Enhancing Financial Decision-Making: Machine Learning and AI-Powered Predictions and Analysis
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915585314521088 |
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| author | Patil, Vishal Bhand, Kavya Mukdam, Kaustubh Sharma, Kavya Kawtikwar, Manas Kavhar, Prajwal Kaware, Hridayansh |
| author_facet | Patil, Vishal Bhand, Kavya Mukdam, Kaustubh Sharma, Kavya Kawtikwar, Manas Kavhar, Prajwal Kaware, Hridayansh |
| contents | The proposed system aims to use various machine learning algorithms to enhance financial prediction and generate highly accurate analyses. It introduces an AI-driven platform which offers inflation-analysis, stock market prediction, and E-learning module powered by a chatbot. It has achieved high accuracy where the Inflation Analysis depicts 0.8% MAE, 1.2% RMSE and the Stock Prediction shows 98% and 96% accuracy for Apple and Google stock prices respectively. Key features include historical price trends, inflation rates, short-term future stock prediction, where the data has been extracted using real-world financial datasets. Additionally, the E-learning feature contributes to bridging financial gaps and promoting informed decisions. We have implemented algorithms like linear regression, ARIMA, LSTM where the accuracy has been evaluated using metrics such as MAE, RMSE and the like. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_25201 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Financial Decision-Making: Machine Learning and AI-Powered Predictions and Analysis Patil, Vishal Bhand, Kavya Mukdam, Kaustubh Sharma, Kavya Kawtikwar, Manas Kavhar, Prajwal Kaware, Hridayansh Computational Engineering, Finance, and Science The proposed system aims to use various machine learning algorithms to enhance financial prediction and generate highly accurate analyses. It introduces an AI-driven platform which offers inflation-analysis, stock market prediction, and E-learning module powered by a chatbot. It has achieved high accuracy where the Inflation Analysis depicts 0.8% MAE, 1.2% RMSE and the Stock Prediction shows 98% and 96% accuracy for Apple and Google stock prices respectively. Key features include historical price trends, inflation rates, short-term future stock prediction, where the data has been extracted using real-world financial datasets. Additionally, the E-learning feature contributes to bridging financial gaps and promoting informed decisions. We have implemented algorithms like linear regression, ARIMA, LSTM where the accuracy has been evaluated using metrics such as MAE, RMSE and the like. |
| title | Enhancing Financial Decision-Making: Machine Learning and AI-Powered Predictions and Analysis |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2510.25201 |