arao07's repositories
IST652-Final-Project
Dataset that we used- We have decided to use Seattle Airbnb Open Data sourced from Kaggle.com for our project. This dataset describes the listing activity of homestays in Seattle, WA and is originally sourced from publicly available information from the Airbnb site. Description of the dataset- This dataset captures Airbnb activity in 102 columns distributed across 3 CSV files, Listings, Reviews and Calendar. Listings contains full description and review score – the major information for analysis. Reviews contains review information including unique id for each reviewer and detailed comments. Calendar contains date and price information including listing id and the price and availability for that day. Methods of data acquisition and analysis- Following are the major steps: • Data Cleaning to remove all the NULL values and preparing the data for analysis • Data Querying to understand the data • Exploratory Analysis to answer business questions Libraries: • Numpy • Pandas • Matplotlib • seaborn Development Tasks The purpose of the project is to understand vibe of Seattle neighbourhood listing descriptions, price spikes and general trend in Airbnb listings including: • Property types and their pricings. • Effect of time of the year on booking prices throughout the year. • Most expensive and least expensive neighbourhoods in the city. • Factors that affect the predicting the price of a listing.
IST652-MiniProject-1
This project deals with answering business questions pertaining to New york city AirBnB dataset.
IST625--Enterprise-Risk-Management
Risk Management Analytics:
IST659-Database-Management-and-Database-Administration
The CENT researcher centre has a lot of physical equipment in the lab. This project focuses on which of the equipment are used as part of faculty-led research projects, student-led research projects or as part of classroom activities. In every case equipment must be loaned out and kept track of so that it does not go missing i.e. Inventory management of the products.
IST687-Data-Science
The project is aimed at analyzing the data from the dataset of customers flying within Southeast airlines and to generate actionable insights by predicting customers with low satisfaction. The real goal is to reduce churn by getting ahead of the loss (of the customer) by identifying some leading indicators, or metrics, that might help keep a customer and identify factors affecting their likelihood to recommend these airlines. We also have to suggest feedback or suggestions to improve the business and help lower the customer churn for the airlines
IST719-Information-Visualization
This folder consists of the final project i.e. a poster that was created after data cleaning, data munging, data visualisations on R studio and cleaning up the plots on Adobe Illustrate.
IST652-Scripting-for-Data-Analysis
IST652, 2019 Spring sem homework and class files