This project aims to classify whether a person is earning over 50k or not based on various features. We will use classification and semi-supervised learning techniques on the UCI Machine Learning Repository dataset. The goal is to build models to predict a person's income level accurately.
We are using the UCI Machine Learning Repository dataset, which contains information about individuals and their income levels. The dataset has the following features:
Age: Age of the individual.Education Level: The highest level of education completed.Occupation: Type of occupation.Marital Status: Marital status of the individual.Race: Race of the individual.- ...
Income: Contains values '<=50k' or '>50k', indicating whether a person earns above or below 50k.
The dataset was obtained from the UCI Machine Learning Repository. It was loaded using Python, and an initial exploration was conducted to understand its structure.
We performed data cleaning to handle missing values and outliers. Additionally, categorical variables were encoded using techniques such as one-hot encoding.
After a careful analysis, we selected Age, Education Level, Occupation, Marital Status, Race, and other relevant features for classification.
The dataset was split into training and testing sets with an 80% training and 20% testing ratio.
We implemented two classification algorithms for this project:
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Logistic Regression:
- Logistic regression is a popular classification algorithm that models the probability of a binary outcome.
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Random Forest Classifier:
- Random Forest is an ensemble learning method that uses multiple decision trees to make predictions.
We also explored semi-supervised learning using the LabelPropagation algorithm. Semi-supervised learning leverages both labeled and unlabeled data to improve classification performance.
In conclusion, we have successfully applied classification and semi-supervised learning techniques to predict income levels.