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manasishrotri / iris.csv
Created October 10, 2020 19:23
sepal_length sepal_width petal_length petal_width species
5.1 3.5 1.4 0.2 setosa
4.9 3.0 1.4 0.2 setosa
4.7 3.2 1.3 0.2 setosa
4.6 3.1 1.5 0.2 setosa
5.0 3.6 1.4 0.2 setosa
5.4 3.9 1.7 0.4 setosa
4.6 3.4 1.4 0.3 setosa
5.0 3.4 1.5 0.2 setosa
4.4 2.9 1.4 0.2 setosa
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manasishrotri / README.md
Created October 7, 2020 01:27
titanicData
@manasishrotri
manasishrotri / README.md
Last active September 21, 2020 19:33
senateVotingData

This data file contains constituency (state-level) returns for elections to the U.S. Senate from 1976 to 2018. This data was taken from Harvard Dataverse

Questions related to dataset:

  1. Compare which party has most senates over years?
  2. Statewise comparison of total votes over years?
  3. For a state, check the distribution different party wins

Attribute Information:

@manasishrotri
manasishrotri / README.md
Last active October 18, 2020 14:55
covidWorldMayData

This is COVID Cases data in May2020. The original data was taken from Kaggle: Corona virus cases It has covid patient count, death counts reported all over the world. Fot the visualization purpose this data is filtered only for month of May

Questions related to dataset:

  1. Which countries were more affected worldwide
  2. Which days had highest count of patient deaths
  3. Display daywise spread continentwise
  4. Which countries had highest count vs highest count per million

Attributes in the data: continent,

@manasishrotri
manasishrotri / README.md
Last active September 21, 2020 19:35
bankCampaignData

The data is related with direct marketing campaigns of a Portuguese banking institution. Data was originally published by UCI MAchine Learning: Bank Campaign Data The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed.

Questions related to dataset:

  1. How many people did took the product based on the compaign
  2. On which category of people this compaign was successful? (Age, Type of job)
  3. What age group is more affected by campaign
  4. What job type people are more affected by campaign