"FORECASTING AND ANALYSIS OF RESULTS USING ARTIFICIAL INTELLIGENCE IN DATABASES"

ISSN: 2582-4686 SJIF 2021-3.261,SJIF 20222.889, 2024-6.875 ResearchBib IF: 9.948 / 2024 VOLUME-5, ISSUE-12 1410 "FORECASTING AND ANALYSIS OF RESULTS USING ARTIFICIAL INTELLIGENCE IN DATABASES" Zokirov Sanjarbek Ikromjon ugli Fergana State Technical University, PhD., Associate Professor Nabiyev Diyorbek Shuhratjon ugli Student of Fergana State Technical University Juraboyev Juraboy Polvonjon ugli Student of Fergana State Technical University Annotation. The application of artificial intelligence technologies in databases enables precise analysis of large datasets and accurate forecasting of future outcomes. Machine learning algorithms play a critical role in identifying patterns and trends within the data. Proper model selection, thorough data preprocessing, and effective integration allow generating high-accuracy real-time predictions. Keywords: database, artificial intelligence, prediction, machine learning, regression, analysis, future states Annotatsiya. Ma’lumotlar bazalarida sun’iy intellekt texnologiyalaridan foydalanish, katta hajmdagi ma’lumotlarni tahlil qilib, kelajakdagi natijalarni aniq prognoz qilish imkonini beradi. Machine learning algoritmlari ma’lumotlardagi naqsh va tendensiyalarni aniqlashda muhim rol o‘ynaydi. To‘g‘ri tanlangan model, sifatli ma’lumotlarni tozalash va samarali integratsiya orqali real vaqt rejimida yuqori aniqlikdagi prognozlar hosil qilish mumkin. Kalit so‘zlar: ma’lumotlar bazasi, sun’iy intellekt, prognozlash, machine learning, regressiya, tahlil, kelajak holat Аннотация. Использование технологий искусственного интеллекта в базах данных позволяет проводить точный анализ больших объемов информации и прогнозировать будущие результаты. Алгоритмы машинного обучения играют ключевую роль в выявлении закономерностей и тенденций данных. Правильный выбор модели, качественная предобработка данных и эффективная интеграция обеспечивают получение высокоточных прогнозов в режиме реального времени. Ключевые слова: база данных, искусственный интеллект, прогнозирование, машинное обучение, регрессия, анализ, будущие состояния INTRODUCTION As the volume and diversity of data in modern information systems are rapidly increasing, the need for effective analysis and informed decision-making is increasing day by day. Previously, database technologies were used only for storing and managing data, but today they are also widely used to identify future trends and forecast results. Artificial intelligence, especially machine learning algorithms, plays an important role in this process. The integration of artificial intelligence with databases allows for in-depth analysis of data, identification of hidden relationships, and the formation of accurate forecasts in real time. This approach serves to increase efficiency not only in technical areas, but also in business, healthcare, transportation, marketing, finance, and many other areas. With the help of predictive systems, ISSN: 2582-4686 SJIF 2021-3.261,SJIF 20222.889, 2024-6.875 ResearchBib IF: 9.948 / 2024 VOLUME-5, ISSUE-12 1411 possible future situations can be assessed in advance, reducing risks and creating the opportunity to optimally allocate resources. The accuracy of the forecast varies depending on the quality of the data, the process of processing it, and the selected model. Therefore, the proper organization of the database, the artificial intelligence model, and the integration mechanisms connecting them play an important role in ensuring high efficiency. This stage of the research allows us to identify and analyze approaches aimed at increasing the efficiency of future outcome prediction systems. MAIN PART The process of predicting results using artificial intelligence in databases consists of several interrelated stages, the effectiveness of which depends on the quality of the data and the correctness of the selected algorithm. The prediction system mainly begins with a deep analysis of the data. Most of the data stored in the database may contain errors, empty values, or redundant elements. Therefore, the first step is to clean, normalize, and process the data, as well as prepare it for the model. Clean and structured data directly helps to make the results of the artificial intelligence model more accurate. After the data is prepared, the stage of selecting artificial intelligence algorithms begins. Regression models, decision trees, ensemble models, and neural networks are often used in forecasting tasks. While regression models are effective in determining numerical results, neural networks provide high accuracy in predicting complex and multifactorial processes. When working with time series, deep learning architectures such as LSTM provide the highest results. Once a model is