Clientrator has been made to meet your expectations
Acts as a report card for your sales and marketing efforts, by tracking lead performance
Market Basket Analysis
Data mining technique used to increase sales by better understanding customer purchasing patterns
USE CASE & BENEFITS
Summarizes the data uploaded by displaying summary statistics like mean, median, mode as well as the number of outliers and missing values in every feature in the data.
A section where you can create graphs of any kind using any feature in the data and visualize the relationships between the features. This helps in doing some exploratory data analysis before building a model.
In this section, the user can find out which input features are highly correlated to the output feature. It also shows the correlation between all the features in the data. This section helps the user in deciding what features should the model be built on.
In this section, the user can select and build the model of his choice from scratch by entering values of parameters or by applying other methods that improve the accuracy of the model. This section also helps the user in decidng what model to select by comparing the results of the models with each other.
In this section, the user can upload data and run the model created in the build model section to predict the output feature for that data. This section also analyzes the relationship between the predicted feature and features in the data uploaded as well as the accuracy of the predictions.