SQL Data Cleaning Repo:
This repository provides End to End projects for data learning and analysis using MYSQL.
-- Download Dataset:https://www.kaggle.com/datasets/swaptr/layoffs-2022
-- Check for Duplicates: Identify and remove any duplicate records.
-- Standardize Data and Fix Errors: Ensure data consistency and correct any errors.
-- Handle Null Values: Identify null values and determine the best approach to address them.
-- Remove Columns with Null Values: Eliminate columns that contain only null values.
-- Data downloaded from kaggle: https://www.kaggle.com/datasets/ankitbansal06/retail-orders
-- Some cleaning and transformation has been performed in python and loaded data to mysql (df.to_sql)
Data analysis part:
-- find top 10 highest reveue generating products
-- find top 5 highest selling products in each region
-- find month over month growth comparison for 2022 and 2023 sales eg : jan 2022 vs jan 2023
-- for each category which month had highest sales
-- which sub category had highest growth by profit in 2023 compare to 2022
-- Download data from: https://www.kaggle.com/datasets/shivamb/netflix-shows
-- Extract data in python and transform to MySQL
-- For data cleaning, remove duplicates, type conversions for date, populate missing values in some columns, make new tables for splited columns, and drop some columns
Data analysis part:
-- for each director count the no of movies and tv shows created by them in separate columns for directors who have created tv shows and movies both
-- which country has highest number of comedy movies
-- for each year (as per date added to netflix), which director has maximum number of movies release
-- what is average duration of movies in each genre
-- find the list of directors who have created horror and comedy movies both.
-- display director names along with number of comedy and horror movies directed by them
-- Download sql dataset from: https://www.kaggle.com/datasets/anshika2301/data-analysis-of-chocolates
Data analysis part:
-- Print details of shipments (sales) where amounts are > 2,000 and boxes are <100.
-- How many shipments (sales) each of the sales persons had in the month of January 2022?
-- Which product sells more boxes? Milk Bars or Eclairs?
-- Which product sold more boxes in the first 7 days of February 2022? Milk Bars or Eclairs?
-- Which shipments had under 100 customers & under 100 boxes? Did any of them occur on Wednesday?
-- What are the names of salespersons who had at least one shipment (sale) in the first 7 days of January 2022?
-- Which salespersons did not make any shipments in the first 7 days of January 2022?
-- How many times we shipped more than 1,000 boxes in each month of each year?
-- Did we ship at least one box of ‘After Nines’ to ‘New Zealand’ on all the months?
-- India or Australia? Who buys more chocolate boxes on a monthly basis?
-- Data set was taken from kaggle: https://www.kaggle.com/datasets/heesoo37/120-years-of-olympic-history-athletes-and-results
-- SQL queries have been implemented to break down information of different olympic games. Take a look on Athlete-Project.sql for different questions.
-- data was taken from kaggle:https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting
-- SQL queries have been implemented to break down information of different sale questions. Take a look on Walmart-Project.sql for different questions.
-- Data was taken from kaggle:https://www.kaggle.com/datasets/mcdonalds/nutrition-facts
-- SqLite3 was used to create the database
-- Connect python to database MySQL and SQLite as well as access database through Magic SQL
-- Pandas was used to read the data from the database and analyze the data to know nutrition facts of McDonald's menu
-- Dataset of Chicago Crime was downloaded and loaded to the google bigquery:https://www.kaggle.com/datasets/chicago/chicago-crime
-- Google BigQuery has been configured as SQL environment.
-- SQL queries have been implemented to break down information of different questions.