ML_Practice

This repository contains implementations of various machine learning models applied to different real-world datasets. I created this repository for practice, which helped me hone my machine learning skills.

Repository Structure

ML_Practice/
├── 1.Data Preprocessing/
│   ├── 1.Importing Datasets
│   ├── 2.Dealing with null values
│   ├── 3.Data Formatting & Data Binning
│   └── 4.Dealing With Categorical Values
├── 2.Regression/
│   ├── 1.Simple Linear Regression
│   ├── 2.Multiple Linear Regression
│   ├── 3.Polynomial Regression
│   ├── 4.Support Vector Regression (SVR)
│   ├── 5.Decision Tree Regression
│   └── 6.Random Forest Regression  
├── 3.Classification/
│   ├── 1.Logistic Regression
│   ├── 2.K-Nearest Neighbors (K-NN)
│   ├── 3.Support Vector Machine (SVM)
│   ├── 4.Kernel SVM
│   ├── 5.Naive Byes
│   ├── 6.Decision Tree Classification
│   └── 7.Random Forest Classification
├── 4.Clustering/
│   ├── 1.K-Means Clustering
│   └── 2.Heirarchical Clustering
├── 5.Association Rule Learning/
│   ├── 1.Apriori
│   └── 2.Eclat
├── 6.Reinforcement Learning/
│   ├── 1.Upper Confidence Bound (UCB)
│   └── 2.Thompson Sampling
├── 7.Natural Language Processing  
├── 8.Deep Learning/
│   ├── 1.Artificial Neural Network (ANN)
│   └── 2.Convolutional Neural Network (CNN) 
├── 9.Dimensionality Reduction/
│   ├── 1.Principal Component Analysis (PCA)
│   ├── 2.Linear Discriminant Analysis (LDA)
│   └── 3.Kernel PCA
├── 10.Model Selection and Boosting/
│   ├── 1.Model Selection
│   ├── 2.XGBoost
│   └── 3.CatBoost
└── Model Selection/
    ├── Classification
    └── Regression