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https://github.com/bala-1409/sql-projects

The repository contains Structured Query Language (SQL) Scripts. The Multiple SQL scripts for various projects which includes data cleaning, data pre-processing, data processing, data transformation and insights gaining through Query Language.
https://github.com/bala-1409/sql-projects

data-analysis data-mining data-science data-transformation database eda etl-framework exploratory-data-analysis microsoft-sql-server query-language sql sql-server sql-server-database sql-server-management-studio

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The repository contains Structured Query Language (SQL) Scripts. The Multiple SQL scripts for various projects which includes data cleaning, data pre-processing, data processing, data transformation and insights gaining through Query Language.

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# _SQL-Projects_

The repository contains Structured Query Language (SQL) Scripts. The Multiple SQL scripts for various projects which includes data cleaning, data pre-processing, data processing, data transformation and insights gaining through Query Language.

## _[Blue Dart Data Analysis](https://github.com/bala-1409/SQL-Projects/tree/main/Blue%20Dart%20Data%20Analysis)_

_The data is collected from stake holders and through their websites. The payments are in international standards (i.e.,USD) and also their ways of exports and imports through various methods of shipping and cargos._

* _Categories - The Categories dataset has Category Id, Category Name. (No. of Records - 8)_

* _Employee Details - The dataset has Employee Id, Name, Birth Date, Age, Qualification, Hire Date, photo. (No. of Records - 10)_

* _Order Details - The dataset has Order Detail Id, Order Id, Product Id, Quantity. (No. of Records - 27,665)_

* _Orders - This dataset has Order Id, Employee Id, Order Date, Shipper Id, Payment Mode. (No. of Records - 830)_

* _Products - The dataset has Product Id, Product Name, Supplier Id, Category Id, Price. (No. of Records - 77)_

* _Shippers - The dataset includes Shippers Id, Shippers Name, Email, Phone, Rates, Carrier Type, Methods, Service Coverage. (No. of Records - 3)_

* _Suppliers - Thie dataset haas Supplier Id, Supplier Name, Contact Person, Contact Email, Contact Phone, Address, City, State/Provision, Country, Postal Code, Product/Service Provided, Price List, Price, Contract Start Date, Contract End Date. (No. of Records - 28)_

## _[Loan Project](Loan%20Project)_

The data is collected by Loan Lending Institution for many individuals from multiple banks whether to sanction the loan approval or not based on their past behaviour in repayment of loans or account mainteinance in their respective banks. The data provided by our stake holder is as follows.

 * Account - The dataset has account id, district id, frequency & date. (No. of Records - 4,500) 

 * Card - The dataset has card id, disposition id, type & issued. (No. of Records - 892)

 * Client - The dataset has client id , birth number alomg with district id. (No. of Records - 5,369)

 * Disp - The dataset has disposition id, client id, account id, type. (No. of Records - 5,369)

 * District - The dataset has different factors such as A1 - A16 which includes much datas than the other datasets but not explained in a well manner. (No. of Records - 77)

 * Loan - The dataset has loan id, account id, date, amount, duartion, payment & status. (No. of Records - 682)

 * Order - The dataset has order id, account id, bank to, account to, amount, k_symbol. (No. of Records - 6,471)

 * Transaction Data - The dataset has transaction id, account id, date, type, operation, amount, balance, k_symbol, bank & account. (No. of Records - 10,56,320)

## _[Pizza Sales Analysis](Pizza%20Sales%20Analysis)_

The Data is from the Pizza Store regarding their Sales and Revenue.

 * The Sales Data is shared for the year 2015 and has 48,620 sales records.

 * The data has various factors such as pizza id, order id, pizza name id, quantity, order date, order time, unit price, total price, pizza size, pizza category, pizza ingredient and pizza name.

 * The Sales report is analysed to get further insights about sales and revenue.

 * By using SQL query commands, getting insights for the analysis report.

## _[Rafik's Kitchen Data Analysis](https://github.com/bala-1409/Rafik-s-Kitchen-Data-Analysis/tree/main/Expenses%20Report)_

_The Data is collected from Fast-food Restaurant about their Expenses in 2022._

- _The Expenses Data is collected for the year 2022 and has 601 records._

- _The data has various factors such as Date, Expenses Category, Amount, Discount, Final Amount after Discount, Paid and Carry Forward._

- _The data is analysed further to get insights about their expenses on external stores._

- _By using, Structured Query Language (SQL) query commands, getting insights for the Expenses Report._