Practicing with different machine learning algorithms and take notes about machine learning courses from Codecademy
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Updated
May 20, 2023 - Jupyter Notebook
Practicing with different machine learning algorithms and take notes about machine learning courses from Codecademy
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.
A demonstration of the basic Machine Learning Algorithms
Supervised and unsupervised analysis
This repository consist of projects related to Machine Learning Classification algorithms
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.
Machine through SKLearn
Heart Disease Prediction using Machine Learning (Classification Use Case)
Backorder Prediction in R | Visualization | Regression
Collection of Machine Learning Notebook files
Implementation of KNN Classifier, Random Forest Classifier, Logistic Regression
A comprehensive set of programs demonstrating machine learning techniques have been made.
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.
Dartmouth COSC 274: Machine Learning models for Amazon Reviews dataset
Make the best model to predict heart attack for patients using machine learning. Three types of models are used: Logistic Regression, Support Vector Machines, Decision Tree and the results will be compared the accuracy and F1-score to determine the best model.
Predicting whether a person will become a diabetes based on several diagnostic measurements
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.
Image Classification
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