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Due to significant technology advancements, people's needs have expanded. As a result, there have been more requests for loan approval in the banking sector. A few qualities, taken for consideration, when choosing a candidate for loan approval in order to, determine loan's status. Banks face a major challenge; when it, comes to assessing loan applications and lowering the risks associated with potential borrower defaults. Since they must thoroughly evaluate each borrower's eligibility for a loan, banks find this process to be particularly challenging. This research proposes combining machine learning (ML) models and ensemble learning approaches to find the probability of accepting individual loan requests. This tactic can increase the accuracy with which qualified candidates are selected from a pool of applicants. As a result, this method can be used to address the problems with loan approval processes outlined above. Both the loan applicants and the bank employees profit from the strategy's dramatic reduction in sanctioning time. Because of the banking industry's expansion, more people were applying to loans at banks. In order to predict the accuracy of loan approval status for applied person, we used four different algorithms namely Random Forest, Naive Bayes, Decision Tree, and KNN. By using these, we obtained better accuracy of 83.73% with Naïve Bayes algorithm as best one.

A very challenging and difficult part of finding a cure for Rheumatoid Arthritis ( RA) disease is the same as finding a cure for an autoimmune disease. In the case of autoimmune diseases, it is very difficult to find the cause. RA is also one of the autoimmune diseases that causes inflammation in joints. It does not only limit to joints but also can spread or occur in various parts of the body like skin, lungs, and many others. Many features like age, sex, and previous history of certain diseases affect the occurrence of RA. The proposed ML model uses a decision tree algorithm to analyze a comprehensive set of clinical and laboratory variables collected from RA patients. These variables include age, ID, time, treatment, gender, and other baseline features. The ML model employs feature selection techniques to identify the most relevant predictors that contribute to disease progression. By training the decision tree algorithm on a large dataset of RA patients, the model generates an accurate predictive model for assessing disease severity and progression. The model is able to detect RA disease with almost 80% accuracy with the given training dataset. RA can have many other symptoms and characteristics. This data was trained on a basic dataset available from the Kaggle website. The dataset is in CSV format and has 6 features. The model was trained using a decision tree algorithm, which categorizes the data into different categories and then checks for specific data when needed in Jupyter Notebook. The study explored detecting basic symptoms and characteristics of RA disease. The Decision tree model has given accuracy of 86.5% and 74.4% on training and testing respectively. So Accuracy of the model is pretty good for the dataset used.

A very challenging and difficult part of finding a cure for Rheumatoid Arthritis ( RA) disease is the same as finding a cure for an autoimmune disease. In the case of autoimmune diseases, it is very difficult to find the cause. RA is also one of the autoimmune diseases that causes inflammation in joints. It does not only limit to joints but also can spread or occur in various parts of the body like skin, lungs, and many others. Many features like age, sex, and previous history of certain diseases affect the occurrence of RA. The proposed ML model uses a decision tree algorithm to analyze a comprehensive set of clinical and laboratory variables collected from RA patients. These variables include age, ID, time, treatment, gender, and other baseline features. The ML model employs feature selection techniques to identify the most relevant predictors that contribute to disease progression. By training the decision tree algorithm on a large dataset of RA patients, the model generates an accurate predictive model for assessing disease severity and progression. The model is able to detect RA disease with almost 80% accuracy with the given training dataset. RA can have many other symptoms and characteristics. This data was trained on a basic dataset available from the Kaggle website. The dataset is in CSV format and has 6 features. The model was trained using a decision tree algorithm, which categorizes the data into different categories and then checks for specific data when needed in Jupyter Notebook. The study explored detecting basic symptoms and characteristics of RA disease. The Decision tree model has given accuracy of 86.5% and 74.4% on training and testing respectively. So Accuracy of the model is pretty good for the dataset used.

Due to significant technology advancements, people's needs have expanded. As a result, there have been more requests for loan approval in the banking sector. A few qualities, taken for consideration, when choosing a candidate for loan approval in order to, determine loan's status. Banks face a major challenge; when it, comes to assessing loan applications and lowering the risks associated with potential borrower defaults. Since they must thoroughly evaluate each borrower's eligibility for a loan, banks find this process to be particularly challenging. This research proposes combining machine learning (ML) models and ensemble learning approaches to find the probability of accepting individual loan requests. This tactic can increase the accuracy with which qualified candidates are selected from a pool of applicants. As a result, this method can be used to address the problems with loan approval processes outlined above. Both the loan applicants and the bank employees profit from the strategy's dramatic reduction in sanctioning time. Because of the banking industry's expansion, more people were applying to loans at banks. In order to predict the accuracy of loan approval status for applied person, we used four different algorithms namely Random Forest, Naive Bayes, Decision Tree, and KNN. By using these, we obtained better accuracy of 83.73% with Naïve Bayes algorithm as best one.