Census-Income-Prediction

Building a classification model for predicting the income using the Adult Census Income Dataset.

Overview:

• In this notebook, we are going to predict whether a person's income is above 50k or below 50k using various features like age, education, and occupation.

Read More about it in my Blogpost

WebApp:

Link: https://census-income-prediction.herokuapp.com/

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Dataset:

Column ID	Column Name	Data type	Values type	Description
0	age	int64	Continous	Age of person
1	workclass	object	Discrete	Workclass of person
2	fnlwgt	int64	Continous	Final weight
3	education	object	Discrete	Education Degree of person
4	education.num	int64	Continous	Number of years of education
5	marital.status	object	Discrete	Marital status of person
6	occupation	object	Discrete	Occupation of person
7	relationship	object	Discrete	Relationship of person
8	race	object	Discrete	Race of person
9	sex	object	Discrete	Sex of person
10	capital.gain	int64	Continous	Capital gain of person
11	capital.loss	int64	Continous	Capital loss of person
12	hours.per.week	int64	Continous	Number of hours per week
13	native.country	object	Discrete	Native country of person
14	income	object	Discrete	Income category of person

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Motivation:

• Building such predictive models can help us better understand the population of a country as well as the various factors affecting the growth in the economy.
• Governments can understand such factors and improve upon them leading to the growth of the country.

Conclusion:

• In this project, we build various models like logistic regression, knn classifier, support vector classifier, decision tree classifier, random forest classifier and xgboost classifier.
• A hyperparameter tuned random forest classifier gives the highest accuracy score of 92.77 and f1 score of 93.08.

Future Work:

• We have a large enough dataset, so we can use neural networks such as an artificial neural network to build a model which can result in better performance.