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Chetan Shekdar's Projects

-titanic-machine-learning-from-disaster icon -titanic-machine-learning-from-disaster

This is another most quoted data set in global data science community. With several tutorials and help guides, this project should give you enough kick to pursue data science deeper. With healthy mix of variables comprising categories, numbers, text, this data set has enough scope to support crazy ideas! This is a classification problem. The data has 891 rows & 12 columns.

awesome-notebooks icon awesome-notebooks

Ready to use data science templates, organized by tools to jumpstart your projects in minutes. 😎 published by the Naas community.

bigmart-sales-prediction icon bigmart-sales-prediction

Retail is another industry which extensively uses analytics to optimize business processes. Tasks like product placement, inventory management, customized offers, product bundling etc are being smartly handled using data science techniques. As the name suggests, this data comprises of transaction record of a sales store. This is a regression problem. The data has 8523 rows of 12 variables.

black-friday-data-set icon black-friday-data-set

This data set comprises of sales transactions captured at a retail store. It’s a classic data set to explore your feature engineering skills and day to day understanding from your shopping experience. It’s a regression problem. The data set has 550069 rows and 12 columns.

credit-card-default-prediction icon credit-card-default-prediction

Objective: Now a day’s the prediction of defaulting the borrower in future is a challenging task for credit card companies. Therefore the main objective of this project is to develop prediction models for defaulting the borrower in the future by taking advantage of available technological advancement.

credit_risk_model icon credit_risk_model

A comprehensive credit risk model and scorecard using data from Lending Club

datacamp-dl icon datacamp-dl

A datacamp downloader based on datacamp-downloader from TRoboto

datacamp-r icon datacamp-r

Datacamp courses and exercises for R career track

demonetization-in-india-twitter-data icon demonetization-in-india-twitter-data

Context The demonetization of ₹500 and ₹1000 banknotes was a step taken by the Government of India on 8 November 2016, ceasing the usage of all ₹500 and ₹1000 banknotes of the Mahatma Gandhi Series as a form of legal tender in India from 9 November 2016. The announcement was made by the Prime Minister of India Narendra Modi in an unscheduled live televised address to the nation at 20:15 Indian Standard Time (IST) the same day. In the announcement, Modi declared circulation of all ₹500 and ₹1000 banknotes of the Mahatma Gandhi Series as invalid and announced the issuance of new ₹500 and ₹2000 banknotes of the Mahatma Gandhi New Series in exchange for the old banknotes. Content The data contains 6000 most recent tweets on #demonetization. There are 6000 rows(one for each tweet) and 14 columns. Metadata: Text (Tweets) favorited favoriteCount replyToSN created truncated replyToSID id replyToUID statusSource screenName retweetCount isRetweet retweeted Acknowledgement The data was collected using the "twitteR" package in R using the twitter API. Past Research I have performed my own analysis on the data. I only did a sentiment analysis and formed a word cloud.

floccus icon floccus

:cloud: Sync your bookmarks across browsers via Nextcloud, WebDAV or Google Drive

harvestify icon harvestify

A machine learning based website that recommends the best crop to grow, fertilizers to use, and the diseases caught by your crops.

pandas-merge-tutorial icon pandas-merge-tutorial

A tutorial on merging and joining data frames using Python Pandas. See accommpanying blog post at www.shanelynn.ie

pandas-videos icon pandas-videos

Jupyter notebook and datasets from the pandas Q&A video series

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