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DevanshMistry890/Adult-Census-Income-Prediction

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Adult Census Prediction System 😄

Description

The prominent inequality of wealth and income is a huge concern, especially in the United States. The likelihood of diminishing poverty is one valid reason to reduce the world's surging level of economic inequality. The principle of universal moral equality ensures sustainable development and improves the economic stability of a nation. This study aims to show the usage of machine learning and data mining techniques in providing a solution to the income equality problem. This project is done as an internship under "iNeuron Intelligence Pvt Ltd."

Objectives

The Goal is to predict whether a person has an income of more than 50K a year or not.

This is basically a binary classification problem where a person is classified into the

50K group or <=50K group.

Life Cycle of Machine Learning Project

Life Cycle of implementing machine learning application.

  • Gathering the Data
  • Data Preparation
  • Data Preprocessing
  • Create Model
  • Evaluate Model
  • Deploy the model

Dataset

The UCI Adult Dataset has been used for this purpose, taken from the Kaggle. link is below.

Homepage (Responsive)



🛠️ Requirements

  • Python (Programming Language version 3.7+)
  • Flask (Python Backend Framework)
  • sklearn (Machine Learning Library)
  • pandas (Python Library for Data operations)
  • NumPy (Python Library for Numerical operations)
  • imblearn (sampling Library)
  • VS code (IDE)
  • Azure (Cloud platform)

How to run this code...

  • Create virtual environment
conda create -n myenv python=3.8
  • Activate the environment
conda activate myenv
  • Install the packages
pip install -r requirements.txt
  • Run the app
python app.py

  • Enter valid values in all input boxes and hit Predict.

If everything goes well, you should be able to see the predcited salary Class on the HTML page!

Authors

Devansh Mistry - Linkedin

If you like this project, please do give the star. If you have any suggestions or issues, please drop me a message.