This repository contains my personal projects exploring data analysis and insights generation using Structured Query Language (SQL). Each project is based on real-world datasets sourced from Kaggle and aims to showcase how SQL can be used to clean, transform, and analyze data effectively.
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Data Cleaning: Handling missing values, duplicates, and inconsistent formats.
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Data Transformation: Using joins, subqueries, CASE statements, and CTEs to organize data.
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Exploratory Data Analysis (EDA): Finding trends, distributions, and performance indicators.
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Business Insights: Translating numbers into actionable insights for decision-making.
This is the database engine, it’s where all your tables and data are stored.
What it does:
- Stores and manages data tables.
- Runs SQL queries.
- Handles relationships between datasets.
Now, this is the interface, the program where you actually type your SQL code.
What it does:
- Lets you type, run, and visualize SQL queries.
- Displays all your databases, tables, and relationships.
- Helps you create backups, import CSVs, and view results easily.
Description
The Customer Sentiment Dataset contains customer demographics, reviews, ratings, product categories, and issue resolution information.
This makes it ideal for practicing real-world SQL queries that answer business questions.
Description
Segmented customers using distribution-based recency and analyzed revenue, demographics, and payment behavior.
Description
In this portfolio, I am using JOINS, CTE, UNION, SUBQUERY, ETC to their maximum potential, since this dataset has 10 tables with one primary key, which is USER_ID.