abhishek4209 / adult-censusincome-prediction

Adult Census Income Prediction

Domain ----> Finance

Demo Video:

https://youtu.be/AdK1KyHy87o?si=1TKDGUKbD1gbV_r2

Deployment URL:

https://adult-censusincome-prediction.onrender.com

Problem Statement:-

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.

Approach:-

The classical machine learning tasks like Data Exploration, Data Cleaning, Feature Engineering, Model Building and Model Testing. Try out different machine learning algorithms that’s best fit for the above case.

Dataset :

The dataset is taken from a Kaggle. You can download the dataset from here

Approach :

Applying machine learing tasks like Data Exploration, Data Cleaning, Feature Engineering, Model Building and model testing to build a solution that should able to predict the premium of the personal for health insurance.

• Data Exploration : Exploring the dataset using pandas, numpy, matplotlib, plotly and seaborn.

• Exploratory Data Analysis : Plotted different graphs to get more insights about dependent and independent variables/features.

• Feature Engineering : Numerical features scaled down and Categorical features encoded.

• Model Building : In this step, first dataset Splitting is done. After that model is trained on different Machine Learning Algorithms such as:

  1. Logistic Regression
  2. Decision Tree Classification
  3. Random Forest Classification
  4. Gradient Boosting Classification
  5. XGBoost Classification

• Model Selection : Tested all the models to check the RMSE & R-squared.

• Pickle File : Selected model as per best RMSE score & R-squared and created pickle file using pickle library.

• Webpage &Deployment : Created a web application that takes all the necessary inputs from the user & shows the output. Then deployed project on the AWS Platform. =======

• Webpage &Deployment : Created a web application that takes all the necessary inputs from the user & shows the output. Then deployed project on the Render Platform.

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Web Inerface :

Libraries used :

1) Pandas
2) Numpy
3) Matplotlib, Seaborn, Plotly
4) Scikit-Learn
5) Flask
6) HTML
7) CSS

Technical Aspects :

1) Python 
2) Front-end : HTML, CSS
3) Back-end : Flask
4) Deployment : Heruko

Usage:

python application.py

Input the characteristics of the diamond when prompted, such as 'age','workclass', 'fnlwgt', 'education', 'marital-status','occupation','relationship', 'race', 'sex', 'capital-gain','capital-loss', 'hours-per-week', 'country'.

The predicted income of the Adult census will be displayed.

Future Improvements:

Experiment with different machine learning algorithms to improve prediction accuracy. Perform feature engineering to extract more relevant information from the dataset. Collect additional data to further train and validate the model.

Contributors:

➡️ABHISHEK UPADHYAY ➡️Email id: [email protected]