Data Science Portfolio

Repository containing portfolio of data science projects completed by me for academic, self learning, and hobby purposes. Presented in the Organised Structures.

Contents

• Regression

  • Regression models (both linear and non-linear) are used for predicting a real value, like salary for example. If your independent variable is time, then you are forecasting future values, otherwise your model is predicting present but unknown values. Regression technique vary from Linear Regression to SVR and Random Forests Regression.
  • In this i have implemented the following Machine Learning Regression models:
    • Simple Linear Regression
    • Multiple Linear Regression
    • Polynomial Regression
    • Support Vector for Regression
    • Decision Tree Classification
    • Random Forest Classification

• Classification

  • Unlike regression where you predict a continuous number, you use classification to predict a category. There is a wide variety of classification applications from medicine to marketing. Classification models include linear models like Logistic Regression, SVM, and nonlinear ones like K-NN, Kernel SVM and Random Forests.
  • In this i have implemented the following Machine Learning Regression models:
    • Logistic Regression
    • K-Nearest Neighbors (K-NN)
    • Support Vector Machine (SVM)
    • Kernel SVM
    • Naive Bayes
    • Decision Tree Classification
    • Random Forest Classification

• Clustering

  • Clustering is similar to classification, but the basis is different. In Clustering we don’t know what we are looking for, and we are trying to identify some segments or clusters in our data. When we use clustering algorithms on our dataset, unexpected things can suddenly pop up like structures, clusters and groupings we would have never thought of otherwise.
  • In this i have implemented the following Machine Learning Clustering models:
    • K-Means Clustering
    • Hierarchical Clustering

• Association Rule Learning

  • Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases.
  • In this i have implemented the following Machine Learning Association Rule Learning models:
    • Apriori

• Natural Language Processing

  • Natural Language Processing (or NLP) is applying Machine Learning models to text and language. Teaching machines to understand what is said in spoken and written word is the focus of Natural Language Processing. Whenever you dictate something into your iPhone / Android device that is then converted to text, that’s an NLP algorithm in action.
  • In this i have implemented the following Machine Learning NLP models:
    • Create a Bag of Words model

• Deep Learning

  • Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.

  • In this i have implemented the following Supervised Deep Learning models:

    • Artificial Neural Networks
    • Convolutional Neural Networks
    • Recurrent Neural Networks

  • In this i have implemented the following Unsupervised Deep Learning models:

    • Boltzmann Machines
    • Self Organizing Maps
    • AutoEncoders