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Indian-Startup-Funding

Indian-Startup-Funding

active ARFF CC0: Public Domain Visibility: public Uploaded 23-03-2022 by Stewart
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Context Interested in the Indian startup ecosystem just like me Wanted to know what type of startups are getting funded in the last few years Wanted to know who are the important investors Wanted to know the hot fields that get a lot of funding these days This dataset is a chance to explore the Indian start up scene. Deep dive into funding data and derive insights into the future Content This dataset has funding information of the Indian startups from January 2015 to August 2017. It includes columns with the date funded, the city the startup is based out of, the names of the funders, and the amount invested (in USD). For more information on the values of individual fields, check out the Column Metadata. Acknowledgements Thanks to trak.in who are generous enough to share the data publicly for free. Inspiration Possible questions which could be answered are: How does the funding ecosystem change with time Do cities play a major role in funding Which industries are favored by investors for funding Who are the important investors in the Indian Ecosystem How much funds does startups generally get in India

10 features

Sr_Nonumeric3044 unique values
0 missing
Date_dd/mm/yyyystring1035 unique values
0 missing
Startup_Namestring2459 unique values
0 missing
Industry_Verticalstring821 unique values
171 missing
SubVerticalstring1942 unique values
936 missing
City__Locationstring112 unique values
180 missing
Investors_Namestring2412 unique values
24 missing
InvestmentnTypestring55 unique values
4 missing
Amount_in_USDstring471 unique values
960 missing
Remarksstring72 unique values
2625 missing

19 properties

3044
Number of instances (rows) of the dataset.
10
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
4900
Number of missing values in the dataset.
3044
Number of instances with at least one value missing.
1
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of nominal attributes.
Average class difference between consecutive instances.
10
Percentage of numeric attributes.
16.1
Percentage of missing values.
100
Percentage of instances having missing values.
0
Percentage of binary attributes.
0
Number of binary attributes.
Number of instances belonging to the least frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the most frequent class.
0
Number of attributes divided by the number of instances.

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