Machine Learning Projects

A Python Implementation Of The Machine Learning Algorithms I am Learning over time.It consists of the general algorithms on regression,classification and regression.The repository is being updated continually updated as and when I learn new algorithms on ML and implement them.

Installation and Usage

You can download zip file or clone this repository using git clone with the URL shown in the code.

The codes can be run in any Python editor like Spyder or Pycharm.The associated datasets should also be downloaded to get the predicted results.

Contents

The Algorithms implemented are :

• Regression

  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Support Vector Regression
  • Decision Tree Regression
  • Random Forest Regression

• Classification

  • Logistic Regression
  • K-Nearest Neighbours
  • Naive Bayes Classification
  • Support Vector Machine
  • Decision Tree Classification
  • Random Forest Classification

• Clustering

  • K-Means Clustering
  • Hierarchial Clustering

• Dimensionality Reduction

  • Principal Component Analysis
  • Linear Discriminant Analysis

• Associate Rule Learning

  • Apriori

• XGBoost

• Reinforcement Learning

  • Upper Confidence Bound Algorithm
  • Thompson Sampling

• Recommender Systems

  • Item-based similarity Approach
  • Collaborative Filtering Approach
    • Memory-Based
    • Model-Based

• Natural Language Processing

  • Using Bag of Words Model
    • Yelp Review Classifier
    • Restuarant Review Classifier
  • Using Tfidf Transformation(Text Processing)

• Mini Projects

  • Fruit Classification problem
  • Algorithms like KNN and SVM on various datasets

Note :

• All algorithms are implemented on small datasets easily available online except the Recommender System which is applied on the popular MovieLens dataset
• Projects and implementations from various courses on Udemy by Jose Portilla and SuperDataScienceTeam are used

Any issues with the code/implementation can be raised and I would be happy to fix it.