How to predict the future using AI (Predictive Analytics)?

Have you ever imagined how election poll estimates are coming (or) How will restaurants manage both regular orders and online orders (or) How is weather prediction done far ahead? Yes you're right you might be thinking machine learning / AI is a reason. It is a very huge subject, so today we will learn some techniques so we can become future predictors. Let’s start from basics, Is there any name from this yes we have and it is…...

Predictive analytics:

Predictive analytics is a form of technology that makes predictions about certain unknowns in the future. It draws on a series of techniques to make these determinations, including artificial intelligence (AI), data mining, machine learning, modeling, and statistics. For instance, data mining involves the analysis of large sets of data to detect patterns from it. Text analysis does the same, except for large blocks of text. Predictive models are used for all kinds of applications, including:

• Weather forecasts
• Creating video games
• Translating voice to text for mobile phone messaging
• Customer service
• Investment portfolio development

Lets understand Predictive Analytics Models,

Classification Model: This technique is used to classify the person’s decision or match result, based on the previous data.
Clustering Model: Have you ever thought about segregating students in our childhood, so that we can focus more on particular students, yes the same procedure here but depends on various factors, Marketing team need to focus on certain kind of customers, then we will use clustering Model.
Time-Series Model: How many in our childhood have thought about the power of prediction, yes you can achieve it through time series, we can predict our sales, weather, stocks, by using forecasting models.

Classification Model:

The classification model is, in some ways, the simplest of the several types of predictive analytics models we’re going to cover. It puts data in categories based on what it learns from historical data. Classification models are best to answer yes or no questions, providing broad analysis that’s helpful for guiding decisive action. These models can answer questions such as:

Use Case-1 : For a loan provider, “Will this loan be approved?”

Use Case-2 : For the house vendor, “Will the person buy this house?” As we discussed classification, let's discuss the most popular technique: random forest.

Random Forest:

Random Forest is perhaps the most popular classification algorithm, capable of both classification and regression. It can accurately classify large volumes of data. The name “Random Forest” is derived from the fact that the algorithm is a combination of decision trees. Each tree depends on the values of a random vector sampled independently with the same distribution for all trees in the “forest.” Each one is grown to the largest extent possible.

Clustering Model:

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is a main task of exploratory data analysis, and a common technique for statistical data analysis, used in many fields

Use Case-1 : Product Segmentation

Use Case-2 : Customer Segmentation in E-Commerce.

As we discussed clustering, let's discuss the most popular technique: K-means.

K-Means: A highly popular, high-speed algorithm, K-means involves placing unlabeled data points in separate groups based on similarities. This algorithm is used for the clustering model. For example, Tom and Rebecca are in group one and John and Henry are in group two. Tom and Rebecca have very similar characteristics but Rebecca and John have very different characteristics. K-means tries to figure out what the common characteristics are for individuals and groups them together. This is particularly helpful when you have a large data set and are looking to implement a personalized plan—this is very difficult to do with one million people.

Time Series Model:

Time series forecasting is a technique for the prediction of events through a sequence of time. The technique is used across many fields of study, from geology to behavior to economics. The techniques predict future events by analyzing the trends of the past, on the assumption that future trends will hold similar to historical trends.

UseCase - 1: Predicting Sales for next quarter.

UseCase - 2: Predicting Orders for the hotels.

As we discussed time series, let's discuss the most popular technique called fbprophet.

fbprophet - Time Series Model: The Prophet algorithm is used in the time series and forecast models. It is an open-source algorithm developed by Facebook, used internally by the company for forecasting. The Prophet algorithm is of great use in capacity planning, such as allocating resources and setting sales goals. Owing to the inconsistent level of performance of fully automated forecasting algorithms, and their inflexibility, successfully automating this process has been difficult. On the other hand, manual forecasting requires hours of labor by highly experienced analysts.