NLP Capstone Word Prediction using n-Gram model using Jurafsky's interpolation method

NLP Capstone Word Prediction using n-Gram model using Jurafsky's interpolation method

NLP Capstone Word Prediction using n-Gram model using Jurafsky Interpolation method

NLP Capstone Word Prediction using n-Gram Jurafsky Interpolation method

NLP Capstone Word Prediction using n-Gram model

NLP Capstone Word Prediction using n-Gram model

NLP Capstone Word Prediction using n-Gram model

NLP Capstone word prediction from n-Gram model

NLP Capstone Word Prediction Shiny App

This Shiny app implements Jurafsky's NLP n-Gram Markov model to predict next word for a given phrase input by the user

This Shiny app implements Jurafsky's NLP n-Gram Markov model to predict next word for a given phrase input by the user

This Shiny app implements Jurafsky's NLP n-Gram Markov model to predict next word for a given phrase input by the user

This is a milestone report to demonstrate preparation for developing a prediction algorithm for the word prediction problem. The report summarizes basic steps to preprocess and conduct exploratory data analysis (EDA) of U.S English language corpus comprising three documents: blogs, news, and twitter text.

Pitch for a Shiny App to predict diamond prices based on Random Forest models. This is a project submission for a Coursera project on Developing Data Products.

Pitch for a Shiny UI for regression analysis of diamond data set for predicting the diamond prices

This is a pitch for a shiny application submitted for Developing Data Products course offered by Coursera. The application provides a simple UI to analyze the well known diamond data set for predicting the diamond prices from a test set.

This is a project report for the Regression Models course which explores some linear multivariable regression models for MPG (miles per gallon) as an outcome variable versus AM (automatic(0)/manual(1) transmission) as a primary predictor along with other covariates in the mtcars dataset. Several candidate regression models are considered for selecting a reasonable fit using ANOVA for comparing the overall significance of rejecting the null hypothesis for each candidate. At the outset, selected metrics are evaluated to check sanity of the selected model

This is a Statistical Inference Course Project report which attempts to do basic exploratory analysis of the ToothGrowth dataset.

The analysis compares the sample mean and sample variance with the population mean and population variance using Monte Carlo simulation for drawing the samples. This work is part of Coursera course project for Statistical Inference.

This is a RR Course Project report which identifies severe weather events based on processing and analysis of U.S. National Oceanic and Atmospheric Administration’s (NOAA) Storm Database. The preprocessing of the data attempts to clean up missing, redundant, and inconsistent values of event types in the source data to prevent data contamination. This report has tentative conclusions to help National or State agencies in optimal allocation of limited resources available by focussing attention to a small but dominant subset of a large collection of severe weather events, which contribute disproportionately to elevated loss of human life, injuries, and damage to property and crops. The report also attempts to explore the impact of these events in terms of geographic and temporal dominance to bring further focus on targeted allocation of resources.

This is a RR Course Project report which identifies severe weather events based on processing and analysis of U.S. National Oceanic and Atmospheric Administration’s (NOAA) Storm Database. This report has tentative conclusions to help National or State agencies in optimal allocation of limited resources available by focussing attention to a small but dominant subset of a large collection of severe weather events, which contribute disproportionately to elevated loss of human life, injuries, and damage to property and crops. The report also attempts to explore the impact of these events in terms of geographic and temporal dominance to bring further focus on targeted allocation of resources.