This app predicts the next term as inputted by the user. It deployes and compares two models: a baseline model and another model that uses Good-Turing Smoothing. Both the models uses simple Katz Backoff algorithm based that is based upon the n-grams generated through the training data.

This app predicts the next term as inputted by the user. It deployes and compares two models: a baseline model and another model that uses Good-Turing Smoothing. Both the models uses simple Katz Backoff algorithm based that is based upon the n-grams generated through the training data.

This document explores the accuracy of whether a donor will donate the blood based on historical data. I have compared a few models using ROC curves and confusion matrix. Libraries used: tidyverse caTools pROC randomForest

Libraries Used: tidyverse Sleuth3 UsingR MASS ggplot2

Libraries Used: tidyverse nycflights13 ggplot2 dplyr plyr

Simulation of statistical and probability theory

Heart Data: Ascertain if a heart disease is present or not. Wine Data: Determine the category of wine based on features Wisconsin Breast Cancer Dataset: Determine if a tumor is malignant or benign This problem set explains various concepts such as Logistic Regression, Linear Regression, Trees, Tree Pruning, Random Forests, Cross Validation, and ROC. Libraries used: tidyverse caTools dplyr ggplot2 pROC randomForest MASS ggplot2 DAAG tree GGally Carat

Libraries used: ggplot tidyverse dplyr nycflights13

Libraries used: jsonlite, nycflights13

This is the milestone report for the 10th course (Data Science Capstone) of the Data Science Specialization by John Hopkins University.

This is for the Shiny App developed as part of the Week 4-course project in the Developing Data Products Course within the Data Science Specialization by the John Hopkins University on Coursera

Week 3 Course Project for the Course Developing Data Products in the Data Science Specialization by the John Hopkins University

Coursera week-2 course project in Developing Data Product Course within the Data Science specialization by JHU

This is the second course-project in the Coursera Course: Reproducible Research, which is a part of the Data Science Specialization by John Hopkins University.