Solving Multiple Classification use cases Using H2O

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