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├── analysis.py
└── README.md
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/analysis.py:
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1 | import pandas as pd
2 | import numpy as np
3 | import matplotlib.pyplot as plt
4 | import seaborn as sns
5 |
6 | dataframe=pd.read_csv("Zomato data .csv")
7 | print(dataframe)
8 |
9 | def handleRate(value):
10 | value=str(value).split('/')
11 | value=value[0]
12 | return float(value)
13 |
14 | dataframe['rate'] = dataframe['rate'].apply(handleRate)
15 | print(dataframe.head())
16 |
17 | dataframe.info()
18 |
19 | dataframe.head()
20 | #use this part to get type of restaurant
21 | sns.countplot(x=dataframe['listed_in(type)'])
22 | plt.xlabel("type of resturant")
23 | plt.show()
24 |
25 | #get graph of voting of restaurant
26 | grouped_data=dataframe.groupby('listed_in(type)')['votes'].sum()
27 | result = pd.DataFrame({'votes':grouped_data})
28 | plt.plot(result,c="purple",marker="o")
29 | plt.xlabel("Type of Restaurent",c="blue",size=20)
30 | plt.ylabel("votes",c="red",size=20)
31 | plt.show()
32 |
33 | #get rating distribution
34 | plt.hist(dataframe['rate'],bins=5)
35 | plt.title("rating distribution")
36 | plt.show()
37 |
38 | #get approx_cost of two peoples
39 | couple_data=dataframe['approx_cost(for two people)']
40 | sns.countplot(x=couple_data)
41 | plt.show()
42 |
43 | #get boxplot of online order and rate
44 | plt.figure(figsize=(6,6))
45 | sns.boxplot(x='online_order',y='rate',data=dataframe)
46 | plt.show()
47 | #get heatmap
48 | pivot_table=dataframe.pivot_table(index='listed_in(type)',columns='online_order', aggfunc='size',fill_value=0)
49 | sns.heatmap(pivot_table,annot=True,cmap="YlGnBu",fmt='d')
50 | plt.title("Heatmap")
51 | plt.xlabel("Online Order")
52 | plt.ylabel("Listed In (Type)")
53 | plt.show()
54 |
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/README.md:
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1 | # Data-analysis-of-Zomato
2 |
3 | ## Tools and Modules
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15 |
16 | ## Overview
17 |
18 | This project analyzes a Zomato restaurant dataset to explore various aspects such as restaurant types, customer ratings, voting patterns, approximate costs for two people, and the availability of online ordering. The analysis uses Python libraries including Pandas, NumPy, Matplotlib, and Seaborn to process data and generate visualizations for insights.
19 |
20 | ## Dataset
21 | The dataset (`Zomato data .csv`) contains information about restaurants listed on Zomato, including:
22 | - Restaurant name
23 | - Rating (out of 5)
24 | - Number of votes
25 | - Approximate cost for two people
26 | - Type of restaurant (e.g., Dining, Cafes)
27 | - Online order availability (Yes/No)
28 | - Other features like location and table booking
29 |
30 | ## Analysis Objectives
31 | - Investigate the distribution of restaurant types.
32 | - Analyze the relationship between restaurant types and total votes.
33 | - Examine the distribution of customer ratings.
34 | - Explore the cost for two people across restaurants.
35 | - Assess the impact of online ordering on ratings.
36 | - Visualize the correlation between restaurant types and online order availability.
37 |
38 | ## Code Description
39 | The Python script performs the following steps:
40 | 1. **Data Loading and Preprocessing**:
41 | - Loads the dataset using Pandas.
42 | - Cleans the `rate` column by extracting the numeric rating (e.g., converts "4.1/5" to 4.1).
43 | - Displays dataset information and checks for data integrity.
44 |
45 | 2. **Exploratory Data Analysis (EDA)**:
46 | - Generates summary statistics and previews the dataset.
47 | - Processes the `rate` column for numerical analysis.
48 |
49 | 3. **Visualizations**:
50 | - **Count Plot**: Displays the distribution of restaurant types.
51 | - **Line Plot**: Shows the total votes per restaurant type with markers.
52 | - **Histogram**: Illustrates the distribution of restaurant ratings.
53 | - **Count Plot**: Visualizes the frequency of approximate costs for two people.
54 | - **Box Plot**: Compares ratings for restaurants with and without online ordering.
55 | - **Heatmap**: Shows the count of restaurants by type and online order availability.
56 |
57 | ## Libraries Used
58 | - **Pandas**: For data manipulation and analysis.
59 | - **NumPy**: For numerical computations.
60 | - **Matplotlib**: For creating static visualizations.
61 | - **Seaborn**: For enhanced statistical visualizations.
62 |
63 | ## How to Run
64 | 1. Ensure Python and required libraries are installed:
65 | ```bash
66 | pip install pandas numpy matplotlib seaborn
67 | ```
68 | 2. Place the dataset (`Zomato data .csv`) in the same directory as the script.
69 | 3. Run the Python script:
70 | ```bash
71 | python analysis.py
72 | ```
73 | 4. Visualizations will be displayed, and console output will show dataset details.
74 |
75 | ## Results
76 | - Restaurant Type Distribution: A count plot shows the frequency of different restaurant types (e.g., Dining, Cafes).
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