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
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)
This repository consist of projects related to Machine Learning Classification algorithms
Backorder Prediction in R | Visualization | Regression
Collection of Machine Learning Notebook files
A comprehensive set of programs demonstrating machine learning techniques have been made.
Implementation of KNN Classifier, Random Forest Classifier, Logistic Regression
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
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.
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.
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
Image Classification
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