cnduc Goto Github PK

bank-s-loan-division

betabank

codejam

crankshaftlist

datascience

eda-markdown-documentation

filmyjunk

goldextractedfromore

identifying-zipcodes

Identify Zip Codes within a 5, 10, 15, etc. Radius of Locations

malaria-detection

Detecting Malaria using Deep Learning 🦟🦠

mobilecarriermegaline

oilygiantcompany

onlinestoreice

predict-if-a-bank-customer-leaves-soon

prepare4vc

report-for-bank-s-loan-division

Your project is to prepare a report for a bank’s loan division. You’ll need to find out if a customer’s marital status and number of children has an impact on whether they will default on a loan. The bank already has some data on customers’ credit worthiness. Your report will be considered when building a credit scoring of a potential customer. A credit scoring is used to evaluate the ability of a potential borrower to repay their loan.

rustybargain

sindhu-portfolio

sindhu.github.io

sindhusirigireddy.github.io

Portfolio of Data Analysis & Data Science Projects

suretomorrowinsurance

sweetlifttaxicompany

telecom-operator-megaline

You work as an analyst for the telecom operator Megaline. The company offers its clients two prepaid plans, Surf and Ultimate. The commercial department wants to know which of the plans is more profitable in order to adjust the advertising budget. You are going to carry out a preliminary analysis of the plans based on a relatively small client selection. You'll have the data on 500 Megaline clients: who the clients are, where they're from, which plan they use, and the number of calls they made and text messages they sent in 2018. Your job is to analyze clients' behavior and determine which prepaid plan is more profitable.

tsf-tasks

video-games-sales-all-over-the-world.

You work for the online store Ice, which sells video games all over the world. User and expert reviews, genres, platforms (e.g. Xbox or PlayStation), and historical data on game sales are available from open sources. You need to identify patterns that determine whether a game succeeds or not. This will allow you to spot potential big winners and plan advertising campaigns. In front of you is data going back to 2016. Let’s imagine that it’s December 2016 and you’re planning a campaign for 2017. (The important thing is to get experience working with data. It doesn't really matter whether you're forecasting 2017 sales based on data from 2016 or 2027 sales based on data from 2026.) The dataset contains the abbreviation ESRB. The Entertainment Software Rating Board evaluates a game's content and assigns an age rating such as Teen or Mature.