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K-nearest neighbors (KNN) is a supervised machine learning algorithm that is used for classification and regression tasks. It works by finding the K nearest data points to a given input and using their labels to predict the label for the input.
The Loan Prediction project aims to determine whether a loan should be approved or rejected by considering various factors. It uses various machine learning algorithms to reach out the best result.
Using Machine Learning to predict the likelihood of a loan default using a loan data set obtainable from Kaggle. I employed Classification for the building the machine learning models as the target variables were binary, 0 and 1 representing no default and defaulted.
This repository is a testament to the potential of machine learning in medical diagnostics, showcasing how cutting-edge algorithms and rigorous data preprocessing techniques can result in highly accurate predictions.
A multi-output-text-classifier model which can predict the drug uses, dosages and side effects of a particular drug based on a short description of that drug. If the model finds no matching drug with the input, it can suggest some relevant drugs too.
I used lending data to create machine learning models that classify the risk level of given loans. Specifically, I compared the performance of the Logistic Regression model and the Random Forest Classifier.