Classification Analysis - Opportunity Winning & Lossing rate

Step 1

Prepare Opportunity dataset in .csv format such as https://github.com/datomnurdin/PredictiveAnalysis/blob/master/InternalData/data/Customer-country_data_5-5_Total-Net-Revenue.csv and place it into project directory.

Step 2

Split training & test data using 70:30 ratio.

Step 3

Open command prompt and go to project directory. Launch jupyter notebook. Use this jupyter app for analysis, https://github.com/datomnurdin/PredictiveAnalysis/blob/master/InternalData/Opportunity.ipynb.

Step 4

Get the file generated result.csv (https://github.com/datomnurdin/PredictiveAnalysis/blob/master/InternalData/data/result.csv) & calculate opportunity winning & lossing probability rate.

Step 5

Update into .json file.

Clustering - Total Net Revenue

Step 1

Prepare Customer Country Total Net Revenue dataset in .csv format such as https://github.com/datomnurdin/PredictiveAnalysis/blob/master/InternalData/data/Customer-country_data_5-5_Total-Net-Revenue.csv and place it into project directory.

Step 2

Open command prompt and go to project directory. Launch jupyter notebook. Use this jupyter app for analysis, https://github.com/datomnurdin/PredictiveAnalysis/blob/master/InternalData/Customer-country_data_5-5_Total-Net-Revenue.csv.ipynb.

Step 3

Discover and interpret finding & analysis based on similarities.

Step 4

Update into .json file.