Certainly, here's a draft of a README file for the provided code:
# Machine Learning Assignment Scaffolding
This repository contains scaffolding code for a Machine Learning assignment. The code provides a foundation for building and evaluating various machine learning models for classification tasks. You are encouraged to complete the provided functions and add any additional functions or classes as needed to complete the assignment.
## Team Members
- Student 1: Law HoFong (Student ID: 10107321)
- Student 2: Kiki Mutiara (Student ID: 10031017)
## Prerequisites
Before running the code, make sure you have the following libraries installed:
- scikit-learn (for machine learning models)
- NumPy (for numerical operations)
- TensorFlow and Keras (for deep learning models)
- pandas (optional, for data manipulation)
- matplotlib (for data visualization)
You can install these libraries using pip:
pip install scikit-learn numpy tensorflow pandas matplotlib
## Usage
1. **Prepare the Dataset**: The `prepare_dataset` function reads a comma-separated text file containing data for classification. It preprocesses the data, standardizes it, and encodes class labels. You should provide the path to your dataset file in the `dataset_path` variable within the function.
2. **Build and Evaluate Classifiers**:
- The `build_DecisionTree_classifier`, `build_NearrestNeighbours_classifier`, `build_SupportVectorMachine_classifier`, and `build_NeuralNetwork_classifier` functions build and train classifiers using different machine learning algorithms. These functions perform hyperparameter tuning using Grid Search with cross-validation to find the best parameters for each algorithm.
- You can customize the hyperparameter search space within these functions if needed.
- The accuracy and confusion matrices for each classifier are displayed in the console.
3. **Plot Confusion Matrices**: The code includes code to plot confusion matrices for each classifier using `matplotlib`. This helps visualize the performance of each classifier on the test data.
4. **Note on Deep Learning Classifier**: The `build_NeuralNetwork_classifier` function creates a neural network classifier with two dense hidden layers. You can adjust the architecture by modifying the `hidden_layer_sizes` hyperparameter.
5. **Encoding Labels**: The `encode` function encodes class labels (e.g., 'B' and 'M') into numeric values suitable for deep learning models.
## Running the Code
To run the code, execute the script in a Python environment:
```bash
python your_script_name.py
This code is provided under the MIT License.
Feel free to modify and extend the code to complete your Machine Learning assignment. Good luck!
Please replace `your_script_name.py` with the actual name of your script if it's different. This README provides a basic structure, and you can further enhance it with additional information about the assignment, dataset, and specific tasks as needed.