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Demand forecasting is the science of predicting the future demand for a product or service. This prediction is based on past behaviour as well as current trends.

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There is a critical need for data processing while using data for analytical purposes. Agility and competitiveness are maintained with the help of an effective data processing strategy.

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Geo-coding is a regular feature of location-based data. A simple download does not suffice to understand the data; it requires specialised methods of extraction and parsing to be understood.

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Creating interactive user interfaces, with a focus on maximisation of usability and user experience, is essential for building a web application. The goal is to make the user's interaction with the web application as simple and efficient as possible.

Databases may generally contain incorrect, incomplete, duplicate or improperly-formatted data. It is essential to employ data cleaning to remove these inconsistencies and prepare data for further analysis.

Demand forecasting provides an estimate of the amount of goods and services that will be purchased in the foreseeable future. Demand forecasting facilitates decision-making and strategic planning.

ARIMA is a time-series forecasting tool that accommodates seasonality, varying trends and multiple parameters.

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.

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.

Predicting customer churn can help your business improve upon those areas where customer service is lacking and improve revenue.

tenthplanet_blog_pentaho_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

tenthplanet_blog_pentaho_Obtaining-Accurate-Predictions--using-Ensemble-Learning-Techniq

Ensemble Methods mostly are used in winning machine learning competitions by devising sophisticated algorithms and producing results with high accuracy