For this project, I have considered Product Demand dataset from Kaggle.
Dataset link: https://www.kaggle.com/felixzhao/productdemandforecasting
Steps to execute:
- Download the files from the github repository.
- Get the Histrorical Product Demand.csv file from its respective .rar file.
- Place the csv files in datasets folder and place the datasets folder in notebooks folder. The notebooks folder should also have ipynb file as well.
- Navigate to terminal and type "jupyter notebook"
- Navigate to the folder where the notebook is placed.
- From the menu icon cell, click on Run all which will run the whole notebook from the first cell. Verify the results.
Steps to follow:
- Set up a data science project structure in a new git repository in your GitHub account
- Download the product demand data set from https://www.kaggle.com/felixzhao/productdemandforecasting
- Load the data set into panda data frames
- Formulate one or two ideas on how feature engineering would help the data set to establish additional value using exploratory data analysis
- Build one or more forecasting models to determine the demand for a particular product using the other columns as features
- Document your process and results
- Commit your notebook, source code, visualizations and other supporting files to the git repository in GitHub