Solving Multiple Classification use cases Using H2O

In this project, we are going to talk about H2O and functionality in terms of building Machine Learning models.


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What will you learn

  • What is H2O and why is it used

  • Initializing an H2O cluster for using all cores of CPU and RAM

  • Importing the dataset from amazon AWS

  • Performing basic EDA on the dataset

  • Visualizing the dataset using Quantiles and Histograms

  • Constructing test and train sets using sampling

  • Defining the data for the model and displaying the results

  • Applying GLM model for making predictions

  • Defining mean_squared_error for evaluation metrics

  • View model information: training statistics, performance, important variables

  • Defining the parameters for a deep leaning Neural Networks

  • The first part of the data, without labels for unsupervised learning

  • The second part of the data, with labels for supervised learning

  • Converting train dataset with autoencoder model to lower-dimensional space

  • Training the DRF on reduced feature space

  • Making the final predictions

Project Description is focused on bringing AI to businesses through software.​ ​

H2O includes many common Machine Learning algorithms, such as generalized linear modeling (linear regression, logistic regression, etc.), Naive Bayes, principal components analysis, k-means clustering, and word2vec. H2O implements best-in-class algorithms at scale, such as distributed random forest, gradient boosting, and deep learning. H2O also includes a Stacked Ensembles method, which finds the optimal combination of a collection of prediction algorithms using a process known as stacking.

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Curriculum For This Mini Project

  Install and Initialize H2O Library
  Introduction to H2O
  Create Histogram
  H2O Operations
  Data Preparation
  Machine Learning Models using H2O
  Deep Learning using H2O - Basic Model
  Deep Learning using H2O - Adding Epochs
  Deep Learning using H2O - Stopping Criteria
  Deep Learning using H2O - Hidden Layer
  Compare Performance of Models
  Cross Validation
  Grid Search
  Supervised VS Unsupervised
  AutoEncoder Model for feature reduction