Python

---

**Project: Zomato Data Analysis **

---

1 - importing all the libraries

## import all liberies..
## pandas for data manipulation of data and analyis
## numpy for numerical operations 
## matplotlib.pyplot and seaborn for data visualization

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

---

2 - create a data frame

dataframe = pd.read_csv("Zomato data .csv")
print(dataframe)

                      name online_order book_table   rate  votes  \
0                    Jalsa          Yes        Yes  4.1/5    775   
1           Spice Elephant          Yes         No  4.1/5    787   
2          San Churro Cafe          Yes         No  3.8/5    918   
3    Addhuri Udupi Bhojana           No         No  3.7/5     88   
4            Grand Village           No         No  3.8/5    166   
..                     ...          ...        ...    ...    ...   
143       Melting Melodies           No         No  3.3/5      0   
144        New Indraprasta           No         No  3.3/5      0   
145           Anna Kuteera          Yes         No  4.0/5    771   
146                 Darbar           No         No  3.0/5     98   
147          Vijayalakshmi          Yes         No  3.9/5     47   

     approx_cost(for two people) listed_in(type)  
0                            800          Buffet  
1                            800          Buffet  
2                            800          Buffet  
3                            300          Buffet  
4                            600          Buffet  
..                           ...             ...  
143                          100          Dining  
144                          150          Dining  
145                          450          Dining  
146                          800          Dining  
147                          200          Dining  

[148 rows x 7 columns]

dataframe

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	name	online_order	book_table	rate	votes	approx_cost(for two people)	listed_in(type)
0	Jalsa	Yes	Yes	4.1/5	775	800	Buffet
1	Spice Elephant	Yes	No	4.1/5	787	800	Buffet
2	San Churro Cafe	Yes	No	3.8/5	918	800	Buffet
3	Addhuri Udupi Bhojana	No	No	3.7/5	88	300	Buffet
4	Grand Village	No	No	3.8/5	166	600	Buffet
...	...	...	...	...	...	...	...
143	Melting Melodies	No	No	3.3/5	0	100	Dining
144	New Indraprasta	No	No	3.3/5	0	150	Dining
145	Anna Kuteera	Yes	No	4.0/5	771	450	Dining
146	Darbar	No	No	3.0/5	98	800	Dining
147	Vijayalakshmi	Yes	No	3.9/5	47	200	Dining

148 rows × 7 columns

---

3 - convert the data type of column - rate

def handlerate(value):
    value =str(value).split("/")
    value = value[0];
    return float(value)

dataframe["rate"]=dataframe["rate"].apply(handlerate)
print(dataframe.head())

                    name online_order book_table  rate  votes  \
0                  Jalsa          Yes        Yes   4.1    775   
1         Spice Elephant          Yes         No   4.1    787   
2        San Churro Cafe          Yes         No   3.8    918   
3  Addhuri Udupi Bhojana           No         No   3.7     88   
4          Grand Village           No         No   3.8    166   

   approx_cost(for two people) listed_in(type)  
0                          800          Buffet  
1                          800          Buffet  
2                          800          Buffet  
3                          300          Buffet  
4                          600          Buffet  

---

4 - for checking any missing or null value

dataframe.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 148 entries, 0 to 147
Data columns (total 7 columns):
 #   Column                       Non-Null Count  Dtype  
---  ------                       --------------  -----  
 0   name                         148 non-null    object 
 1   online_order                 148 non-null    object 
 2   book_table                   148 non-null    object 
 3   rate                         148 non-null    float64
 4   votes                        148 non-null    int64  
 5   approx_cost(for two people)  148 non-null    int64  
 6   listed_in(type)              148 non-null    object 
dtypes: float64(1), int64(2), object(4)
memory usage: 8.2+ KB

---

QUESTION 1:

Which type of restaurant do the majority of customers prefer to order food from?

dataframe.head()

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	name	online_order	book_table	rate	votes	approx_cost(for two people)	listed_in(type)
0	Jalsa	Yes	Yes	4.1	775	800	Buffet
1	Spice Elephant	Yes	No	4.1	787	800	Buffet
2	San Churro Cafe	Yes	No	3.8	918	800	Buffet
3	Addhuri Udupi Bhojana	No	No	3.7	88	300	Buffet
4	Grand Village	No	No	3.8	166	600	Buffet

Using a count plot to exact accurate counting of data.

sns.countplot(x=dataframe["listed_in(type)"],palette="Set1") #Set2,coolwarm
plt.xlabel("TYPE OF RESTAURANT's",fontsize=10)
plt.ylabel("NUMBER OF ORDER'S",fontsize=10)
plt.xticks(rotation=90)

C:\Users\arjit\AppData\Local\Temp\ipykernel_17072\2928614793.py:1: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(x=dataframe["listed_in(type)"],palette="Set1") #Set2,coolwarm





([0, 1, 2, 3],
 [Text(0, 0, 'Buffet'),
  Text(1, 0, 'Cafes'),
  Text(2, 0, 'other'),
  Text(3, 0, 'Dining')])

# bargraph

Conclusion:

• The majority of customers tend to order food from Dining type restaurants, followed by Cafes. Buffet restaurants come in third, while other types of restaurants account for a smaller share of customer preferences.
• This suggests a strong preference for dining-in experiences, with cafes also being a popular choice for many customers.

