What we did
1. Applied the CRISP-DM method for the execution of the project
2. Experimented with different models and selected Random Forest Classifier as the best
3. Used Orange Data mining along with Python
4. Used Microsoft PowerBI to visualize the reports
The Vitrin9 difference
Our team worked closely with the client to gather all available data from sources like Salesforce, product raw data, NAICS data, sales financials, etc. Once data was identified, the model was deployed to evaluate all possible combinations of attributes and easily access the probability via dashboards.