Collecting and maintaining data from various data sources, both in structured and unstructured forms, applying data mining and statistical models for predictive analytics – is the most critical part of the business in this digital world.

 

For predictive analytics to be effective, it is essential to have knowledge of different source from where once can get reliable data and how to translate this data into the required outcome.

 

Many a time, data is not available that easily; and when it is, it may need to be preprocessed before feeding it to the data mining/machine learning models.

There are mainly two types of machine learning models:

  • Supervised
  • Unsupervised
  • While Unsupervised models mainly use clustering techniques, Supervised models use classifications and regressions
  • Many modern day techniques are combination of these where multiple algorithms are applied in sequential or parallel manner

Python is the de-facto language when it comes to embed these algorithms for machine learning and predictive analytics solutions; but R is also very commonly used tool for deriving and applying various data science algorithms and techniques.

 

While Sarjen has worked on use cases related to Sales and Price Forecasting, also Sales Volume and Price relationships amongst others, Sarjen can quickly help customers to work on newer use cases for their businesses.

Sarjen can help in:

  • Gathering data from different sources for data mining and data storage
  • Cleaning the data using various pre-processing steps to convert it into a model-ready dataset
  • Applying various statistical and data science algorithms and identify the best algorithm for the problem
  • Creating end-to-end pipelines to perform predictive analytics