Machine Learning Pipeline Project

🚀 Project Setup

Prerequisites

• Python 3.8+
• pip (Python package manager)
• Git

1. Clone Repository

# Clone the project
git clone https://github.com/semyonsw/Diabetes_Dataset
cd Diabetes_Dataset

2. Virtual Environment Setup

# Create virtual environment
python3 -m venv venv

# Activate virtual environment
# On macOS/Linux
source venv/bin/activate
# On Windows
venv\Scripts\activate

3. Install Dependencies

# Install required packages
pip install -r requirements.txt

🔧 Project Structure

├── Data_Analyse                      # Data analyse of 
├── src_models_prcsd_data/            # Folder in which we have saved processed datasets and source files
|         ├── train_processed.csv     # Processed train dataset
|         ├── test_processed.csv      # Processed test dataset
│         ├── __init__.py             # ML core logic
│         └── pipeline.py             # GUI implementation
├── .gitignore                        # Contains files to ignore
├── data/                             # Dataset storage (test/train and the raw data(csv))
├── requirements.txt                  # Required python libraries
└── README.md                         # Tutorial file (follow along)

💻 Application Workflow

Data Preparation

1. Prepare CSV dataset with features and target column
2. Ensure data is clean and preprocessed

Running the GUI

# Launch ML Pipeline GUI
python src/pipeline.py

GUI Navigation

1. Data Loading

   • Click "Browse"
   • Select input CSV file for training model
   • Choose target column
   • Select relevant features
   • Do preprocessing of the dataset by hitting "Process Train Data" button

2. Model Configuration

   • Select model type:
     • Gradient Boosting
     • Decision Tree
     • Random Forest
   • Adjust model hyperparameters
   • Click "Train Model"
   • See the result of your selected model (Accuracy)

3. Model Evaluation

   • Load test dataset
   • Process the dataset by clicking the corresponding button
   • Click "Test Model"
   • See the test results (Accuracy)

Custom Prediction

• Input individual data points
• Click "Predict Custom Data Point" for single sample classification
• See whether it has Diabetes(Predicts Target column) or hasn't

🛠 Troubleshooting

• Verify Python version: python --version
• Check pip installation: pip --version
• Reinstall dependencies if errors occur: pip install -r requirements.txt

📋 Requirements

• pandas
• scikit-learn
• tkinter
• joblib

🆘 Support

Open GitHub issues for bugs or feature requests.