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Community Blog Analyzing Census Data Using Alibaba Cloud's Machine Learning Platform

Analyzing Census Data Using Alibaba Cloud's Machine Learning Platform

This article shows you how to set up a machine learning platform with Alibaba Cloud Machine Learning Platform for AI to analyze census data.

By Garvin Li

A census is an official survey of a population that records the details of individuals in various aspects. Through census data, we can measure the correlation of certain characteristics of the population, such as the impact of education on income level. This assessment can be made based on other attributes such as age, geographical location, and gender. In this article, we will show you how to set up the Alibaba Cloud Machine Learning Platform for AI product to perform a similar experiment using census data.

Dataset Introduction

Data source: UCI open source dataset Adult is a census result for a certain region in the United States, with a total of 32,561 instances. The detailed fields are as follows:

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Data Exploration Procedure

On the Machine Learning Console home page, select the census case and click Create from Template as shown below.

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The experiment interface is as shown in the following figure.

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  1. The first part of the figure is the component area. The user can drag it to the blank area in the middle to set up the experiment.
  2. The second part of the figure is the experimental area. The user can set up an experiment in this area.
  3. The third part of the figure is the component configuration area. The user can configure component parameters in this area.

The experiment includes three parts, as shown in the following figure.

The first part relates to the data source preparation, the second part relates to the data statistics, and the third part relates to the impact of education on income.

Data Source Preparation

Upload the data to MaxCompute via machine learning IDE or Command line tool Tunnel. Read the data through the Read Table component (Data source - Demographics in the figure). Then right click on the component to view the data, as shown below.

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Data Statistics

Through the full table statistics and numerical distribution statistics (data view and histogram component in the experiment), it can be determined whether a piece of data conforms to the Poisson distribution or the Gaussian distribution, and whether it is continuous or discrete.

Each component of Alibaba Cloud Machine Learning provides result visualization. The figure below is the output of the histogram component of the numerical statistics, in which the distribution of each input record can be clearly seen.

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Impact of Education on Income

Through feature extraction, machine learning algorithms are used to compute which factors have the greatest impact on income. This document simply analyzes the income of people with different education levels. The main purpose is to introduce the use of the machine learning platform.

Data preprocessing

As shown in the following figure, the first component the data passing through is the SQL script, which implements data preprocessing. This experiment converts the "income" field from string type into a binary form of 0 and 1. 0 means an annual income below 50K, and 1 means an annual income above 50K (digitizing text data is a common method in machine learning feature processing).

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Filtering and Mapping

Through the filtering and mapping component, the data is divided into three parts based on the education, namely, doctor, master and bachelor, as shown in the following figure.

The filtering and mapping component supports SQL statements, and the user needs to fill in the "where" filter in the configuration bar on the right.

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Result Statistics

The income proportion under each class can be obtained through the percentile components. The following is the line chart presentation. It can be seen that the population with an annual income below 50K (dots with the value of 0) accounts for about 25% of the total number.

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Combine the three percentile components to get the results shown below.

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GarvinLi

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