/Machine-Leaning-Algorithms-Dashboard

Primary LanguageJavaScript

Machine Learning Dashboard Website

Introduction

Welcome to the Machine Learning Dashboard website! This platform is a comprehensive resource for understanding and visualizing machine learning algorithms. Each algorithm is presented with a detailed explanation, interactive visualizations, and practical examples.

ML Dashboard Logo

Features

  • Algorithms Covered: A wide range of machine learning algorithms like KNN, Logistic Regression, Decision Trees, Random Forest, Neural Networks, and more.
  • Interactive Visualizations: Utilize Plotly and other visualization libraries for interactive graphs.
  • Practical Examples: Detailed practical code examples for each algorithm.
  • Advantages & Disadvantages: Learn the pros and cons of each algorithm.
  • Use Cases: Discover real-world applications.
  • Implementation Guides: Step-by-step guides to implement each algorithm.
  • Performance Metrics: Evaluate each algorithm using various metrics.

Project Structure

.
├── algorithms
│   ├── templates
│   │   └── algorithms
│   │       ├── cnn.html
│   │       ├── decision-tree.html
│   │       ├── knn.html
│   │       ├── logistic-regression.html
│   │       ├── lstm.html
│   │       ├── naive-bayes.html
│   │       ├── neural-network.html
│   │       ├── random-forest.html
│   │       ├── recurrent-neural-network.html
│   │       ├── simple-linear-regression.html
│   │       └── xgboost.html
│   └── views.py
├── static
│   ├── css
│   ├── images
│   └── js
├── templates
│   ├── partials
│   └── base.html
├── urls.py
└── README.md

Screenshots

Algorithms Algorithms

Algorithms with filter applied Algorithms

Decision Tree Classifier Decision Tree Classifier

Multiple Linear Regression Multiple Linear Regression

Long Short-Term Memory (LSTM) Long Short-Term Memory (LSTM)

Overfitting and Underfitting Overfitting and Underfitting

Logistic Regression Logistic Regression

Algorithms Covered

1. Simple Linear Regression

Description: Simple Linear Regression is a statistical method used to understand the relationship between one independent variable and one dependent variable.

Key Characteristics:

  • Equation: y = mx + c
  • Applications: Predictive analysis, trend estimation, stock market analysis, sales forecasting.

2. K-Nearest Neighbors (KNN)

Description: K-Nearest Neighbors is a simple, non-parametric algorithm used for classification and regression.

Key Characteristics:

  • Parameter: k (number of neighbors)
  • Applications: Image classification, fraud detection, recommender systems.

3. Decision Tree Classifier

Description: A decision tree classifier is a predictive model that maps features to target labels using a tree-like structure.

Key Characteristics:

  • Parameter: max_depth, criterion
  • Applications: Credit scoring, disease diagnosis, customer segmentation.

4. Convolutional Neural Network (CNN)

Description: CNNs are a type of deep learning model specifically designed for image processing and classification tasks.

Key Characteristics:

  • Layers: Convolutional, pooling, fully connected.
  • Applications: Image classification, object detection, face recognition.

5. Long Short-Term Memory (LSTM)

Description: LSTM networks are a type of recurrent neural network capable of learning long-term dependencies in sequential data.

Key Characteristics:

  • Layers: LSTM, dropout, fully connected.
  • Applications: Stock price prediction, text generation, speech recognition.

6. XGBoost

Description: XGBoost is an optimized gradient boosting library designed to be efficient, flexible, and portable.

Key Characteristics:

  • Parameters: n_estimators, max_depth, learning_rate
  • Applications: Kaggle competitions, anomaly detection, predictive maintenance.

Getting Started

Prerequisites

  • Python 3.x
  • Django
  • TensorFlow
  • Plotly

Installation

  1. Clone the Repository:

    git clone https://github.com/Alibakhshov/ML-Algorithm-Dashboard.git
cd MLdashboard

  2. Create and Activate Virtual Environment:

    python3 -m venv venv
source venv/bin/activate

  3. Install Dependencies:

    pip install -r requirements.txt

  4. Run the Server:

    python manage.py runserver

Usage

  1. Open your browser and navigate to http://127.0.0.1:8000/
  2. Explore the algorithms, interactive visualizations, and practical guides.