# EDA-on-Retail-Data
This repository is implementation of Exploratory Data Analysis on Retail data.
## Getting Started
To use this repo just download the repository, open in jupyter notebook. Start creating something awesome! Good Luck!
### Prerequisites
Things reuired<br>
1. Python3
2. Jupyter Notebook
3. Matplotlib
4. Pandas
5. Other dependencies
## Find it on Kaggle
* [Kaggle](https://www.kaggle.com/iammhaseeb/exploratory-data-analysis-on-retail-data) - The notebook on kaggle.
* [Dataset](https://www.kaggle.com/manjeetsingh/retaildataset) - The dataset used in this notebook.\
Insights
At the time of Sales boost there was following factors which boosted their sales:
- There was holiday on those days
- Fuel prices were average
- Temperature was average
- Unemployment was low
- No MarkDown1, MarkDown2, MarkDown4, MarkDown5
- Yes there was MarkDown3 All out insights very similar to the one we extracted before. It's MarkDown3 that's helping stores to boost their sales specially on holidays.
Insights At the time of Sales boost there was following factors which boosted their sales:
- CPI is much higher
All our insights very similar to the one we extracted before. It's CPI specially that's helping them to boos their sales also all the MarkDowns are same till some extend.
Insights At the time of Sales boost there was following factors which boosted their sales:
- There is holiday
- Fuel prices are average
- Temperature is moderate
- Unemployment is increasing(Strange)
- CPI is above average or at peak
- MarkDown1, MarkDown4 and MarkDown 5 are 0
- MarkDown3 is very high
- MarkDown2 started high but falls down
All our insights very similar to the one we extracted before. The same case it's MarkDown3 that's causing boost in sales.
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