├── Clean Zamoto Data.csv ├── README.md ├── Zamoto Analysis.py ├── Zamoto raw data ├── Country-Code.xlsx └── zomato.csv └── Zomato Dashboard.pbix /README.md: -------------------------------------------------------------------------------- 1 | # Analysis-Zomato-data-with-python and Visualization with Power-Bi 2 | ![image](https://github.com/ritikaga/Data-Analysis-of-Zomato-Restaurant/assets/66274316/c358d249-02e2-4bc2-932b-387fdf56d469) 3 | 4 | ## Project Objective 5 | In this project, we investigate a dataset that carries approximate facts about restaurant chains all over the world. The project's main aim is to perform descriptive and statistical analysis based on a few goals designed. 6 | 7 | ## Dataset 8 | The dataset provides information like an average cost for two, location, votes, the aggregate rating, cuisines, country, rating text, etc. Various analysis like the top cuisines, most expensive restaurants, average rating for each country, and many other analysis are performed on the dataset. 9 | The dataset's owner is Zomato and Kaggle is the source providing the data. 10 | 11 | 12 | ## Zamoto Analysis 13 | * Load the Raw Data. 14 | * Data cleaning is performed on Python libraries Numpy, and pandas. 15 | * Made sure data is Dropping unnecessary columns, Dropping duplicate rows, and Cleaning individual rows. 16 | * Cleaned the data, and it is ready for developing the Interactive Dashboard. 17 | 18 | ## Questions 19 | The analysis that we are going to perform shall answer the following questions: 20 | 21 | * Total restaurants and total cuisines all over the world? 22 | * Which countries have the greatest number of restaurants enrolled in Zomato? 23 | * Which cities in India have the greatest number of "value for money restaurants? 24 | * What are the top 10 cuisines that have the highest number of votes in India.? 25 | * Which countries have restaurants that deliver online? 26 | * In terms of the number of restaurants, which locality has the most? 27 | * Which Restaurants have Good Cuisine and an average rating? 28 | 29 | ## Dashboard 30 | Dashboard snapshort 31 | 32 | 33 | ## Conclusion 34 | * The dataset shows the restaurants with the highest cost for two, countries with a greater number of restaurants on Zomato, countries with 35 | restaurants that offer online delivery, average aggregate rating for countries, etc. 36 | * Based on our analysis performed, the restaurants present in different localities can improve the quality of the restaurant, deliver online,work on days that have an average aggregate rating, 37 | and check for value-added restaurants to check on how they work and can implement a few techniques like including both online delivery and table booking, providing various cuisines to 38 | visitors, etc. 39 | * It is easy for frequent visitor's filter based on the average cost for two, votes, the aggregate rating, cuisines, etc. This can be done before 40 | visiting the restaurant 41 | -------------------------------------------------------------------------------- /Zamoto Analysis.py: -------------------------------------------------------------------------------- 1 | # Zamoto Analysis 2 | 3 | # Import Libaries 4 | import pandas as pd 5 | import numpy as np 6 | 7 | # Loading .csv file of zomato data using pandas 8 | df_data = pd.read_csv(r"C:\Users\ritik\OneDrive\Documents\project data analysis\Power bi project\Zamoto project\Zamoto raw data\zomato.csv", encoding='unicode_escape') 9 | print(df_data) 10 | 11 | 12 | # Loading excel file of country code using pandas 13 | 14 | df_country = pd.read_excel(r"C:\Users\ritik\OneDrive\Documents\project data analysis\Power bi project\Zamoto project\Zamoto raw data\Country-Code.xlsx") 15 | print(df_country) 16 | 17 | 18 | # Data Cleaning (Zamaoto data) 19 | #check the datatype 20 | df_data.info() 21 | 22 | # Check the Null Values 23 | print(df_data.isnull().sum()) 24 | 25 | # the top 5 rows of sales data 26 | print(df_data.head(10)) 27 | 28 | # describe return description of the data in the DataFrame 29 | print(df_data.describe()) 30 | 31 | # Check Duplicates 32 | print(df_data.duplicated().sum()) 33 | 34 | # Columns Names 35 | print(df_data.columns) 36 | 37 | #check the datatype 38 | print(df_data.dtypes) 39 | 40 | print(df_data.shape) 41 | 42 | 43 | # now clean the Country Code Data 44 | 45 | # view data of country code 46 | print(df_country.head()) 47 | 48 | print(df_country.info) 49 | 50 | print(df_country.shape) 51 | 52 | #checking null values 53 | print(df_country.isnull().sum()) 54 | 55 | # checking Duplicates 56 | df_country.duplicated().sum() 57 | 58 | 59 | # Merging country code and Data file) 60 | df=pd.merge(df_data,df_country,on='Country Code', how= 'left') 61 | print(df.head()) 62 | 63 | 64 | #droping unwanted columns 65 | df.drop(['Restaurant ID','Locality Verbose','Country Code','Longitude','Latitude'],axis=1,inplace=True) 66 | print(df.columns) 67 | 68 | # checking the null values after merging 69 | df.isnull().sum() 70 | 71 | # remove null values 72 | df=df.dropna() 73 | print(df.isnull().sum()) 74 | 75 | # count total transcation happen in all the world 76 | print(df.Country.value_counts()) 77 | 78 | 79 | #downlaod data 80 | #df.to_csv(r"C:\Users\ritik\OneDrive\Documents\project data analysis\Power bi project\Zamoto project\zomato.csv") 81 | print(df.to_string) -------------------------------------------------------------------------------- /Zamoto raw data/Country-Code.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ritikaga/Zomato-Analysis-with-Python-and-visualization-with-Power-BI/f35c2f36e581d444f1e7bedcf35e214a5401f8a1/Zamoto raw data/Country-Code.xlsx -------------------------------------------------------------------------------- /Zamoto raw data/zomato.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ritikaga/Zomato-Analysis-with-Python-and-visualization-with-Power-BI/f35c2f36e581d444f1e7bedcf35e214a5401f8a1/Zamoto raw data/zomato.csv -------------------------------------------------------------------------------- /Zomato Dashboard.pbix: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ritikaga/Zomato-Analysis-with-Python-and-visualization-with-Power-BI/f35c2f36e581d444f1e7bedcf35e214a5401f8a1/Zomato Dashboard.pbix --------------------------------------------------------------------------------