amansingh1912 / d-challenger_nics_firearm-challenge Goto Github PK

I have been a part of the NICS Firearm Challenge for the month of August.

• I was provided one excel file which contained 3 datasets: NICS-Firearm-Background-Checks, Explanation Sheet
• The sheet "NICS-Firearm-Background-Checks" contains information about month, handgun, long gun and information about prepawn, redemption, return to seller, returned guns and private sale guns.
• While the "Explanation Sheet" contains information about the different handgun, long gun, prepawn firearms and redeemed firearms.

Although, in this challenge objective was not defined. So, I came up with my own objectives:

• Comparing trend of Number of Redemption and Number of Pre-pawn Guns By Year.
• Comparing trend of Number of Return to Seller Guns and Sum of Returned Guns By Year.
• Top States by Permits they received
• Top States By number of guns.

After loading the dataset in Power BI, I applied following operations:

• First of all, I started with the "NICS-Firearm-Background-Checks" sheet where guns are in different columns i.e. of types long guns, hand guns, other, etc. and same goes for prepawn and redempted guns too.

• Now, I applied simple arithmetic calculation operation i.e of DAX in Power BI, where I put several columns like Total Guns, Total Number of Redemption Guns , Total Number of Prepawn Guns, Total Number of Return to Seller Guns, Total Number of Private Sale Guns and Total Number of Returned Guns.

• Then, I used DAX function "SUM" to calcuate SUM of all those parameters/columns which are newly made.

• I chose the color palette i.e. Brown, Orange, Green, Dark Blue and white for outline.
• From the symmetrical point of view, I took 2 columns side by side where on one side I used 6 cards whereas on the second side of column I used 4 charts of which 2 were line charts and 2 of them are Bar charts.
• Imported icons from Logomakr.com website and used them on the top of columns used in visualization.

• Texas has most number of guns.
• Washington has most number of permits.

Overall, it was a nice dataset to work around with and I loved to visualize the dataset and come with the insights from it.