Summary of Optimizing Fintech Marketing: a Comparative Study Of Logistic Regression and Xgboost, by Sahar Yarmohammadtoosky Dinesh Chowdary Attota
Optimizing Fintech Marketing: A Comparative Study of Logistic Regression and XGBoost
by Sahar Yarmohammadtoosky Dinesh Chowdary Attota
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Statistical Finance (q-fin.ST); Applications (stat.AP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research aims to predict customer responses to mail campaigns and assess the likelihood of default among those who engage. To achieve this, the study employs advanced machine learning techniques, including logistic regression and XGBoost, to analyze consumer behavior. The authors integrate different data preprocessing strategies, such as imputation and binning, to enhance the robustness and accuracy of their predictive models. The results indicate that XGBoost consistently outperforms logistic regression across various metrics, particularly in scenarios using categorical binning and custom imputation. These findings suggest that XGBoost is effective in handling complex data structures and provides a strong predictive capability in assessing credit risk. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study tries to figure out why people respond or don’t respond to mail campaigns asking them to apply for credit cards. It uses special computer programs called machine learning techniques to understand how people behave before they decide whether to get a new card. The researchers used two different methods, logistic regression and XGBoost, to see which one works best. They also cleaned up the data by filling in missing information and grouping similar things together. The results show that XGBoost is better at making predictions than the other method, especially when dealing with complicated data. This means that XGBoost can be a useful tool for banks and lenders to help them decide who is likely to pay back their debts. |
Keywords
» Artificial intelligence » Likelihood » Logistic regression » Machine learning » Xgboost
