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.


Each project comes with 2-5 hours of micro-videos explaining the solution.

Code & Dataset

Get access to 50+ solved projects with iPython notebooks and datasets.

Project Experience

Add project experience to your Linkedin/Github profiles.

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.

Similar Projects

Big Data Project Build a Customer Churn Prediction Model for Insurance Domain
Machine Learning Project in R -Predict which customers will leave an insurance company in the next 12 months.
Big Data Project Data Science Project-Movie Review Sentiment Analysis using R
Learn to classify the sentiment of sentences from the Rotten Tomatoes dataset. You will be asked to label phrases on a scale of five values: negative, somewhat negative, neutral, somewhat positive, positive.
Big Data Project Predict Wine Preferences of Customers using Wine Dataset
In this machine learning project, you will build predictive models to identify wine preferences of people using physiochemical properties of wines and help restaurants recommend the right quality of wine to a customer.
Big Data Project Predict Macro Economic Trends using Kaggle Financial Dataset
In this machine learning project, you will uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.

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