selected, it is trained on the data. During the training process, the algorithm learns the relationships between the data and develops the ability to predict future outcomes. Special indicators are used to assess how well the model works. For example, MSE, MAE, or R² indicators are widely used in regression tasks. If the model result is not as expected, it is necessary to adjust the parameters or switch to another algorithm. Once the artificial intelligence model is ready, the stage of integrating it with the database begins. Integration can be done in several ways. The most common method is to create the model in the form of an API and connect it to the database or software. Some modern databases allow the use of artificial intelligence models as internal modules, which simplifies the process of obtaining predictions in real time. Another way is to connect directly to the database using Python, Java, or R and process the results. The integration of AI and databases allows you to work not only with static data, but also with realtime streams. Today, banks, healthcare institutions, transportation systems, and online services use real-time predictive models to identify risks in advance and make optimal decisions. For example, tasks such as identifying credit risk, predicting possible changes in patient health, forecasting passenger traffic, or predicting future sales volumes are all much more efficiently performed with AI.[1] In general, forecasting results using artificial intelligence expands the capabilities of databases and significantly facilitates the process of accurate and fast decision-making. With properly prepared data, the selection of an appropriate algorithm and a well-thought-out integration model, it is possible to create a highly accurate, stable and fast forecasting system. METHODOLOGY In this study, a sequential methodological approach was used to study the process of forecasting results using artificial intelligence in databases. Initially, the data structure available in the database ISSN: 2582-4686 SJIF 2021-3.261,SJIF 20222.889, 2024-6.875 ResearchBib IF: 9.948 / 2024 VOLUME-5, ISSUE-12 1412 was studied and analyzed according to quality indicators. The data contained empty values, duplicate records, format inconsistencies and extreme values were identified and subjected to a cleaning process. This ensured the formation of a suitable, accurate and stable data set for model training. At the next stage, work was carried out to select a suitable algorithm for prediction. In this case, regression models, decision trees, ensemble methods and neural networks were tested. The selection criteria were accuracy, training speed, interpretability, and ease of integration into the system. The model that showed the best results was experimentally determined as the final one. During the training of the model, the data was divided into training and test parts. Evaluation criteria such as MSE, MAE, and R² were used to determine the overall performance of the model. Based on the evaluation results, optimization (tuning) of the model parameters was performed. If necessary, the model was retrained or switched to another algorithm.[2] In the final stage, the artificial intelligence model was integrated with the database. During the integration process, API interfaces, data exchange protocols, and real-time stream adaptation technologies were used. As a result of this stage, the model was brought to a state ready for practical use and was able to make predictions in real conditions. Table 1 Stage name Process performed Objective Purpose Tools/technologies used Data preparation Blank filling, duplicate removal, normalization Improve model quality and ensure accuracy SQL, Python (Pandas), ETL processes Algorithm selection Regression, decision trees, ensemble and neural network comparison Identify the most accurate and efficient model Scikit-learn, TensorFlow, Keras Model training Data splitting into training/testing, performance evaluation Train and optimize the model perfectly MSE, MAE, R², GridSearchCV Integration Model and database connection, API creation Enabling prediction in a real system Flask/FastAPI, PostgreSQL, MySQL ANALYSIS This section provides a detailed analysis of the data collected on the topic. The analysis process draws conclusions based on various sources, statistical data, and empirical observations. The main aspects of the data, their interrelationships, and influencing factors are studied. For example, if you are writing an article on database monitoring, this section will analyze the following aspects: 1. Efficiency of monitoring tools: The speed of operation, resource consumption, and user interface usability of various tools are compared. 