What is predictive analytics? An introductory guide

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Contents

This article was published as part of the Data Science Blogathon

Introduction

statistics, machine learning, mathematical modeling and artificial intelligence is known as predictive analytics. With the help of past data, makes predictions. We use predictive analytics in our day to day without much thought. For instance, predict the sales of an item (for instance, flores) in a market for a particular day. If it's valentine's day, Rose sales would be high! We can easily say that flower sales would be higher on holidays than on normal days.

In predictive analytics, we find the responsible factors, we collect data, we apply machine learning techniques, data mining, predictive modeling and other analytical techniques to predict the future. Data insights include patterns, the relationship between different factors that might be previously unknown. Unraveling those hidden knowledge is worth more than you think. Companies use predictive analytics to improve their process and achieve their goals. Information obtained from structured and unstructured data can be used for predictive analytics.

How data statistics help?

In recent years, Organizations have chosen to collect large amounts of data assuming that, if they collect a sufficient amount, will eventually lead to relevant business information. Even Instagram and Facebook provide information on business accounts. But, data in its raw form is useless no matter how large. The more data to analyze, more difficult is to separate valuable business information from irrelevant. A data insight strategy builds on the real potential of the data, you must first determine why you are using them and what business value you expect to get from them. Then, explains how to obtain valuable information from the data and how to use it.

1. Definition of the problem statement / business objective.

Define the project results, The deliverables, the scope of the effort, business goals, prepare a questionnaire for the data to be obtained based on the business objective.

2. Data collection based on the answers to the questions created based on the problem statement.

Based on the questionnaire, collect responses as data sets.

3. Integrate data from multiple sources.

Data mining for predictive analytics prepares data from multiple sources for analysis. This provides a comprehensive view of customer interactions.

4. Data analysis with tools / analytics software. We can visualize the data to observe patterns and relationships between various factors.

Data analysis is the process of inspecting, clean up, transform and model data in order to discover useful information to reach a conclusion.

5. Validate assumptions, hypotheses and test them using statistical models.

Statistical analysis allows to validate the assumptions, hypotheses and test them using statistical models. The assumptions are based on the problem statement, formed during EDA.

6. Generation of models

The model is generated with algorithms to automate the process with the new data combined with the existing data. Several models can also be combined for better results.

7. Implement the model to generate predictions and monitor its accuracy.

The implementation of the predictive model provides the option to implement the analytical results in the daily decision-making process to obtain results, reports and outputs by automating modeling-based decisions.

What's more, we manage and monitor the performance of the model to ensure that it is delivering the expected results.

predictive analytics

Incorrect or incomplete data can lead to poor models and accuracy causing chaos. That is why it is extremely necessary to have an adequate data set to obtain information and train the model.. Predictive analytics has its own challenges, but it can lead to invaluable business results, including acquiring customers before they leave, optimization of the commercial budget and satisfaction of customer demand.

Models and algorithms

Various domain techniques, including machine learning, Data mining, the statistics, analysis and modeling, are used in predictive analytics. Predictive algorithms can be broadly classified into two groups: machine learning models and deep learning models. Some of them are described in this article. Although they have their own merits and demerits, a great merit of all of them is that they are reusable and can be trained using algorithms with specific rules of the company. Predictive analytics is an iterative process that involves collecting, preprocessing, modeling and implementing data to get results. We can automate the process to provide us with new predictions based on the new data that is fed regularly over time..

Once a model is trained, we can input new data to get predictions and no need to train over and over again, but a disadvantage is that it needs a lot of data to be trained. Since predictive analytics is based on machine learning algorithms, requires proper classification of data on labels, what, on the contrary, would cause poor performance and accuracy. Generalization is a problem, since the model has little capacity to transfer its findings from one case to another. Although there are some applicability issues when it comes to findings derived from a predictive analytics model, can be solved by certain methods, like transfer learning.

Predictive analytics models

  1. Classification model

It is one of the simplest models. Classify new data based on what you learned from historical data. They are best for binary classification when answering binary questions like Yes / No, True / Fake, but they can also be used for multiclass classification. Decision trees, support vector machines are some classification algorithms.

P.ej. : Loan approval is a classic use case of a classification model. Another example is the messages / spam detection emails.

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  1. Clustering model

A clustering model classifies data points into groups based on similarity of attributes. There are many clustering algorithms, but no algorithm can be considered the best for all use cases. It is an unsupervised learning algorithm, unlike supervised classification.

For instance: Group students from a school based on their location in a city for transportation services. Group customers based on their item preferences to recommend products related to their interests.

  1. Forecast model

Being one of the most used predictive analysis models, deals with the prediction of metric values, estimating a numerical value for new data based on what has been learned from historical data. Can be applied whenever numerical data is available.

Not .: Prediction of traffic on the main road of a city during different periods. Stores estimating availability of products in their warehouse.

  1. Outlier model

As the name suggests, relies on anomalous data entries in your dataset. An outlier could be a data entry error, Measurement error, error experimental, intentional, data processing error, sampling error or natural error. Although outliers can cause poor performance and precision, some help us find novelty or observe new inferences.

Not .: Credit card theft / debt.

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  1. Time series model

Can be used for any sequence of data points with a time period as the input parameter. Use past data to develop a numeric metric and predict future data using that metric.

Not .: weather forecast, stock market / cryptocurrency price prediction.

Some common predictive algorithms are Random Forests, generalized linear model, reinforced gradient pattern, grouping of K-means and Prophet. The random forest is a combination of decision trees, in which they try to achieve the smallest possible error by using the technique of “embolsado” O “impulse”. The generalized linear model is a more complex variant of the general linear model that trains very quickly. The response variable can have any form of exponential distribution type that provides a clear understanding of how the predictors influence the outcome..

Although they are resistant to overfitting, require a large data set for training and are susceptible to outliers. Gradient Boosted Model is a prediction model based on a set of decision trees. Unlike random forests, build one tree at a time and correct previous bugs while building a new tree. K-means is useful when looking to implement a custom plan on a large data set. Used in grouping models. The prophet is an algorithm used in time series and forecasting models. It is not only automatic, also incorporates useful heuristics and assumptions. It is popular for being fast, reliable and robust.

Some u

Predictive Analytics as said already has many applications in different domains. To name a few,

  1. Health care
  2. Collection analysis
  3. Fraud detection
  4. Risk management
  5. Direct marketing
  6. Cruz-

Then, How exactly do they help in your domains? We receive alerts when we log into our Gmail account from a new device. We receive alerts when we use our credit cards / debit in new places. How do they detect it? With predictive analytics, fraud examiners take a few sets of predetermined variables that are known to be involved in past fraud events and put those variables into processes to determine the likelihood that future results or events are or are not fraud. Suppose you regularly use your credit cards in Kerala, when your credit card is used in New Delhi it is a possible case of fraud. Commonwealth Bank uses analytics to predict the likelihood of fraudulent activity for any given transaction before it is authorized., within the 40 milliseconds after the start of the transaction.

In addition to detecting claims fraud, the health insurance industry is taking steps to identify patients at highest risk for chronic diseases and find the best interventions. Express Scripts, a large pharmaceutical benefits company, uses testing to identify those who do not adhere to prescribed treatments, which generates significant savings. Predictive analytics apps analyze spending, usage and other customer behavior, leading to efficient cross-selling or selling additional products to existing customers for an organization offering multiple products.

About the Author

I am keerthana, a data science student fascinated by mathematics and its applications in other domains. I'm also interested in writing articles related to math and data science.. You can connect with me at LinkedIn Y Instagram. Check out my other items here.

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