Our primary objective is to find out 20 important insights from IDZ Digital Dataset. The dataset contains the details like Name, Gender, Age, Occupations, Salary, Marital Status, Children. (For exploitation, get the Dataset & Dashboard )
METHODS-
Import the data set into the Power BI tool.
Data transformation according to need.
Visualize the data and make Dashboard using diff graphs.
Prepare reports.
Here is following 20 important insights-
Insight1- We can see that Myra Grant having the highest salary, whose age is 47,occupation is Graphic Designer and who is having 2 children.
Insight2- Here we can see that in this dataset, 514 male and 486 female. Insight3- Among them 513 people are single and 487 people are married. Insight4- Out of 514 male 247 are married and 276 are single. Out of 486 females 240 are married and 246 are single.
Insight5- From the total salary male contribute 52% and female 48%. Insight6- The average salary of male is high i.e.-5.38k and female’s avg salary 5.23k. Insight7- If we breakdown the avg price of gender with respect to marital status, we can see single male earn more than married male. And female women’s salary are same with both status.
Insight8- The most no of person (35) present in the dataset their age is 55 and then followed by others. The least no of persons(16) present with age 37. Insight9- If we break down into gender, then we can see that mostly there is slightly deviation in male and female ratio. And for a few age categories like 45, there Male ratio is more. Insight10- Now If we break down into Marital Status, then we can see that mostly there is deviation in Marital Status. Like age group 18,36,45 there Married ratio is more. And Age group 39,51,60 they are single.
Insight11- Here we break down the age into 5 categories, and we can see the category-(40–50) there are most no of people. then followed by 50–60, 20–30, 30-40,Below20. Insight12- And If we break down into gender, then we can see there are slightly more male in 40–50 years categories. Insight13- And if we breakdown age categories into marital status, we can see in 40–50 yeas’s category Married people are more . And categories-(50–60,30–40,20–30) there single people are more.
Insight14- Among all the age categories below20’s income is more 5.7k. Then 20–30’s income is more which is 5.5k. Then 30–40,40–50,50–60 their income is the same, which is 5.2k. Insight15- In respect to total salary contribution, category 40–50, 20–30 and 50–60 there male contribute more i.e. 14%,13% and 12%. But in category 30–40 and Below20 there, females contribute more.
Insight16- Most no of people(29) choose Insurer as their profession. And least no of people(9) choose scientist as their profession. Insight17- Few profession there male dominant are more like Insurer, Programmer, Electrician, Engineer, Medic, Police, Composer etc. And some profession there female nomination is more like Pharmacist, Hairdresser, Social Worker, Fine Artist, Lower etc. Insight18- Few profession, their number of single people are more- like Driver, Programmer, Mathematician, Biker, Social Worker, Managers etc. Insight19- The avg salary of Mechanic is most i.e. 6.88k and following it Pilot (6.69k), Accountant (6.62k), Firefighter(6.58k) etc.
Insight20- Now if we breakdown the salary into categories, we can see most people’s salary is in between 4k-5k followed by 8k-9k, 9k-10k,2k-3k etc. Insight21- Category 4k-5k,6k-7k their male contributors are respectively more than other categories.
Insight22- The salary range in the (40–50) years category is quite interesting and distributed. In the 50–60 year’s category 34 people’s salary range 8k-9k and rest are distributed. Followed by other categories are the same.
Insight23- Most no. of people have 1 child followed by no child, 4 childs, 2 childs, 5 childs. Insight24- Single child by male is more 96. And rest of the following. Insight25- surprisingly we see in every category the single married status people are equally distributed.
Insight26- Child with 2 and 4 , there parent’s average salary is more i.e. 5.5k. Then followed by single child, then no child, 3 childs and 5 childs.
