Data Science Mini Projects 🧠📊

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Table of Contents

• Overview
• Directory Structure
• Automated Setup Script
• Usage
• Git Initialization & Push
• Contributing
• License
• Contact

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Overview

This repository hosts a collection of Supervised and Unsupervised Learning mini-projects, each organized into:

• Concepts (concepts.md) – Core explanations
• Intricacies (intricacies.md) – Deep-dive technical details
• Implementation (<Concept>.py) – Python code

Use this as an educational reference or a starting point for your own data-science workflows.

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Directory Structure

Below is the fully expanded tree under Datascience/. Every method has its own subfolder with three files:
concepts.md, intricacies.md, and <MethodName>.py.

Datascience/
├── SupervisedLearning/
│   ├── Regression/
│   │   ├── LinearRegression/
│   │   │   ├── concepts.md
│   │   │   ├── intricacies.md
│   │   │   └── LinearRegression.py
│   │   ├── LogisticRegression/
│   │   │   ├── concepts.md
│   │   │   ├── intricacies.md
│   │   │   └── LogisticRegression.py
│   │   ├── RidgeRegression/
│   │   │   ├── concepts.md
│   │   │   ├── intricacies.md
│   │   │   └── RidgeRegression.py
│   │   ├── LassoRegression/
│   │   │   ├── concepts.md
│   │   │   ├── intricacies.md
│   │   │   └── LassoRegression.py
│   │   └── PolynomialRegression/
│   │       ├── concepts.md
│   │       ├── intricacies.md
│   │       └── PolynomialRegression.py
│   ├── Classification/
│   │   ├── DecisionTrees/
│   │   │   ├── concepts.md
│   │   │   ├── intricacies.md
│   │   │   └── DecisionTrees.py
│   │   ├── RandomForest/
│   │   │   ├── concepts.md
│   │   │   ├── intricacies.md
│   │   │   └── RandomForest.py
│   │   ├── SupportVectorMachines/
│   │   │   ├── concepts.md
│   │   │   ├── intricacies.md
│   │   │   └── SupportVectorMachines.py
│   │   └── NaiveBayes/
│   │       ├── concepts.md
│   │       ├── intricacies.md
│   │       └── NaiveBayes.py
│   └── NeuralNetworks/
│       ├── Perceptron/
│       │   ├── concepts.md
│       │   ├── intricacies.md
│       │   └── Perceptron.py
│       ├── ConvolutionalNeuralNetworks/
│       │   ├── concepts.md
│       │   ├── intricacies.md
│       │   └── ConvolutionalNeuralNetworks.py
│       └── RecurrentNeuralNetworks/
│           ├── concepts.md
│           ├── intricacies.md
│           └── RecurrentNeuralNetworks.py
├── UnsupervisedLearning/
│   ├── Clustering/
│   │   ├── KMeans/
│   │   │   ├── concepts.md
│   │   │   ├── intricacies.md
│   │   │   └── KMeans.py
│   │   ├── HierarchicalClustering/
│   │   │   ├── concepts.md
│   │   │   ├── intricacies.md
│   │   │   └── HierarchicalClustering.py
│   │   └── DBSCAN/
│   │       ├── concepts.md
│   │       ├── intricacies.md
│   │       └── DBSCAN.py
│   ├── DimensionalityReduction/
│   │   ├── PrincipalComponentAnalysis/
│   │   │   ├── concepts.md
│   │   │   ├── intricacies.md
│   │   │   └── PrincipalComponentAnalysis.py
│   │   └── SingularValueDecomposition/
│   │       ├── concepts.md
│   │       ├── intricacies.md
│   │       └── SingularValueDecomposition.py
│   ├── AnomalyDetection/
│   │   ├── IsolationForest/
│   │   │   ├── concepts.md
│   │   │   ├── intricacies.md
│   │   │   └── IsolationForest.py
│   │   └── LocalOutlierFactor/
│   │       ├── concepts.md
│   │       ├── intricacies.md
│   │       └── LocalOutlierFactor.py
│   └── AssociationRules/
│       ├── Apriori/
│       │   ├── concepts.md
│       │   ├── intricacies.md
│       │   └── Apriori.py
│       └── Eclat/
│           ├── concepts.md
│           ├── intricacies.md
│           └── Eclat.py
└── struct.sh