Practical Application III: Comparing Classifiers

Model Improvement Project

This project focuses on improving machine learning models through feature engineering, hyperparameter tuning, and adjusting performance metrics.

Table of Contents

• Introduction
• Summary of Findings
• Feature Engineering
• Notebook

Introduction

In this project, we aim to enhance the performance of our machine learning models by exploring various techniques such as feature engineering, hyperparameter tuning, and adjusting performance metrics. The goal is to achieve better accuracy and robustness in our predictions.

Summary of Findings

1. Feature Engineering: We explored various features and their impact on model performance. For example, the gender feature was analyzed for its correlation with the target variable. We found that certain features significantly improved model accuracy.
2. Hyperparameter Tuning: Using GridSearchCV, we optimized hyperparameters for KNN and Decision Tree models. This resulted in improved cross-validation accuracy for both models.
3. Performance Metrics: We evaluated different performance metrics such as precision, recall, F1-score, and AUC-ROC. This helped us better understand model performance, especially for imbalanced datasets.

Feature Engineering

Feature engineering involves creating new features or modifying existing ones to improve model performance. For example, we might consider whether to keep the gender feature based on its correlation with the target variable and its impact on model performance.

Notebook

https://github.com/ranjeevkumar/Practicalapp3/blob/main/prompt_III.ipynb