
  "model_name": "FXTimeSeries",
  "license": "mit",
  "language": "en",
  "tags": ["time-series", "regression", "exchange-rate", "finance", "streamlit"],
  "datasets": ["custom"],
  "metrics": ["rmse", "mae", "r2"],
  "model-index": [
    {
      "name": "FXTimeSeries",
      "results": [
        {
          "task": {
            "name": "Time Series Forecasting",
            "type": "time-series-forecasting"
          },
          "metrics": [
            {
              "name": "RMSE",
              "type": "rmse",
              "value": "See README"
            },
            {
              "name": "MAE",
              "type": "mae",
              "value": "See README"
            },
            {
              "name": "R²",
              "type": "r2",
              "value": "See README"
            }
          ]
        }
      ]
    }
  ],
  "widget": {
    "type": "table",
    "inputs": "CSV file with the same feature columns used in training"
  },
  "pipeline_tag": "time-series-forecasting",
  "library_name": "sklearn"
}

KawgKawgKawg/FXTimeSeries-models