Project 1

create schema ecommerce;

use ecommerce;

'right click on ecommerce schema -> Table Data import Wizard -> Give path of the file -> Next -> choose options :

Create a new table , select delete if exist -> next -> next'

use ecommerce;

describe users_data;

select * from users_data limit 100;

use ecommerce;

select count(country) country, count(language) language from users_data;

select sum(socialNbFollowers) ,gender from users_data group by gender;

select count(*) ProfilePicture from users_data where hasProfilePicture = 'true';

select count(*) Application from users_data where hasAnyApp = 'true';

select count(*) Androidapp from users_data where hasAndroidApp = 'true';

select count(*) iosapp from users_data where hasIosApp = 'true';

select count(productsBought) ProductsBought ,country from users_data group by country order by

count(productsBought) desc;

select count(productsSold) ProductsSold ,country from users_data group by country order by count(productsSold) desc;

select country ,max(productsPassRate) ProductsPassRate from users_data group by country order by

max(productsPassRate) desc limit 10 ;

select count(*) language from users_data group by language ;

select sum(socialProductsLiked), sum(productsWished) from users_data where gender = 'F';

select count(productssold), count(productsbought) from users_data where gender = 'M';

select count(productsbought) from users_data group by country order by count(productsbought) desc;

select country, productssold from users_data where productssold = '0' limit 10;

select * from users_data order by daysSinceLastLogin limit 110;

select gender ,count(daysSinceLastLogin) from users_data where gender = 'F' and daysSinceLastLogin>100;

select count(*)female from users_data where gender= 'F' order by country ;

select count(*)male from users_data where gender= 'M' order by country ;

select avg(productssold), avg(productsbought) from users_data where gender = 'M';