• Shaik Zareena
• P Jaya Surya
• T Divya Rani
• B. Sanjeev
• Dr. Subhani Shaik

In the rapidly evolving landscape of financial services, the accurate assessment of creditworthiness is paramount. This research paper delves into the realm of machine learning algorithms to ascertain their efficacy in predicting credit scores. The research employs a comprehensive dataset comprising diverse financial indicators and demographic information. By systematically evaluating the performance of each algorithm against this dataset, we aim to discern the strengths and limitations of different approaches in capturing the intricacies of credit risk. Our findings shed light on the most optimal machine learning algorithm for credit score prediction, considering both predictive accuracy and interpretability. The study compares various types of machines learning algorithms. Among all machine learning algorithms Logistic Regression is good in accuracy, F score, sensitivity and time also low. For time taken point of view KNN consume less time to finish the work but accuracy is low comparatively Logistic Regression. AdaBoost algorithm perform less compared to all machine learning algorithms and it take huge amount of time for execution of the data classification.

Machine Learning Algorithms, Credit Score Prediction, Accuracy, Efficiency.

"MACHINE LEARNING ALGORITHMS FOR FINDING CREDIT SCORE PREDICTION FOR OPTIMAL OUTCOME", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.l368-l375, April-2024, Available :http://www.jetir.org/papers/JETIR2404B50.pdf

"MACHINE LEARNING ALGORITHMS FOR FINDING CREDIT SCORE PREDICTION FOR OPTIMAL OUTCOME", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppl368-l375, April-2024, Available at : http://www.jetir.org/papers/JETIR2404B50.pdf