Analysis And Prediction Of Income And Economic Hierarchy On Census Data Using Data Analytics And Dat

Research p a p e r

Analysis and Prediction of Income and Economic Hierarchy on Census Data using Data Analytics TAGSMachine-learning, Data Analytics, data analytics, Statistical Analysis, Statistical data analysis, Data Interpretations, Cluster Analysis, Statistical Analysis, Data Analytics.

SERVICESResearch Planning | Data Collection | Semantic Annotation | Business Analytics | Bio Statistics

Copyright ÂŠ 2019 Statswrok. All rights

The greatest trouble in the machine- learning field is the availability of clean and high quality datasets.

IN TRODUCTION

Demographic data constitutes a major role in the economic growth of the nation.

It helps in finding the income growth of the people, how much are from urban and rural areas and how educated every person in the nation.

Example :DATA ANALYTICS IN PREDICTING THE INCOME AND ECONOMIC HIERARCHY ON CENSUS DATA ANALYTICS.

.

The data analytics method to predict the income and economic hierarchy on the census data obtained from Kaggle Sharath et al (2016) . The dataset involve 3.5 million U.S. households consists of their education, work, transportation they use, usage of internet, etc. Before analysing the data, the main pre-requisite is that the data must be normalized for performing Statistical Analysis. Hadoop is used as a first stage for the large dataset and PIG MapReduce is adopted for the normalisation of the dataset. Later, the statistical analysis is performed and the results are interpreted. OCHARINTO COACHING | 202 Copyright ÂŠM 2019 Statswrok. All rights reserved 0

Aim of the study .

.

Gender distribution against occupation Relationship between education and salary Economic hierarchy and prediction of classes Plotting theoretical versus the actual values for Benfordâ&#x20AC;&#x2122;s Law Mean and Median of Income using Heatmap

Huge Dataset

Fig. 1: Step by step procedure

LOAD

HADOOP

PIG for MapReduce

NORMALIZED DATA

Graphical Representatio n and Interpretation

Data Mining/ statistical Analysis

Final Processed Data

Personal Care

80%

7 8%

Building Cleaning

68%

5 8%

56%

78%

2 3%

Foo d

55%

3 5%

0

70%

Farming, Fishing

65%

Farming, Fishing

7 5%

Building Cleaning

75%

Foo d

88%

10

20

30

40

50

60

(a) Before Normalization

70

40%

3 0%

0

10

20

30

40

50

60

70

(b) After Normalization

RESULTS & DISCUSSIONS

.

It is noted that the percentage of men in farming and fishing industry is found to be

increased

. .

for 3.8% after normalization and the percentage of women in that field is also gets

Normalization is usedthe to reduce theimportance execution time and improvesofthe This clearly satisfies need and of normalization theefficiency census of the results. increased. Two normalization is done for this purpose but before that there exists few blank entries data. in the dataset and this can be handled to avoid invalid results. First level, the actual data is used without any modifications

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Data Interpretations Fig 3: Depicts the first objective of the study i.e. obtaining the percentage of gender distribution against occupation.

.

Percentage of men in the dale s field i s mo r e in n u mbe r than compa r ed to

o t he rs .

..

T r an s po rt a t ion field al s o con t ain s highe r percentage of men next to the Sale s . In addi t ion , percentage of w omen i s di str ib ut ed almo st eq u all y in all the occ u pa t ional field s .

.

F urt he r , in o r de r to achieve the s econd objec t i v e , bo x plo t t echniq u e i s us ed to iden t if y the r ela t ion s hip between the ed u ca t ion and s ala ry .

.

Thi s help s in u nde rst anding the income g r o wt h u nde r diffe r en t le v el s of

ed u ca t ion .

.

U su all y , the mo r e educated pe rs on g aph , tPhighe r ofe ssrional deg ree are wrill ge s ala ry . Ho w eholde v e r , frs r om tge hi stting mo r e s ala ry than the doctorate degree holde rs w hich i s q u i t e u n usu al .

.

Like w i s e , one can compa r e the median and q u a rt ile s of each field in the bo x plo t

K-MEANS CLUSTERING

. . .

Cluster Analysis methods are the useful tool for analysing large dimensional

dataset. However, K-means clustering is the most versatile technique for getting valid and the distance between each data values and a set of clusters are measured using centroid clustering method and then plotted the against the to achieve the economic hierarchy, k-means clustering technique is adopted for economic results. In cluster that sense, in order classes.

. . .

income variable, Even though, the clustering technique is widely used in the literature, the problem of finding the number of clusters

Finally, the mean and median the income the states is versus depicted heat Furthermore, Benfordâ&#x20AC;&#x2122;s law isofdiscussed foracross plotting the actual theusing theoretical maps. still persist. In addition, the time complexity of the performance of the analysis gets decreased by including the level of normalization is also discussed and are tabulated below. values. Copyright ÂŠ 2019 Statswrok. All rights reserved

Summar y

Handling large datasets becomes a major problem in the Statistical Analysis because of the inferring invalid results. Recently with the advent of computational strategies, researchers are involved in handling big data in easier way particularly using Hadoop and MapReduce techniques. There are lot of scope for the Data Scientist to handle big data through machine learning and deep learning techniques. The development of the nation is analysed using the census data collected during certain periods. This helps in understanding the growth of the population, wealth of the nation, and understanding the needs for the improvement for the welfare of the nation and people.

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Analysis And Prediction Of Income And Economic Hierarchy On Census Data Using Data Analytics And Dat

Published on Dec 12, 2019

Statswork

Handling large datasets becomes a major problem in the Statistical Analysis because of the inferring invalid results. However, recently with the advent of computational strategies, researchers are involved in handling big data in an easier way particularly using Hadoop and MapReduce techniques. There is a lot of scope for the Data Scientist to handle big data through machine learning and deep learning techniques. Statswork offers statistical services as per the requirements of the customers. When you Order statistical Services at Statswork, we promise you the following – Always on Time, outstanding customer support, and High-quality Subject Matter Experts.   Contact Us: Website: www.statswork.com Email: info@statswork.com UnitedKingdom: +44-1143520021 India: +91-4448137070 WhatsApp: +91-8754446690