Expert System

Expert System is a platform that allows users to do preprocessing, build, train, and test Machine Learning models without any programming languages. Designed to make Machine Learning accessible to everyone, this project provides an interface to handle the entire Machine Learning workflow.

Key Features

• Data Upload: Easily upload datasets for processing and analysis, or choose from one of the sample datasets available.

• Preprocessing: Perform

  • Encoding
  • Scaling
  • Handling Missing Values

• Algorithm Selection: Choose from:

  Classification	KNN, Naive Bayes, Logistic Regression, SVM, Decision Tree, Random Forest
  Regression	Linear Regression, Lasso Regression, Ridge Regression, Decision Tree, Random Forest,
  Clustering	K-Means, Hierarchical Clustering

• Customization: Select features, target variables, and fine-tune hyperparameters.

• Model Building: Train models using selected configurations.

• Predictions: Generate predictions using the trained model.

• Evaluation Metrics:

  • Accuracy, Precision, Recall, F1 Score (for classification)
  • MAE, MSE, RMSE, R-Squared (for regression)
  • Inertia, Silhouette Score (for clustering)

• Visualization: Generate visualizations like heatmaps, scatters, cluster plots and dendrograms.

• Sample Code: View sample Python code for the training process.

• Model Download: Export trained models as .pkl files for reuse.

• Learn: Learn about Data Science and Machine Learning concepts using the notes available.

Expert System is especially aimed at students who want to explore Machine Learning concepts without the complexities of coding.

Screenshots

Workflow

flowchart LR
    A(Start) --> B(Preprocessing)
    B --> D(Modelling)
    D --> E[Select Algorithm]
    E --> F[Pick Features, Target, Hyperparameters]
    F --> G(Training)
    G --> H[Evaluation Metrics]
    H --> I[Prediction using the model]
    I --> J[View Code]
    J --> K[Download the Model]

    K --> Z(End)

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Technologies Used

Usage

1. Clone the project:

   git clone https://github.com/akshay-rajan/expertsystem.git
   

2. Navigate to the project directory:

   cd expertsystem
   

3. Create a virutal environment:

   python -m venv myenv

4. Activate the virtual environment:

   • Linux / MacOS

     source myenv/bin/activate

   • Windows

     .\myenv\Scripts\activate

5. Install the requirements:

   pip install -r requirements.txt

6. Run database migrations:

   python manage.py migrate

7. Start the Django server:

   python manage.py runserver

The application will be accessible at at http://127.0.0.1:8000/ .

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Akshay R, Deepu Joseph, Masters in Computer Applications, College of Enginnering, Trivandrum (2023-25)