Predictive Modelling

Predictive modeling, also called predictive analytics, is a mathematical process that seeks to predict future events or outcomes by analyzing patterns that are likely to forecast future results. ... As additional data becomes available, the statistical analysis will either be validated or revised.

Useful video - Models (recap)

Predictive analysis and modelling

Predictive models use known results to develop (or train) a model that can be used to predict values for different or new data. The modeling results in predictions that represent a probability of the target variable (for example, revenue) based on estimated significance from a set of input variables.

Predictive Modelling with Python (real world use)

Uses

One of the most common uses of predictive modeling is in online advertising and marketing. Modelers use web surfers' historical data, running it through algorithms to determine what kinds of products users might be interested in and what they are likely to click on.

Typical Methods

Some of the most popular methods include:

• Decision trees. Decision tree algorithms take data (mined, open source, internal) and graphs it out in branches to display the possible outcomes of various decisions. Decision trees classify response variables and predict response variables based on past decisions, can be used with incomplete data sets and is easily explainable and accessible for novice data scientists.
• Time series analysis. This is a technique for the prediction of events through a sequence of time. You can predict future events by analyzing past trends and extrapolating from there.
• Logistic regression. This method is a statistical analysis method that aids in data preparation. As more data is brought in, the algorithm's ability to sort and classify it improves and therefore predictions can be made.

The most complex area of predictive modeling is the neural network

Common Algorithms for Predictive Modelling

>>Random Forest. An algorithm that combines unrelated decision trees and uses classification and regression to organize and label vast amounts of data.

>>Gradient boosted model. An algorithm that uses several decision trees, similar to Random Forest, but they are more closely related. In this, each tree corrects the flaws of the previous one and builds a more accurate picture.

>>K-Means. Groups data points in a similar fashion as a clustering model and is popular with personalized retail offers. It can create personalized offers when dealing with a large group by seeking out similarities.

>>Prophet. A forecasting procedure especially effective when dealing with capacity planning. This algorithm deals with time series data and is relatively