https://github.com/deva-246/business-insights-on-realtime-swiggy-data-using-python
Data analysis for business decision-making and insights of a real time segment of Swiggy data.
https://github.com/deva-246/business-insights-on-realtime-swiggy-data-using-python
data-visualization jupyter pandas python seaborn
Last synced: about 1 month ago
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Data analysis for business decision-making and insights of a real time segment of Swiggy data.
- Host: GitHub
- URL: https://github.com/deva-246/business-insights-on-realtime-swiggy-data-using-python
- Owner: deva-246
- Created: 2023-10-11T13:22:31.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-10-12T15:31:31.000Z (over 2 years ago)
- Last Synced: 2025-03-22T06:17:34.542Z (about 1 year ago)
- Topics: data-visualization, jupyter, pandas, python, seaborn
- Homepage:
- Size: 25.4 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Business-Insights-on-Realtime-Swiggy-Data-using-Python
Data analysis on a part of real time Swiggy data to gain insights and make business based decisions. This dataset contains the information about items and orders placed from the period 2017-2022.
## Datasets
The fields present in items dataset are,
1. id
2. order id
3. Name of the item
4. is_veg / not
The fields present in Orders dataset are,
1. id
2. order id
3. order_total
4. Restaurent Name
5. Order Time
6. Rain mode
7. On time / not
## Business Insights
1. **Unique Items that are ordered**
There are 164 unique items present in the dataset, which are been delivered by swiggy.
2. **Count of Veg and Non veg Items**
There were **12** **Non veg** items
**180 Veg items**
and a **new category(undefined)** - Maybe Desserts - **1 item**
3. **Details on Chicken based dishes**
The value -1 denotes the absence of chicken and other positive valuesdenotes the presence of the word chicken.
In order to filter the chicken based dishes we can access with the chickenfound field value.
4. **Items that are ordered the most**
To know the most ordered items - we can group the items by their name along with the count of it's order. As the default result displays in ascending order use sort_values() function with ascending=False as it's parameter
**Classic Mac & Cheese** is the most ordered item according to the dataset.
5. **Count of distinct orders**
There are 95 unique orders.
6. **Unique Restaurents where the orders are placed**
There are **49** unique restaurents which have recieved orders.
7. **Top Restaurents with highest number of orders**
The **The Bowl Company** has most number of orders in swiggy and it's identified using rank() which has 12 unique orders accorind to the given data.
8. **Revenue generated by each year from 2017 - 2022**
This can be found by grouping the data with respect to year and summing up the order_total field.
Each year generated Revenue value is listed above, from which we come to know that swiggy has generated on the year **2021** with a value of **11343.0**.
9. **Revenue difference between 2020 and 2021**
To get the difference it's necessary to find the previous sales value in order to find the difference percentage. In python we can get the previous value by using shift() function and this is a challenging act according to SQL.
From the above image, we can see that there is **2.295% ** difference between 2020 and 2021 year in terms of Revenue generation. The negative values of different percentage shows that there was a drop in revenue between the corresponding years (2019-20)(2021-22).