** Predictive modelling** is the process that uses data mining (the process of finding similar patterns in the data), and finds the probability of any occurrence to produce output. In simple words, forecasting based on various methods.

It uses statistics to predict the results. Basically predictions are used to predict the future outcomes, but it can be applied on unknown data and where occurrence of time doesn’t matter.

Models are chosen on the basis of Detection theory(ability to differentiate between informative pattern and random pattern from the data).

The model is selected on the basis of testing , validation using the Detection theory to find the probability of any output of given input.

**Predictive Modeling Process:**

Modelling processes involve many algorithms and models and this process is iterative in nature, using multiple models or algorithms on the same data gives the best fit model of the data.

*Categories of Models:*

1. Predictive model

In this model, it uses past predictions for analyses and uses them for future prediction.

2. Descriptive Model

It defines the relationship between various entities of the data used.

3. Decision Model

This is used for making decisions over particular conditions and this is a repeatable approach which can be used again and again.

*Exponential Smoothing:*

It is a part of a time series where older data has less weight( less priority) as compared to new data and new data is more relevant and has more weight.

Smoothing constant represented by α ( it represents the weight of observation).

This is used for short term forecasting and is used in Tableau as well.

*Types of Exponential Smoothing:*

- Simple exponential smoothing
- Double exponential smoothing
- Triple exponential smoothing

Simple exponential smoothing

*Arima Model:*

It stands for Auto-regresive Integrated Moving Average .

Arima model belongs to the class of statistical models for analyzing and forecasting time series.

There are two types of models used for forecasting time series are:

1. Seasonal

2. Non- seasonal

ARIMA model is used for time series forecasting.

A fellow mate helped me write this article. Thanks to her.

*Thank you for the read 🙂*