Sales Data Analysis is a practice project performing Exploratory Data Analysis on some example sales data. This project is to learn and understand different data analytic techniques to gain insight from a raw sales data.
This project uses the following python libraries for data analysis
- Numpy
- Pandas
- Matplotlib
The packages used in the project are installed in a python virtual environment to avoid conflicts with existing packages. Follow the steps to clone and run the project in jupyter notebook
git clone https://github.com/rama-2402/py-salesdataanalysis.git
cd py-salesdataanalysis/
After navigating to the project directory, Create a virtual environment and install the packges using the following commands.
python3 -m venv venv/
#If you are using bash shell
source venv/bin/activate
#If you are using Fish shell
source venv/bin/activate.fish
#To install the necessary dependencies
pip install -r requirements.txt
#To run the jupyter notebook
jupyter notebook
#Nuke the environment
rm -r venv/
#Install a new environment and dependencies
python3 -m venv venv/
pip install -r requirements.txt
Following analysis has been made on the sales data
- Data cleaning
- Finding Months with highest sales
- Finding Cities with highest sales
- Finding a Hour of the day to show ADs to drive more sales
- Finding the product pairs that are most often bought together
- Finding products that are most sold during holodays
- Observation: The month of December has the highest sales recorded in the year
- Insight: This could indicate the customer behavior of spending more during holidays
- Observation: The month of Kanuary has the lowest sales recorded in the year
- Insight: This could indicate the customer behavior of spending less or rather depleted savings right after holidays
- Observation: The city of "San Francisco" has the highest sales recorded in the year
- Observation: The store has recorded an average "peak sales" during the hours of "11AM - 1PM" and "5PM - 7PM" eeryday throughout the year.
- Insights: Running an a general or a targeted Ad campaign just hours before the peak sale hours could drive more sales to the stores.
- Observation: iPhone and Lightning cables are the most often purchased products in pairs.
- Insights: This gives us an insight on the customer behavior of purchasing tech products in pairs and having a discounted sale on relevant products in pairs could drive more sales to the store.
The sales data for this exploratory analysis has been taken from the materials provided in freecodecamp. The exploratory analysis was done in my point of view using different approach.