Trying different data sets mainly about customer Experience, Segmentation and Behavior

1-Prosper Loan DataSet:

Prosper Marketplace is America's first peer-to-peer lending marketplace, with over $7 billion in funded loans, borrowers request personal loans on Prosper and investors (individual or institutional) can fund anywhere from USD 2000 to USD 40000 per loan request. Investors can consider borrowers’ credit scores, ratings, and histories and the category of the loan. Prosper handles the servicing of the loan and collects and distributes borrower payments and interest back to the loan investors. DataSet Source

This data set contains 113,937 loans with 81 variables on each loan, including loan amount, borrower rate (or interest rate), current loan status, borrower income, and many others.(Last updated 03/11/2014)

Investigating the interest rate ranges , and factors that may affect the borrower's interest rate 1 - income range and it's impact on the interest and the loan amount. 2 - Credit Score Range 3 - Loan amount

Investigating the factors affecting a loan status: 1- Prosper Score 2- Income range

2-Women Entrepreneurship and Labor Force:

The data were obtained from the Women Entrepreneurship Index and Global Entrepreneurship Index report published in 2015. The research is limited to OECD countries where all data for 2015 are available at the same time in the database. link to file from Kaggle

3- IMDB Dataset:

This was the Dataset I chose for my Udacity project submission, it has +10,000 observations of movies from IMDB

4-Seattle Airbnb Open Data:

this Kaggle Airbnb Seattle dataset include:

-Listings, including full descriptions and average review score

-Reviews, including unique id for each reviewer and detailed comments

-Calendar, including listing id and the price and availability for that day

Acknowledgement: This dataset is part of Airbnb Inside, and the original source can be found here

Inspiration: A kaggle member anlaysis about improving Customer Experience

5- IBM_HR_Analysis DataSet:

The dataset contains 1470 observation and 35 features, "the task is to Uncover the factors that lead to employee attrition and explore important questions such as ‘show me a breakdown of distance from home by job role and attrition’ or ‘compare average monthly income by education and attrition’ " as stated by dataset contributer on kaggle