Revenue Forecasting

Problem Statement:

  1. Many entrepreneurs complain that building forecasts with any degree of accuracy takes a lot of time–time that could be spent selling rather than planning. But few investors will put money in your business if you’re unable to provide a set of thoughtful forecasts.More important, proper financial forecasts will help you develop operational and staffing plans that will help make your business a success.
  2. Company fetches the financial data from various clients as individual excel sheets. These sheets are then clubbed as zip file in empirical model sheet . Financial Company calculates regression model using statistical way.But we can solve this problem using machine learning.Healthcare company wants to forecast sales of revenues from empirical sheet based on past sales for better results,reduce labour work and fastest computations.


  • The challenge is that the building forecasts with a degree of accuracy take a lot of time. It is also important to take the seasonal & holiday cycles into account to make it more realistic. Thus time-series analysis was used to build the prediction


  1. iVentura Machine Learning Platform was used for building the solution. iVentura provides the complete ecosystem for data scientists to build models without worrying about the underlying Infra & Security. Either for a team or an individual data scientist, iVentura is ideally suited as a platform of choice.
  2. To deal with the above problem statement ,datasets needs to be analysed and evaluated with metrics to acquire best outcome. Here we go:
  3. 1) Extract zip file and read empirical model sheet.
  4. 2) Extracted excel file is in unstructured format.perform all data preprocessing operation according to excel file sets.
  5. 3) Analysis month sales graph to understand pattern.
  6. 4) Perform dickey-fuller stationarity test
  7. 5) Trained model using FBProphet on time series data
  8. 6) Plot forecasting results and revenue component
  9. 7) Forecast for quarterly sales
  10. 8) Save the data into pickle
  11. 9) Deployment & Visualization