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231 changes: 87 additions & 144 deletions Shark Tank Analysis Project/SQL Project on Shark Tank India.sql
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Original file line number Diff line number Diff line change
@@ -1,144 +1,87 @@
select * from [Shark tank project]..data

-- total episodes

select max( [Ep# No#]) from [Shark tank project]..data
select count(Distinct [Ep# No#]) from [Shark tank project]..data

-- pitches

select count(Distinct [Brand]) from [Shark tank project]..data

--- pitches converted

select cast(sum(a.converted_not_converted) as float) /cast(count(*) as float) from (
select [Amount Invested lakhs] ,case when [Amount Invested lakhs] >0 then 1 else 0 end as converted_not_converted from [Shark tank project]..data) a


--- total male

select sum(male) from [Shark tank project]..data

--- total female

select sum(female) from [Shark tank project]..data


--- gender ratio
select sum(female)/sum(male) from [Shark tank project]..data

--- total invested amount

select sum([Amount Invested lakhs])from [Shark tank project]..data


-- avg equity taken

select avg(a.[Equity Taken %]) from
(select * from [Shark tank project]..data where equitytakenp>0) a



--- highest deal taken


select max([amount invested lakhs]) from [Shark tank project]..data


--higheest equity taken

select max([Equity Taken %]) from [Shark tank project]..data


-- startups having at least women

select sum(a.female_count) startups having at least women from (
select female,case when female>0 then 1 else 0 end as female_count from [Shark tank project]..data) a


-- pitches converted having atleast One women

select * from [Shark tank project]..data

select sum(b.female_count) from(

select case when a.female>0 then 1 else 0 end as female_count ,a.*from (
(select * from [Shark tank project]..data where deal!='No Deal')) a)b


select avg([Team members]) from [shark tank project]..data


--- amount invested per deal

select avg(a.[amount invested lakhs]) amount_invested_per_Deal from
(select * from [Shark tank project]..data where deal!='No Deal')a


-- avg age group of contestants

select avg age,count(avg age) cnt from [Shark tank project]..data group by avg age order by cnt desc


-- location group of contestants

select location,count(location) cnt from [Shark tank project]..data group by location order by cnt desc



-- sector group of contestants

select sector,count(sector) cnt from [Shark tank project]..data group by sector order by cnt desc


--partner deals

select partners,count(partners) cnt from [Shark tank project]..data where partners!='-' group by partners order by cnt desc


-- making the matrix


select * from [Shark tank project]..data

select 'Ashnner' as keyy,count([Ashneer Amount Invested]) from [Shark tank project]..data where [Ashneer Amount Invested] is not null


select 'Ashnner' as keyy,count([ashneer amount invested]) from [Shark tank project]..data where [Ashneer Amount Invested] is not null AND [Ashneer Amount Invested]!=0


SELECT 'Ashneer' as keyy,SUM(C.[ashneer amount invested]),AVG(C.[Aman Equity Taken %])
FROM (SELECT * FROM [Shark tank project]..DATA WHERE [Ashneer Equity Taken %]!=0 AND [Ashneer Equity Taken %] IS NOT NULL) C

select m.keyy,m.total_deals_present,m.total_deals,n.total_amount_invested,n.avg_equity_taken from

(select a.keyy,a.total_deals_present,b.total_deals from(

select 'Ashneer' as keyy,count([Ashneer Amount Invested]) total_deals_present from [Shark tank project]..data where [Ashneer Amount Invested] is not null) a

inner join(
select 'Ashneer' as keyy,count([ashneer amount invested]) total_deals from [Shark tank project]..data
where [Ashneer Amount Invested] is not null AND [Ashneer Amount Invested]!=0)b

on a.keyy=b.keyy) m

inner join

(SELECT 'Ashneer' as keyy,SUM(C.[ashneer amount invested]) total_amount_invested
,AVG(C.[Aman Equity Taken %]) avg_equity_taken
FROM (SELECT * FROM [Shark tank project]..DATA WHERE [Ashneer Equity Taken %]!=0 AND [Ashneer Equity Taken %] IS NOT NULL) C)n

on m.keyy=n.keyy


-- which is the startup in which the highest amount has been invested in each domain/sector




select c.* from
(select brand,sector,[amount invested lakhs],rank() over(partition by sector order by [amount invested lakhs] desc) rnk

from [Shark tank project]..data) c

where c.rnk=1
-- Total episodes
SELECT COUNT(DISTINCT [Ep# No#]) AS total_episodes
FROM [Shark tank project]..data;

-- Pitches
SELECT COUNT(DISTINCT [Brand]) AS total_pitches
FROM [Shark tank project]..data;

-- Pitches converted
SELECT CAST(SUM(CASE WHEN [Amount Invested lakhs] > 0 THEN 1 ELSE 0 END) AS FLOAT) / CAST(COUNT(*) AS FLOAT) AS conversion_rate
FROM [Shark tank project]..data;

-- Total male and female contestants
SELECT SUM(male) AS total_male, SUM(female) AS total_female
FROM [Shark tank project]..data;

-- Gender ratio
SELECT SUM(female) / SUM(male) AS gender_ratio
FROM [Shark tank project]..data;

-- Total invested amount
SELECT SUM([Amount Invested lakhs]) AS total_invested_amount
FROM [Shark tank project]..data;

-- Average equity taken (%)
SELECT AVG([Equity Taken %]) AS avg_equity_taken
FROM [Shark tank project]..data
WHERE [Equity Taken %] > 0;

-- Highest deal amount
SELECT MAX([Amount Invested lakhs]) AS highest_deal_amount
FROM [Shark tank project]..data;

-- Highest equity taken (%)
SELECT MAX([Equity Taken %]) AS highest_equity_taken
FROM [Shark tank project]..data;

-- Startups with at least one woman
SELECT SUM(CASE WHEN female > 0 THEN 1 ELSE 0 END) AS startups_with_women
FROM [Shark tank project]..data;

-- Pitches converted with at least one woman
SELECT SUM(CASE WHEN female > 0 AND deal != 'No Deal' THEN 1 ELSE 0 END) AS pitches_with_women
FROM [Shark tank project]..data;

-- Average team members
SELECT AVG([Team members]) AS avg_team_members
FROM [Shark tank project]..data;

-- Average amount invested per deal
SELECT AVG([Amount Invested lakhs]) AS avg_amount_invested_per_deal
FROM [Shark tank project]..data
WHERE deal != 'No Deal';

-- Average age group of contestants
SELECT AVG([avg age]) AS avg_age, COUNT(*) AS cnt
FROM [Shark tank project]..data
GROUP BY [avg age]
ORDER BY cnt DESC;

-- Location group of contestants
SELECT location, COUNT(location) AS cnt
FROM [Shark tank project]..data
GROUP BY location
ORDER BY cnt DESC;

-- Sector group of contestants
SELECT sector, COUNT(sector) AS cnt
FROM [Shark tank project]..data
GROUP BY sector
ORDER BY cnt DESC;

-- Partner deals
SELECT partners, COUNT(partners) AS cnt
FROM [Shark tank project]..data
WHERE partners != '-'
GROUP BY partners
ORDER BY cnt DESC;

-- Highest amount invested per sector
SELECT brand, sector, [amount invested lakhs]
FROM (
SELECT brand, sector, [amount invested lakhs],
RANK() OVER (PARTITION BY sector ORDER BY [amount invested lakhs] DESC) AS rnk
FROM [Shark tank project]..data
) AS ranked_data
WHERE rnk = 1;