https://github.com/fawadeqbal/data-science

A comprehensive repository covering essential Data Science concepts using PyTorch, including anomaly detection, classification, clustering, regression, and more. Includes hands-on implementations and tutorials for each concept.
https://github.com/fawadeqbal/data-science

calculus calculus-2 data-science data-visualization dataset machine-learning python pytorch statistics

Last synced: about 1 month ago
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A comprehensive repository covering essential Data Science concepts using PyTorch, including anomaly detection, classification, clustering, regression, and more. Includes hands-on implementations and tutorials for each concept.

• Host: GitHub
• URL: https://github.com/fawadeqbal/data-science
• Owner: fawadeqbal
• Created: 2024-11-13T16:37:22.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2024-12-01T06:16:25.000Z (about 1 month ago)
• Last Synced: 2024-12-01T07:22:21.551Z (about 1 month ago)
• Topics: calculus, calculus-2, data-science, data-visualization, dataset, machine-learning, python, pytorch, statistics
• Language: Python
• Homepage:
• Size: 17.6 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# DataScience

Welcome to **DataScience Concepts**, a comprehensive repository that demonstrates various data science concepts and machine learning techniques using **PyTorch**. This repository provides hands-on implementations and practical tutorials on **classification**, **clustering**, **anomaly detection**, **regression**, and other fundamental topics.

## Key Concepts Covered:

### 1. **Classification Algorithms**

   - Logistic Regression

   - Decision Trees (using PyTorch-based custom models)

   - Multi-Layer Perceptron (MLP)

   - Convolutional Neural Networks (CNN)

### 2. **Clustering Algorithms**

   - k-Means Clustering (Implemented with PyTorch)

   - Custom Clustering Models

### 3. **Regression Models**

   - Linear Regression

   - Polynomial Regression

   - Neural Networks for Regression

### 4. **Anomaly Detection**

   - Autoencoders for Anomaly Detection

   - Isolation Forest (using PyTorch-based models)

### 5. **Deep Learning Models**

   - Neural Networks (Feedforward, CNN, RNN, LSTM)

   - Transfer Learning: Leveraging pre-trained models like ResNet for custom tasks.

   - Transformers: Applied to sequence-to-sequence tasks such as text generation and translation.

   - 

### 6. **Model Evaluation and Tuning**

   - Cross-Validation

   - Hyperparameter Tuning using Grid Search

   - Model Evaluation Metrics (Accuracy, Precision, Recall, F1-Score)

### 7. **Time Series Analysis with LSTMs**

   - LSTM for Sequential Data Forecasting

   - Time Series Forecasting using PyTorch-based RNN/LSTM models

## Technologies Used

- Python

- **PyTorch**

- NumPy

- Pandas

- Matplotlib/Seaborn (for visualization)

## Installation

Clone this repository to your local machine:

```bash

git clone https://github.com/fawadeqbal/DataScience.git

cd Data-Science