# Tableau Projects
Project 1 : Chocolate Brand Popularity Data Viz
Link to the Dashboard: https://t.co/UkurrESp8Q
Conclusions:
1. even though Twirl's brand ranking among various considered age groups isn't the highest it wins the most popular brand type
2. Dairy milk is very popular with the ages 65+ which states that the older generation enjoys soft and less fancy chocolates.
3. Crunchie could make some changes in representing their brand, for example: packaging with more panache since the 18-24 ages would be quickly drawn in
4. KitKat is going very well with their 'have a break' campaign which resonates with their brand ranking.
#Power BI Projects
Project 1 : Chocolate Company Sales and Trends
Link to the Dashboard: https://bit.ly/3TTcNUU
Description: I became familiar with Power BI and its various tools and workflow. I took demo data from an excel file. Cleaned the data and visualised the data with respect to salespersons, teams, and boxes sold in a region, by category, and by the amount per box, also predicted the future sales trends using the Power BI analysis tool which is based on pure statistics.
Project 2 : Creating a Waterfall Pipeline (Workout Wednesday Challenge Week 38 '22)
Link to the Dashboard: https://bit.ly/3gPp4uH
Project 3 : Custom Page Navigation and Report for Food Waste (Workout Wednesday Challenge Week 2 '22)
Link to the Dashboard : https://bit.ly/3gNMgJQ
Description: I learnt custom navigation using Power BI. This report gives us a summary of food wastage from all aspects from its generation, cause, how to monitor the data using interactive dashboard and its possible prevention solutions. Demo data used from https://refed.org/
Project 4 : NCAA Revenue Report using Drill through fields (Workout Wednesday Challenge Week 1,2,3,4 2021)
Link to the Dashboard : https://bit.ly/3sEH1yJ
Description: I learnt Drill through fields in Power BI.
Data set that breaks down NCAA athletic department expenses and revenues by year taken from https://data.world/jbaucke/2021-w1-power-bi-wow-ncaa-financials.
Analysed the data and answered various questions which include team that caused the most loss in a particular year and the cause for the loss using the interactive dashboard
Project 5 : Cat or Croissant Game - DAX (Workout Wednesday Challenge Week 18)
Link to Dashboard : https://bit.ly/3Dt4APV
Project 6 : Error Bars (WoW Challenge week 28)
Link to Dashboard : http://bit.ly/3G5l6ZC
Project 7 : Connected Scatterplot for fuel price and travel in the US (WoW challenge week 31)
Link to Dashboard : http://bit.ly/3A5VQ1y
Description: I learnt and used plotlyJS to make this visualisation. The dataset used is from http://bit.ly/3GthwZy.
A connected scatterplot is a combination of a line plot and a scatterplot. They help us summarize trends and label them as required.
Project 8: Hotel Revenue, trends analysis {SQL +Power BI}
Link to Dashboard : http://bit.ly/3O5WLoy
Description: I used a demo excel file which contains data on two types of hotels. I used SQL commands to import the data into Power BI. I used SQL queries like SELECT, LEFT JOIN, and GROUP BY for this project.
I focused on three main questions for arriving at my dashboard:
- is the hotel revenue growing by year?
→ we have two types of hotel types so it would be good to segment revenue by hotel type and analyse - should we increase our parking lot size?
→ the parking percentage is roughly the same in 2018 and 2019 and hence it is difficult to determine if increasing parking space would be a good option or not. - Explore trends in data and display facts and figures
→ interactive dashboard
Project 9: Visualisation of Diamond price prediction using K Neighbours Regression model. {Python (ML)+ Power BI}
Link to Dashboard: http://bit.ly/3XL2VyO
Description: used the diamond price dataset to predict price of diamonds using various regression models. Best results were obtained from KNN Regression model.
Integrated the python script with Power BI to visualise the gap between actual price and predicted price taking into consideration all the mentioned parameters like cut, colour, clarity. Prediction error can also be adjusted in range from 0-5000.
Project 10: Calculation Groups (WoW Challenge week 34 2022)
Link to Dashboard: https://bit.ly/wow34_22
Description: used the extrenal tool Tabular Editor (https://github.com/TabularEditor/TabularEditor/releases/tag/2.17.1) to make Month-To-Date, Quarter-To-Date, and Year-To-Date view across Revenue, Orders, and Units. Calculation groups allow defining of each of the date views for whichever measure is being used in the Power BI view, providing a much simpler solution.