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This is basically a binary classification problem where a person is classified into the >50K group or <=50K group.

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sriphaniN/adult-income-prediction

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INTERNSHIP PROJECT

adult-income-prediction project

Problem Statement:

The dataset contains different features like age,gender,education,occupation,capital-gain,capital-loss,race,work per hour,country etc. The proposed approach will implement a different techniques and algorithms like Random Forest and Boosting Techniques. Random Forest performed well with 86% accuracy.

Approach used This is basically a binary classification problem where a person is classified into the >50K group or <=50K group

Data Exploration : I started exploring dataset using pandas,numpy and pandas-profiling.

Data visualization : Ploted graphs to get insights about dependend and independed variables.

Feature Engineering : Removed missing values and created new features as per insights.

Model Selection I : Tested all base models to check the base accuracy, Also ploted residual plot to check whether a model is a good fit or not.

Pickle File : Selected model as per best accuracy and created pickle file .

Technologies Used

Python Sklearn Flask Html Pandas Numpy pandas-profiling

Project Title: Adult Census Income Prediction

Technologies: Machine Learning Technology

Domain :Finance

Project Difficulties level: Intermediate

video link of depolyment: https://github.com/sriphaniN/adult-income-prediction/blob/4057050a051344dc3c16fb4a65cf1f0c00e0edde/project1/prediction.webm

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This is basically a binary classification problem where a person is classified into the >50K group or <=50K group.

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