Scenario Design for Recommender system

This is the final project Proposal for Statistics and probability course. DATA 606

Instructions for Assignment: In Text Mining with R, Chapter 2 looks at Sentiment Analysis. In this assignment, you should start by getting the primary example code from chapter 2 working in an R Markdown document. You should provide a citation to this base code. You’re then asked to extend the code in two ways: Work with a different corpus of your choosing, and Incorporate at least one additional sentiment lexicon (possibly from another R package that you’ve found through research).

The New York Times web site provides a rich set of APIs, as described here: https://developer.nytimes.com/apis You’ll need to start by signing up for an API key. Your task is to choose one of the New York Times APIs, construct an interface in R to read in the JSON data, and transform it into an R DataFrame.

Working with XML and JSON in R. Mehreen Ali Gillani

This analysis of the Exercise Schedule & Meal Plan dataset revealed several key insights into fitness patterns across gender, BMI categories, and fitness goals. Mehreen Ali Gillani

This analysis demonstrates that Walmart’s sales[2010-2012] performance is influenced by a complex interplay of store-specific factors, economic conditions, and seasonal patterns. The findings highlight both the resilience of the retail operation and significant opportunities for targeted improvements.

Create a .CSV file (or optionally, a MySQL database!) that includes all of the information included in the dataset. You’re encouraged to use a “wide” structure similar to how the information appears in the discussion item, so that you can practice tidying and transformations as described below.  Read the information from your .CSV file into R, and use tidyr and dplyr as needed to tidy and transform your data. [Most of your grade will be based on this step!]  Perform the analysis requested in the discussion item. Mehreen Ali Gillani

Create a .CSV file (or optionally, a MySQL database!) that includes all of the information included in the dataset. You’re encouraged to use a “wide” structure similar to how the information appears in the discussion item, so that you can practice tidying and transformations as described below.  Read the information from your .CSV file into R, and use tidyr and dplyr as needed to tidy and transform your data. [Most of your grade will be based on this step!]  Perform the analysis requested in the discussion item. Mehreen Ali Gillani

Chess ELO calculations Based on difference in ratings between the chess players and each of their opponents in our Project 1 tournament, calculate each player’s expected score (e.g. 4.3) and the difference from their actual score (e.g 4.0). List the five players who most overperformed relative to their expected score, and the five players that most underperformed relative to their expected score. You’ll find some small differences in different implementation of ELO formulas. You may use any reasonably-sourced formula, but please cite your source. Submitted by: Mehreen Ali Gillani

Chess Tournament Performance Analysis

Global Baseline Estimates

Implementation of window function on Apple and Microsoft stock data

Mehreen Ali Gillani

Mehreen Ali Gillani

Updated by Mehreen Ali Gillani

This is the first assignment for course 607.

This is my first assignment for 607. It includes R code data file.