ML-projects

• Notebooks of ml projects created during taking a course
• All data is also there in the files in csv or tsv formats
• My kaggle page: https://www.kaggle.com/prathamshahmldlds

Notebooks:

• Regression

  • Multiple linear R
  • SVM, Kernel SVM
  • Polynomial R
  • Support vector R
  • Descision tree R
  • Random forest R
  • Artificial nn R(Tensorflow)

• Classification

  • Logistic regression
  • Support vector C
  • Random forest C
  • Descision tree
  • Naive Bayes
  • Artificial nn C(Tensorflow)

• Clustering

  • K-means C
  • Hierarchical C

• Assotiation Rule learning

  • Apriori

• Natural Language processing

  • Bag-O-words model

• Reinforcement learning

  • Upperconfidence bound
  • Thompson sampling

• Additional

  • Data preprocessing toolkit
  • Comparing all Regression models
  • Comparing all classification models
  • Kfold and Grid search to evaluate models

  Projects

  • Graph Neural Network based collaborative filtering recommender system
  • LSTM from scratch using pytorch-lightning
  • Word Vectors in pytorch
  • Data augmentation using albumentations for transfer learning
  • Time series
    • Time series decomposition from scratch
    • Simulating and forecasting a random walk
    • Using a MA model for stock prediction
    • Using an auto-regressive model

Pytorch

• Deep neural network implementations in pytorch for regression and classification