This repository contains the steps and information related to my Power BI project, where I used CSV data to create insightful visualizations. In this README, I'll provide an overview of the project and the key steps involved.

• Project Overview
• Getting Started
• Data Sources
• Data Cleaning
• Data Modeling
• Visualization
• Custom Columns
• Dashboard Creation

The first step in this project was to connect to the database, which can be in various forms such as S Excel sheets, web, or text. For this project, I used CSV data. The essential steps in this process include:

1. Connecting to the Database: I connected to the CSV data source to access the necessary data.

2. Analyzing Tables and Relations: Understanding the structure of the data and defining relationships between tables.

3. Data Cleaning: I used Power Query Editor to clean and transform the data.

The project uses CSV data as its primary data source.

Data cleaning was performed in Power Query Editor. It involved tasks such as removing duplicates, handling missing values, and formatting data to ensure it's ready for analysis.

In this phase, I worked on data modeling and creating relationships between tables. I used Power Query to define custom columns and intermediate values.

Once the data was prepared and relationships were established, I used Power BI's visualization capabilities to create informative charts and graphs. Some key points about visualization:

• Top Five Products: I created visualizations to highlight the top five products based on specific criteria.

• User Interface and Performance: The project also focused on enhancing the user interface and optimizing performance for a seamless experience.

One notable task in data modeling was creating custom columns. An example of this is the "Ship or Order Difference" column, which calculates the difference between the ship date and order date. This custom column helped in analyzing order fulfillment timelines.

The final step of the project was creating interactive dashboards. These dashboards allow users to explore the data visually and gain insights. To make the dashboards more appealing:

• I added an image as a background.
• Adjusted transparency and formatting for a polished look.

Feel free to explore the project files and visualizations for more details.