Data Science Exploration: A Journey Through Diverse Techniques

This repository serves as a playground for exploring various data science techniques, encompassing:

• Regression:
  • Linear Regression: Building a linear relationship between independent and dependent variables for prediction tasks.
  • Polynomial Regression: Modeling non-linear relationships by adding polynomial terms to the equation.
• Clustering:
  • K-Means: Grouping data points into clusters based on their similarities, defined by K pre-determined centroids.
  • Gaussian Mixture Models (GMM): Modeling data distribution using a mixture of Gaussian components for more nuanced cluster representation.
• CNN:
  • Convolutional Neural Networks: Extracting features and patterns from visual data for object recognition and image classification.
• ResNet:
  • Deep Residual Neural Networks: Addressing the vanishing gradient problem in deep learning for improved image classification performance.
• Decision Tree:
  • A tree-like structure for classification, making predictions based on conditional rules derived from data attributes.
• Random Forest:
  • Combines multiple decision trees to improve accuracy and reduce overfitting, offering robust classification capabilities.
• Disease Prediction:
  • Implementing five different methods to predict the onset or risk of various diseases based on patient data.

Repository Highlights:

• Implementation of various data science algorithms in Python using libraries like scikit-learn and TensorFlow.
• Exploration of real-world datasets across different domains.
• Visualization of results and insights generated by each algorithm.
• Performance comparison and analysis of different techniques for specific tasks.
• Documentation and explanation of each method to enhance understanding.

Getting Started:

1. Clone this repository.
2. Install the required dependencies according to the instructions provided.
3. Run the provided Jupyter notebooks or Python scripts to explore specific algorithms and datasets.

Further Resources:

• Scikit-learn: https://scikit-learn.org/
• TensorFlow: https://www.tensorflow.org/
• Machine Learning Crash Course: https://developers.google.com/machine-learning/crash-course