Predict Census Income using Deep Learning Models

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

Understanding the problem statement
Importing the dataset from AWS
Importing important libraries and understanding its use
Using Deep Learning Models for making predictions
Understanding classification ,regression,clustering and dimension reduction
Learining Back Propagation and Forward Propagation
Understanding Cost Function
Performing basic EDA and checking for null values
How to use the summary function in R and interpret the result
Installing h2o and creating h20 clusters for faster calculation
Defining parameters for Deep Learning model
Compute variable importance and performance
Performing GRID search for hyperparameter tuning
Training the model and making predictions using them
Closing the initiated h20 cluster

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.

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Curriculum For This Mini Project

Problem Statement
04m
Import Data Sets
08m
What is Deep Learning?
35m
Understanding H2O
05m
Data Sanity Check
13m
Remove leading white space
03m
Impute Missing values
00m
Initializing H2O
02m
Train H2O model without hidden layer (C )
04m
Hyperparameter optimization
16m
Random Grid Search
10m