---

Question 2:

How many votes does each restaurant type receive from its customers?

dataframe.head()

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	name	online_order	book_table	rate	votes	approx_cost(for two people)	listed_in(type)
0	Jalsa	Yes	Yes	4.1	775	800	Buffet
1	Spice Elephant	Yes	No	4.1	787	800	Buffet
2	San Churro Cafe	Yes	No	3.8	918	800	Buffet
3	Addhuri Udupi Bhojana	No	No	3.7	88	300	Buffet
4	Grand Village	No	No	3.8	166	600	Buffet

grouped_data=dataframe.groupby("listed_in(type)")["votes"].sum()
result = pd.DataFrame({"votes": grouped_data})
plt.plot(result,c="blue",marker="o")
plt.xlabel("Type Of Restaurant",c="grey",size=15)
plt.ylabel("Votes",c="grey",size=15)
plt.show()

# line graph 

Conclusion:

Dining-type restaurants receive the highest number of votes from customers, indicating a strong preference for this restaurant category.

---

Question 3:

What is the highest rating received by restaurants across different categories?

dataframe.head()

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	name	online_order	book_table	rate	votes	approx_cost(for two people)	listed_in(type)
0	Jalsa	Yes	Yes	4.1	775	800	Buffet
1	Spice Elephant	Yes	No	4.1	787	800	Buffet
2	San Churro Cafe	Yes	No	3.8	918	800	Buffet
3	Addhuri Udupi Bhojana	No	No	3.7	88	300	Buffet
4	Grand Village	No	No	3.8	166	600	Buffet

plt.hist(dataframe["rate"],bins=4)
plt.title("Rating Distribution")
plt.show()

# HISTOGRAM

Conclusion:

The highest ratings for restaurants typically fall within the range of 3.25 to 4.

---

Question 4:

What is the average spending per order by couples at restaurants?

dataframe.head()

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	name	online_order	book_table	rate	votes	approx_cost(for two people)	listed_in(type)
0	Jalsa	Yes	Yes	4.1	775	800	Buffet
1	Spice Elephant	Yes	No	4.1	787	800	Buffet
2	San Churro Cafe	Yes	No	3.8	918	800	Buffet
3	Addhuri Udupi Bhojana	No	No	3.7	88	300	Buffet
4	Grand Village	No	No	3.8	166	600	Buffet

couple_data = dataframe["approx_cost(for two people)"]
sns.countplot(x=couple_data,palette="viridis")

C:\Users\arjit\AppData\Local\Temp\ipykernel_17072\3262229793.py:2: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(x=couple_data,palette="viridis")





<Axes: xlabel='approx_cost(for two people)', ylabel='count'>

# BARGRAPH

Conclusion:

The average spending per order by couples at a restaurant is ₹300.

---

Question 5:

Which mode (online or offline) receives higher ratings in terms of user satisfaction?

dataframe.head()

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	name	online_order	book_table	rate	votes	approx_cost(for two people)	listed_in(type)
0	Jalsa	Yes	Yes	4.1	775	800	Buffet
1	Spice Elephant	Yes	No	4.1	787	800	Buffet
2	San Churro Cafe	Yes	No	3.8	918	800	Buffet
3	Addhuri Udupi Bhojana	No	No	3.7	88	300	Buffet
4	Grand Village	No	No	3.8	166	600	Buffet

plt.figure(figsize = (6,6))
sns.boxplot(x = "online_order",y ="rate",data = dataframe,palette ="Set2")

C:\Users\arjit\AppData\Local\Temp\ipykernel_17072\1069442493.py:2: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(x = "online_order",y ="rate",data = dataframe,palette ="Set2")





<Axes: xlabel='online_order', ylabel='rate'>

# box plot

  Cell In[90], line 1
    |
    ^
SyntaxError: invalid syntax

Conclusion:

In conclusion, online orders receive higher ratings than offline orders. This trend reflects greater customer satisfaction with online ordering.

---

Question 6:

What type of restaurant generates the highest number of offline orders, making them eligible for Zomato's promotional offers?

dataframe.head()

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	name	online_order	book_table	rate	votes	approx_cost(for two people)	listed_in(type)
0	Jalsa	Yes	Yes	4.1	775	800	Buffet
1	Spice Elephant	Yes	No	4.1	787	800	Buffet
2	San Churro Cafe	Yes	No	3.8	918	800	Buffet
3	Addhuri Udupi Bhojana	No	No	3.7	88	300	Buffet
4	Grand Village	No	No	3.8	166	600	Buffet

pivot_table = dataframe.pivot_table(index ="listed_in(type)",columns="online_order",aggfunc="size",fill_value=0)
sns.heatmap(pivot_table,annot =True,cmap="YlGnBu",fmt="d")
plt.title("HeatMap")
plt.xlabel("Online_order")
plt.ylabel("Listed_in(Type)")
plt.show()

#HEATMAP

Conclusion:

Dining restaurants receive more offline orders, while cafes receive more online orders. This suggests that customers prefer in-person dining at restaurants and opt for online ordering at cafes.

---

---

Conclusion:

This Zomato Data Analysis project provides insightful findings based on the analysis of restaurant data across various categories. The following key conclusions can be drawn from the analysis:

1. Most Preferred Restaurant Type:

• The majority of customers tend to prefer Dining type restaurants, followed by Cafes. Buffet restaurants come in third, while other types have a relatively smaller share in customer preferences.
• This suggests that dining-in experiences are more popular, with cafes also being a popular choice.

3. Votes by Restaurant Type:

• Dining type restaurants receive the highest number of votes, reinforcing the fact that this category has the most customer engagement.
• This further emphasizes the popularity of dining options for customers.

5. Highest Ratings by Restaurant Category:

• The highest ratings across various restaurant types were analyzed, offering a glimpse into customer satisfaction levels and the overall quality of services provided by restaurants.

Through this analysis, we gain a better understanding of customer behavior, which can be used for further research or business decisions in the restaurant industry. The project effectively demonstrates the use of Python libraries like Pandas, NumPy, and Seaborn for data manipulation and visualization, and serves as an excellent showcase of data analysis skills.