Publications by Nandini Guntur
Milestone Report
Introduction The goal of the capstone project is to create a predictive text model using a large text corpus of documents as training data. Natural language processing techniques will be used to perform the analysis and build the predictive model. This milestone report describes the major features of the training data with our exploratory data an...
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R Package Vignette
Mastering Software Development in R Nandini Guntur 11/29/2020 Table of Contents 1. Introduction 1.1. Timeline Plots 1.2. OpenStreet Maps 2. MSDR Package 3. Usage 3.1. Loading Data 3.2. My first geom_timeline plot 3.3. Adding Annotations 3.4. Earthquakes Info in OpenStreet maps 3.5. Earthquakes Popup in OpenStreet maps 1. Introduction This c...
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Prediction Model Using Bayesian Inference
Introduction This project will focus on attributes make a movie popular as well as how much audiences and critics like movies and numerous other variables about the movies. The dataset was randomly collected from Rotten Tomatoes and IMDB in the US. Set up 2.1 Load packages 2.2 Load data load("movies.Rdata") Scope of Inference: This dataset inc...
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Data Visualization 2
An Information Flow Model for Conflict and Fission in Small Group Overview This file implements Programming Assignment 2: Visualize Network Data for the Coursera Data Visualization Class. This assignment uses R Programming Language to visualize data. Q1. What is the data that you chose? Why? Data from a voluntary association are used to construct...
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Statistical Model for Real Estate
Background As a statistical consultant working for a real estate investment firm, your task is to develop a model to predict the selling price of a given home in Ames, Iowa. Your employer hopes to use this information to help assess whether the asking price of a house is higher or lower than the true value of the house. If the home is undervalue...
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Statistics Quiz Document
First, let us load the data and necessary packages: load("ames_train.Rdata") library(MASS) library(ggplot2) library(dplyr) ## ## Attaching package: 'dplyr' ## The following object is masked from 'package:MASS': ## ## select ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects ...
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