Sure! Below is the generated README file for your project:
---
# Machine Learning Project
This repository contains a machine learning project that includes data preprocessing, model building, training, evaluation, and visualization. The project structure and scripts are organized to ensure a streamlined workflow.
## Project Structure
The project is organized into the following directories:
- `data/`: Directory to store the dataset.
- `models/`: Directory to save trained models.
- `scripts/`: Directory for additional scripts.
- `notebooks/`: Directory for Jupyter notebooks.
## Prerequisites
Make sure you have the following packages installed:
- `pandas`
- `numpy`
- `tensorflow`
- `scikit-learn`
- `matplotlib`
- `seaborn`
- `pyyaml`
- `pytest`
You can install these packages using pip:
```bash
pip install pandas numpy tensorflow scikit-learn matplotlib seaborn pyyaml pytest
```
## Configuration
The project uses a configuration file (`config.yaml`) to specify parameters for data preprocessing, model architecture, training, and evaluation.
### Sample Configuration (`config.yaml`)
```yaml
data:
filepath: 'data/your_dataset.csv'
target_column: 'target_column_name'
test_size: 0.2
model:
layers:
- type: 'Dense'
units: 64
activation: 'relu'
- type: 'Dense'
units: 32
activation: 'relu'
optimizer: 'adam'
loss: 'mse'
metrics: ['mae']
save_path: 'models/saved_model.h5'
train:
epochs: 50
batch_size: 32
```
## Usage
### Main Script
The main script (`main.py`) performs the entire workflow:
1. Loading the configuration.
2. Loading and preprocessing the data.
3. Building the model.
4. Training the model.
5. Evaluating the model.
6. Visualizing metrics.
7. Saving the trained model.
To run the main script, execute:
```bash
python main.py config.yaml
```
### Unit Tests
Unit tests are provided to validate different components of the project. To run the tests, execute:
```bash
pytest
```
### Functions
- **load_config(config_path: str) -> dict**:
Loads the configuration from a YAML file.
- **load_data(filepath: str) -> pd.DataFrame**:
Loads data from a CSV file and drops empty rows.
- **preprocess_data(df: pd.DataFrame, config: dict) -> tuple**:
Preprocesses the data and splits it into training and testing datasets.
- **build_model(config: dict) -> tf.keras.Model**:
Builds a TensorFlow model based on the configuration.
- **train_model(model: tf.keras.Model, train_data: tuple, val_data: tuple, config: dict) -> tf.keras.Model**:
Trains the model using the specified training data, validation data, and configuration.
- **save_model(model: tf.keras.Model, model_path: str) -> None**:
Saves the trained model to a specified path.
- **load_model(model_path: str) -> tf.keras.Model**:
Loads a model from a specified path.
- **evaluate_model(model: tf.keras.Model, test_data: tuple) -> dict**:
Evaluates the model and returns metrics.
- **plot_metrics(history: dict) -> None**:
Plots the training and validation metrics.
### Additional Resources
For more information on the efficiency and reliability of machine learning systems, visit our [comprehensive review](https://forexroboteasy.com/forex-robot-review/comprehensive-review-of-ssx-titan-tt-trading-system-efficiency-and-reliability/).
## License
This project is licensed under the MIT License.
---
Feel free to open issues or submit pull requests. For any further questions, refer to the [comprehensive review](https://forexroboteasy.com/forex-robot-review/comprehensive-review-of-ssx-titan-tt-trading-system-efficiency-and-reliability/).
---
This README should give you a good overview of the project and help you get started with using and contributing to it.
---
# Machine Learning Project
This repository contains a machine learning project that includes data preprocessing, model building, training, evaluation, and visualization. The project structure and scripts are organized to ensure a streamlined workflow.
## Project Structure
The project is organized into the following directories:
- `data/`: Directory to store the dataset.
- `models/`: Directory to save trained models.
- `scripts/`: Directory for additional scripts.
- `notebooks/`: Directory for Jupyter notebooks.
## Prerequisites
Make sure you have the following packages installed:
- `pandas`
- `numpy`
- `tensorflow`
- `scikit-learn`
- `matplotlib`
- `seaborn`
- `pyyaml`
- `pytest`
You can install these packages using pip:
```bash
pip install pandas numpy tensorflow scikit-learn matplotlib seaborn pyyaml pytest
```
## Configuration
The project uses a configuration file (`config.yaml`) to specify parameters for data preprocessing, model architecture, training, and evaluation.
### Sample Configuration (`config.yaml`)
```yaml
data:
filepath: 'data/your_dataset.csv'
target_column: 'target_column_name'
test_size: 0.2
model:
layers:
- type: 'Dense'
units: 64
activation: 'relu'
- type: 'Dense'
units: 32
activation: 'relu'
optimizer: 'adam'
loss: 'mse'
metrics: ['mae']
save_path: 'models/saved_model.h5'
train:
epochs: 50
batch_size: 32
```
## Usage
### Main Script
The main script (`main.py`) performs the entire workflow:
1. Loading the configuration.
2. Loading and preprocessing the data.
3. Building the model.
4. Training the model.
5. Evaluating the model.
6. Visualizing metrics.
7. Saving the trained model.
To run the main script, execute:
```bash
python main.py config.yaml
```
### Unit Tests
Unit tests are provided to validate different components of the project. To run the tests, execute:
```bash
pytest
```
### Functions
- **load_config(config_path: str) -> dict**:
Loads the configuration from a YAML file.
- **load_data(filepath: str) -> pd.DataFrame**:
Loads data from a CSV file and drops empty rows.
- **preprocess_data(df: pd.DataFrame, config: dict) -> tuple**:
Preprocesses the data and splits it into training and testing datasets.
- **build_model(config: dict) -> tf.keras.Model**:
Builds a TensorFlow model based on the configuration.
- **train_model(model: tf.keras.Model, train_data: tuple, val_data: tuple, config: dict) -> tf.keras.Model**:
Trains the model using the specified training data, validation data, and configuration.
- **save_model(model: tf.keras.Model, model_path: str) -> None**:
Saves the trained model to a specified path.
- **load_model(model_path: str) -> tf.keras.Model**:
Loads a model from a specified path.
- **evaluate_model(model: tf.keras.Model, test_data: tuple) -> dict**:
Evaluates the model and returns metrics.
- **plot_metrics(history: dict) -> None**:
Plots the training and validation metrics.
### Additional Resources
For more information on the efficiency and reliability of machine learning systems, visit our [comprehensive review](https://forexroboteasy.com/forex-robot-review/comprehensive-review-of-ssx-titan-tt-trading-system-efficiency-and-reliability/).
## License
This project is licensed under the MIT License.
---
Feel free to open issues or submit pull requests. For any further questions, refer to the [comprehensive review](https://forexroboteasy.com/forex-robot-review/comprehensive-review-of-ssx-titan-tt-trading-system-efficiency-and-reliability/).
---
This README should give you a good overview of the project and help you get started with using and contributing to it.