Time-Series Forecasting using TBATS model
Time-series data with multiple seasonal effects are difficult to model and require the use of specialised algorithms. TBATS is a time-series forecasting method that accounts for multiple seasonalities.
Extracting Meaningful Insights from Data using Exploratory Data Analysis
Raw data is normally ambiguous and difficult to interpret. Cleaning it is essential in order to understand the relationships between the variables present in the data.
Steps to backup and restore Pentaho+ repository
Pentaho Repository is an environment for collaborative analysis and ETL development. The necessity of backing up the pentaho repository helps to restore
Predicting Customer Churn using Machine Learning
Predicting customer churn can help your business improve upon those areas where customer service is lacking and improve revenue.
Steps to enhance server security through Server hardening
Estimating Future Customer Demand using Demand Forecasting
Demand forecasting is essential for estimating how customer's demand for products and services vary in the foreseeable future
