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DATA ANALYSIS

Greetings! This repository is dedicated for data analysis projects which help me to expand my career as a data analyst or also a data scientist.

Tools used:

  • SQL
  • Python
  • Power BI

*These projects are based on datasets of real data from the web.

These datasets are available in kaggle.com

Data analysis and Visualization

Python

SQL

Power BI




Credit Cards Customers

Description:Information about a bank's customers with details like age, marital status, educational level etc -> https://www.kaggle.com/datasets/sakshigoyal7/credit-card-customers

Goal: Discover the possible causes of bank customers leaving the service to better understanding their mistakes and improve their customers retention.

Techniques:

  • Grouping
  • Visualization



London houses

Description:Information about the properties in London with their respectives details like address, price etc. -> https://www.kaggle.com/datasets/oktayrdeki/houses-in-london

Goal: Analyze the different London properties and discover if there is an important factor that justify their price.

This is a good insight for local people and tourists who want to buy or rent a property in London.

Techniques: -Grouping -Filtering -Visualization

Uber analytics

Description : Information about all the ride_sharing data performed by Uber vehicles during 2024 -> https://www.kaggle.com/datasets/yashdevladdha/uber-ride-analytics-dashboard

Goal: Create a general idea of the kind of vehicles in performance and the operations made with clients and payments methods.This analysis can be useful to companies who want to discover their assets`s moves and how these can influence in their business.

Techniques: -Grouping -Filtering -Visualization


Shopping trends

Description:Information about customers registered in USA with details like age, gender, marital status etc. -> https://www.kaggle.com/datasets/tanishqqqq/shopping-trends

Goal: Summarize all details about customers grouping them by age, gender and state to discover the relationship between these characteristics and their buying habits and product preferences.

This information can be important for shops who want to increase their sales by segmentating their market for specific customers.

Techniques:

  • Grouping
  • Summarize
  • Sorting



Metacritic analysis

Description:Descriptive analysis about all the videogames review by Metacritic: a company who reviews videogames for all plattforms like PC, Playstation4 etc. -> https://www.kaggle.com/datasets/brunovr/metacritic-videogames-data

Goal:Focusing on the videogames with the best critics to be informed about the possible profits of their creators and the influence they generated over the this century and the previous one.

Techniques:

  • Grouping
  • Sorting
  • Subqueries



Netflix titles

Description: Information about the different netflix titles either shows or movies with their respectives casting, duration or seasons count etc. -> https://www.kaggle.com/datasets/shivamb/netflix-shows

Goal: Discover the most popular netflix titles to discover if there is a preffered gender or people tend to enjoy these titles because of the actor involved

Techniques:

  • Grouping
  • Sorting
  • Counting
  • Subqueries



Balaji Fast Foods

Description: Information about the different restaurants in Spain with their price, type of food etc. -> https://www.kaggle.com/datasets/rajatsurana979/fast-food-sales-report

Goal: Create a report about the different results retrieved by the different dishes and the profits.

Techniques:

  • Dashboard
  • Filtering
  • Counting



Ecommerce analysis

Description: Information about all the ecommerce transactions retrieved from UK retailers from 2010 and 2011, containing details like quantities, prices, profits generated etc -> https://www.kaggle.com/datasets/gabrielramos87/an-online-shop-business

Goal: Create a report about the results about the profits generated by the ecommerce activity in London

Techniques:

  • Dashboard
  • Filtering
  • Counting

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