Identification of trends in the stock prices of a company by performing fundamental analysis of the company. News articles were provided as training data-sets to the model which classified the articles as positive or neutral. Sentiment score was computed by calculating the difference between positive and negative words present in the news articl…

Pixel based classification of satellite imagery - feature generation using Orfeo Toolbox, feature selection using Learning Vector Quantization, CLassification using Decision Tree, Neural Networks, Random Forests, KNN and Naive Bayes Classifier

Training ensemble machine learning classifiers, with flexible templates for repeated cross-validation and parameter tuning

This Repository Contains R-Codes executed on various Datasets in RStudio. I Hope This Repository is very helpful for those who are Willing to build their Career in Data Science, Big Data.

Exercises From Book "Applied Predictive Modeling" by "Kuhn and Johnson (2013)"

Various Classification models used are Logistic regression, K-NN, Support Vector Machine, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification using R

Analyze NASDAQ100 stock data. Used ARIMA + GARCH model and machine learning techniques Naive Bayes and Decision tree to determine if we go long or short for a given stock on a particular day

Designing and applying unsupervised learning on the Radar signals to perform clustering using K-means and Expectation maximization for Gausian mixture models to study ionosphere structure. Both the algorithms have been implemented without the use of any built-in packages. The Dataset can be found here: https://archive.ics.uci.edu/ml/datasets/ion…

A tidy implementation of the Naive Bayes classifier

Text Mining / data mining - model, GUI in R/shiny-app

A Naive Bayes Classifier and confusion matrix built for the Wisconsin Breast Cancer Database.

The program is written in R which analysis patient's health condition using sentiment analysis and classifies as exist, deteriorate and recover using machine learning algorithm - Naive bayes

a predictive model to determine the income level for people in US. Imputed and manipulated large and high dimensional data using data.table in R. Performed SMOTE as the dataset is highly imbalanced. Developed naïve Bayes, XGBoost and SVM models for classification

Data Science (Data preprocessing) along with machine learning where patients with digestive and kidney diseases are predicted using(kNN, Naïve Bayes , and Random Forest) classifiers in R Programming Language

Portfolio of machine learning projects

Supplementary material to chapter 'Computational Sermon Analysis' in 'Predigtanalyse. Zugänge zu einer homiletischen Grundaufgabe' (2024)

Naive Bayes, Confusion Matrix, and ROC Analysis were conducted using R to determine how different variables lead to a customer of a bank taking out a personal bank loan.

Naive Bayes Machine Learning Model

Six machine learning models for predicting bladder cancer progression using Caret package

Loyola Chicago Course - INFS 348