moniaravind / demographic-data-analysis-and-income-prediction Goto Github PK

This machine learning project involves the analysis of a dataset without converting categorical columns to numeric values. Key steps include:

Data Exploration: Analyzing the dataset's shape, columns, and exploring age-related statistics. Investigating work class and income distribution for insights.

Data Visualization: Creating bar charts to visualize education levels and generating pie charts for sex and marital status.

Data Manipulation: Renaming and selecting relevant columns, dropping unnecessary ones. Handling null and duplicate values to ensure data integrity.

Data Preprocessing: Transforming categorical columns into numeric using label encoding techniques. Scaling features for uniformity using StandardScaler.

Model Training: Splitting the data into training and testing sets. Utilizing logistic regression, linear regression, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) models for predictions.

Hyperparameter Tuning: Employing grid search for optimizing model performance.

Prediction: Using the trained models to predict income class for new entries.

This project showcases proficiency in data preprocessing, visualization, and machine learning model implementation for classification tasks.