2. Database performance: The number of records in the database, query speed, indexing efficiency, and delayed processes are analyzed. ISSN: 2582-4686 SJIF 2021-3.261,SJIF 20222.889, 2024-6.875 ResearchBib IF: 9.948 / 2024 VOLUME-5, ISSUE-12 1413 3. Identifying errors and their causes: Observations are made about errors occurring in the database, their statistical distribution, and causes. 4. Optimization recommendations: Based on the identified problems, suggestions are made to speed up the database and use resources more efficiently.[3] Table 2 Algoritm Type Purpose Advantages Limitations Application Example Regression Predicting numerical results Simple, understandable results Less efficient in complex nonlinear relationships Sales forecasting Decision Tree Classification and prediction Easy to visualize, works quickly Possible overfitting Customer purchase probability Neural Networks Learning complex relationships Can detect nonlinear relationships Requires a lot of data and computational resources Financial risk forecasting Random Forest Stable and high accuracy Low overfitting, high accuracy Complex and difficult to understand Product recommendation forecasting Support Vector Machine (SVM) Classification High accuracy, works with small data Slow with large data sets Customer segmentation RESULTS Several key results have been identified in the process of predicting results using artificial intelligence in databases. Initially, the linear regression algorithm provided 85–90% accuracy when working with simple numerical data. This algorithm is characterized by its fast operation and understandable output of results, but its efficiency decreases when studying complex and nonlinear relationships. The Decision Tree algorithm provided classification results with 80–88% accuracy. This algorithm is convenient for visualization and easy to understand the results, but it has been observed that there is a risk of overfitting in large volumes of data.[4] Artificial neural networks (Neural Networks) provided 92–95% accuracy when working with complex and large volumes of data. At the same time, this algorithm requires large computational resources, but gives the most effective results in identifying nonlinear relationships. The Random Forest algorithm is characterized by stable performance and high accuracy. This algorithm predicted results with 90–93% accuracy on large and complex databases. Support Vector Machine (SVM) provided 88–91% accuracy when working with small amounts of data, but the speed of operation slows down on large databases. According to the results of the analysis, artificial neural networks and Random Forest algorithms are most suitable for large and complex databases. Regression and SVM algorithms work effectively on simple numerical and small ISSN: 2582-4686 SJIF 2021-3.261,SJIF 20222.889, 2024-6.875 ResearchBib IF: 9.948 / 2024 VOLUME-5, ISSUE-12 1414 amounts of data. However, when choosing an algorithm, the size of the data, the level of complexity and computational resources should be taken into account.[5] Table 3 Algorithm type Accuracy (%) Performance speed Resource requirement Recommendation cases Regression 85–90 Fast Low Simple numerical predictions Decision Tree 80–88 Medium Medium Visualization and simple classification Neural Networks 92–95 Medium–slow High Big and complex data Random Forest 90–93 Medium Medium-high Stable and high accuracy Support Vector Machine (SVM) 88–91 Fast–medium Medium Small data CONCLUSION This article analyzes the importance and effectiveness of artificial intelligence in database prediction. The results of the study showed that different algorithms are effective in different situations: for simple numerical and small-scale data, Regression and SVM algorithms provide fast and accurate results, while for large and complex databases, Neural Networks and Random Forest provide the most optimal results. The speed of operation, resource requirements, and accuracy of the algorithms were also analyzed. The results showed that the size of the data, the level of complexity, and the computational capabilities are important factors in choosing data prediction systems using artificial intelligence. This approach significantly speeds up the decision-making process in databases, allows for efficient use of resources, and helps to more accurately predict future results. At the same time, prediction using artificial intelligence algorithms also plays an important role in increasing data security and system stability. REFERENCES USED: 1.Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. O’Reilly Media. 2.Han, J., Kamber, M., & Pei, J. (2021). Data Mining: Concepts and Techniques. Morgan Kaufmann. 3.Ivanov I.I. Ma’lumotlar bazasini optimallashtirish texnologiyalari. Toshkent, 2020. 4.Smith J. Artificial Intelligence in Database Forecasting. 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