Project Motivation
- This is an Udacity Nanodegree project.I was interested in using Prosper Loan Data to better understand :
Project Overview
This project has two parts that demonstrate the importance and value of data visualization techniques in the data analysis process. In the first part, you will use Python visualization libraries to systematically explore a selected dataset, starting from plots of single variables and building up to plots of multiple variables. In the second part, you will produce a short presentation that illustrates interesting properties, trends, and relationships that you discovered in your selected dataset. The primary method of conveying your findings will be through transforming your exploratory visualizations from the first part into polished, explanatory visualizations.
Project Details
This project is broken into two halves. In the first section, you will do exploratory data analysis on a dataset of your choice. To investigate the dataset's variables and comprehend the data's structure, anomalies, patterns, and relationships, you will utilize Python data science and data visualization modules. This section's analysis should be systematic, progressing from basic univariate relationships to multivariate relationships, although it does not have to be clean or faultless. There is no one solution that must emerge from a particular dataset. This section of the project allows you to ask questions about the data and create your own findings. Project Details comes to an end, and it may take several steps to get to what you're really searching for.
Step 1.1: Choose your Dataset:
Loan Data from Prosper with Prosper Data Dictionary to Explain Dataset's Variables
Dataset
This data set contains information on peer to peer loans facilitated by credit company Prosper. There are 113,937 loans with 81 variables. For the purpose of this investigation I've taken the following variables: Term, LoanStatus, BorrowerRate, ProsperRating (Alpha), ListingCategory (numeric), EmploymentStatus, DelinquenciesLast7Years, StatedMonthlyIncome, TotalProsperLoans, LoanOriginalAmount, LoanOriginationDate, Recommendations and Investors.
Step 1.2: Explore Your Data
It's time to get to the good stuff. Investigate your data and record your results in a report. The report should begin by briefly introducing the dataset and then on to walk through the points of exploration that you did. Headers and text should be used to arrange your thoughts and discoveries. Visualizations in this section of the project do not need to be perfect: this is merely your personal experimentation at this time. However, you must still follow the concepts of utilizing proper plot types and encodings so that reliable conclusions can be formed, and you must include enough comments and labeling so that when you return to your work, you can easily comprehend your analytic methods.
step 2.1: Write down your story
You've undoubtedly discovered a lot of items at the conclusion of your exploration. It is now time to compile your results and choose a tale to tell others. You should describe your primary discoveries and remark on the steps you followed in your data investigation in your readme document. You should also outline the important insights you want to express in your explanatory report, as well as any updates to visualizations or new visualizations that will be generated to connect your insights.
Step 2.2: Make a Slide Deck
Following the goals you outlined in the previous phase, develop a slide presentation using explanatory data visualizations to convey a story about the data you investigated. You can start with the code you used in your investigation, but make sure it is altered so that your plots are refined. In your modifications, be sure to keep features of design integrity in mind.
Step 2.3: Review and Submit the Project
Result :
- Business and home improvement don't have nearly equivalent means at all, with the exception of auto.
- Business-related categories typically have more
