Project Title: Predicting Census Income using Machine Learning Techniques

Project Description:

In this project, I applied machine learning techniques to predict the annual income of individuals based on census data. The objective of the project was to identify patterns and relationships in the data and build a model to accurately predict income.

Methods:

• Collected and preprocessed the census data using Python libraries such as Pandas and Numpy
• Explored and visualized the data to identify patterns and relationships
• Built and evaluated several machine learning models, including Decision Trees, Random Forest, and Gradient Boosting
• Optimized the model using techniques such as hyperparameter tuning and feature selection
• Evaluated the performance of the final model using metrics such as accuracy and F1 score

Key Findings:

• The Gradient Boosting model outperformed the other models and had the highest accuracy and F1 score
• Important features that had a significant impact on income prediction included education level, occupation, and age

Conclusion:

The results of this project demonstrated my ability to apply machine learning techniques to a real-world problem and extract meaningful insights from data. The model developed in this project could be used to predict the income of individuals and support decision-making in various industries. This project showcases my technical skills in Python, data analysis, and machine learning.