Project 2 - Supply and Demand Gap Analysis
Aim:-
This project aims to collect and analyze the data of Food prize with food rate analytics site data and analyze it detail by each cuisine, review, flower, time, cost, link, collection etc on the site
Introduction
The main objective of this project is to analyze the data on Food prices with cousins.
Through the Analysis of data, we can find out some important insights
Problem Statement
We can find out how may cousins
Total pricing by cousins and restart
No. of Review and Flowers cousins and restaurant
Methodology
Step 1: Data cleaning
In data cleaning I cleaned the data with the transform data option present in Power BI because the dataset was not cleaned and included some missing values, so with the help of the duplicates function in Power BI, I cleaned the data and replace the null values with 0 and then started working on it.
Step 2: Data interpretation
I found some important information about the data set in this data interpretation step.
- The data set was almost clear and completed.
- I replaced the null values with 0.
- There is the first total of 1000 rows and 7 columns.
- There is the first total of 106 rows and 6 columns.
Step 3: Visualization
In visualization, I took the help of Power BI Desktop software to make graphs and charts here some relevant graphs and charts are attached.
Analysis (Data sheets pertaining to it):-
In observation found some interesting points that are mentioned below.
Sum of cost by restaurant and Cuisines
Top 10 Sum of Rating by Restaurant
Top 10 sum of rating by cuisines
Top 10 Sum of cost by cuisines and restaurant
Maximum cost 3K, rating 5 and followers 13K
Top 10 sum of rating by cuisines
Restaurant with the sum of pictures high to low
Top 10 sum of reviews and sum of followers by restaurant filter in Review
Top 10 sum of reviews and sum of followers by restaurant filter in Followers
Top 5 sum of rating by restaurant and sum of cost by restaurant and cuisines
0 to 20 parameter filter in Restaurant with a sum of pictures
Top 10 Rating and Cost button shows the visual
Sum of Reviews by name
Insights:-
- In conclusion, we can take a look at the final Report for further analysis.
- We can see the values according to gender.
Recommendations:-
- We can collect a large dataset for analysis.
- We can analyze the data of the trip destination.
- Like this dataset we can perform operations with various types of cabs, gender, or destination type.
- We can work on large datasets and analyze them with the proper format of the chart.
Conclusion:-
In conclusion, we can take a look at the final Report for further analysis.
As we can see in the Report, The highest value of the pricing type, etc. and we can see the maximum value is, 81367 of the pricing type of females.
Reference:-
Kaggle.com :https://www.kaggle.com/datasets/batjoker/zomato-restaurants-hyderabad, Google.com
PowerBIDesktop.com, Wikipedia.com