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Predict Census Income using Deep Learning Models

In this project, we are going to work on Deep Learning using H2O to predict Census income.

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What will you learn

  • Apply DL models
  • Use of H2O
  • Compare the DL models
  • Grid Search on DL models
  • Tuning the accuracy in DL models

What will you get

  • Access to recording of the complete project
  • Access to all material related to project like data files, solution files etc.

Prerequisites

  • Knowledge of Data Science and R.
  • Language Used: R

Project Description

This data was extracted from the census bureau database found at:
http://www.census.gov/ftp/pub/DES/www/welcome.html
Donor: Ronny Kohavi and Barry Becker,
            Data Mining and Visualization
            Silicon Graphics.
            e-mail: ronnyk@sgi.com for questions.
Split into train-test using MLC++ GenCVFiles (2/3, 1/3 random).
48842 instances, mix of continuous and discrete    (train=32561, test=16281)
45222 if instances with unknown values are removed (train=30162, test=15060)
Duplicate or conflicting instances : 6
Class probabilities for adult.all file
Probability for the label '>50K'  : 23.93% / 24.78% (without unknowns)
Probability for the label '<=50K' : 76.07% / 75.22% (without unknowns)

Extraction was done by Barry Becker from the 1994 Census database.  
A set of reasonably clean records was extracted using the following conditions:
   ((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0))
Prediction task is to determine whether a person makes over 50K a year.

Instructors

 
Pradeepta

Curriculum For This Mini Project

 
  Problem Statement
00:04:02
  Import Data Sets
00:08:48
  What is Deep Learning?
00:35:37
  Understanding H2O
00:05:56
  Data Sanity Check
00:13:55
  Remove leading white space
00:03:01
  Impute Missing values
00:00:45
  Initializing H2O
00:02:06
  Train H2O model without hidden layer (C )
00:04:39
  Hyperparameter optimization
00:16:46
  Random Grid Search
00:10:57