Machine learning algorithms implemented in python. Some are implemented in R. Algorithms include XGBoost, Convolutional Neural Network, Recursive Neural Network, Support Vector Machine, K-nearest neighbors, Naive Bayes, Natural Language Processing

Sentiment Analysis of Movies Dataset

MACHINE LEARNING ALGORITHMS

Machine Learning model to predict Red Wine Quality using Random Forest Classifier

random forest classification (with hyperparameter tuning) on heart disease dataset.

Predicted the disease using the symptoms observed in the patients.

In this project the data is been used from UCI Machinery Repository. Main aim of this project is to predict telling tumor of each patient is Benign (class – 2) or Malignant (class – 4) the models used are – Decision tree Classification, Logistic Regression, K-Nearest Neighbors, SVM, Kernel SVM, Naïve-Bayes and Random Forest Classification.

Data analysis project on Digital Addiction for master thesis

Prediction of students' dropout using classification models. Data visualisation, feature selection, dimensionality reduction, model selection and interpretation, parameters tuning.

Implemented and compared Random Forest, Decision Tree, KNN, SVM, and Logistic Regression outcomes with a confusion matrix. Concluded that Random Forest achieved the highest accuracy of 85% to predict the loan status for investors.

This repository will help in understanding the basic concept of Random Forest algorithm and will also learn how to optimize the hyperparameters and prevent overfitting.

Minimal implementation of Random Forest classifier using decision stumps and bootstrap sampling without sklearn.

Random forests is a supervised learning algorithm. It can be used both for classification and regression. It is also the most flexible and easy to use algorithm. A forest is comprised of trees. It is said that the more trees it has, the more robust a forest is. Random forests creates decision trees on randomly selected data samples, gets predict…

"Stock Predictor" project basically aims to provide a visual representation and analysis of data related to time-series data which is constantly changing. This provides a dashboard to user displaying current trends and stocks data which uses ML like "LSTM" and "Random Forest" model.

Build and evaluate classification model using PySpark 3.0.1 library.

Audio Pattern Recognition project - Music Genres Classification

Full machine learning practical with R.

Driver Analysis with Factors and Forests: An Automated Data Science Tool using Python