🚀 Power BI Call Center Dashboard – Unlocking Insights from Data! 📊

I’m excited to share my latest Power BI project, where I built an interactive call center dashboard to analyze customer service performance and efficiency.

🔍 Key Features & Insights: ✅ Comprehensive KPIs to track total calls, call duration (hours & minutes), average call duration, and response time percentage. ✅ Visual breakdown of call distribution by day, state, channel, sentiment, and reason. ✅ Grid View Dashboard for detailed call logs, with filters for city, date, and channel, allowing easy data export. ✅ Advanced Power BI Techniques including data cleaning, modeling, DAX, time intelligence functions, and custom charts. ✅ Optimized data handling with a new Date Table in Power Query to improve time-based insights.

📈 Key Takeaways: This dashboard empowers decision-makers to monitor call center efficiency, optimize agent performance, and enhance customer experience by identifying trends and bottlenecks.

💡 Tech Stack Used: Power BI | DAX | Power Query | Data Visualization | Data Modeling

Here’s a clean, professional description you can use for your Kaggle Pokémon dataset:

📄 Dataset Description

This dataset provides detailed information on all available Pokémon, sourced directly from the PokeAPI. It includes key attributes such as:

• ID and Name
• Base experience, height, and weight
• Primary and secondary types
• Abilities
• Top 5 moves
• Base stats (e.g., HP, Attack, Defense, Speed)

The dataset is ideal for:

• 📊 Exploratory data analysis
• 🧠 Machine learning projects
• 🎮 Game design and balance modeling
• 📚 Educational or statistical learning

All data is extracted programmatically via the official PokeAPI using Python and stored in a structured MySQL table before export.

Description: This dataset contains detailed information for all Pokémon fetched from the PokeAPI, including:

Basic attributes (ID, Name, Height, Weight)

Combat stats (Attack, Defense, HP, Speed, etc.)

Types (e.g. Grass, Poison, Fire)

Abilities (e.g. Overgrow, Blaze)

Top 5 Moves

Data fetched programmatically using Python and stored in a MySQL database

This dataset is ideal for:

Data analysis

Machine learning projects

Pokémon classification models

Power BI/Tableau visualizations

Bi Power World Wide Z Limited Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

This file contains the digital vector boundaries for Lower layer Super Output Areas (LSOAs), in Birmingham for the 2021 Census geography.The boundaries available are: Generalised Clipped (BGC) - Generalised to 20m and clipped to the coastline (Mean High Water mark) and more generalised than the BFE boundaries.Lower layer Super Output AreasLower layer Super Output Areas (LSOAs) are made up of groups of Output Areas (OAs), usually four or five. They comprise between 400 and 1,200 households and have a usually resident population between 1,000 and 3,000 persons.Using Census 2021 data, some changes were made to 2011 LSOAs as a result of population and household changes since 2011. New 2021 LSOAs were created by merging or splitting 2011 LSOAs to ensure that population and household thresholds were met.Contains both Ordnance Survey and ONS Intellectual Property Rights.TopoJSON Shapefile for Power BIOn the Export tab you will find a file under the Alternative exports. This file is in TopoJSON format and is ready for use in compatible visualisation tools such as Power BI or Mapbox.