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You must have heard about the Amazon Future Forecast. The way Amazon predicts its business outcomes such as product demand, resources, financial performance, etc results in increasing their profits.
Have you ever thought how it becomes so easy for them to predict what is better for their business? You will definitely say data science, right? Yes! Data Science is here but if you read more about it you will find a term “Predictive Modeling for Data Science“.
So, the actual answer to the above question is Predictive Modeling. Many companies are using this and are growing at a faster pace.
Don’t be afraid of this term. I am explaining the best way which I followed to understand the concept of predictive modeling for data science.
Predictive Modeling and Data Science are two terms that have revolutionized data industries. While Data Science is a pool of data operations, predictive modeling is a major part of it.
There are various types of predictive models and steps that are associated with creation of these models. We will explore these topics further in the blog.
Predictive Modeling for Data Science
Predictive Modeling is an essential part of Data Science. It is one of the final stages of data science where you are required to generate predictions based on the historical data. In order to get an in-depth insight inside data and make decisions that will drive the businesses, we need predictive modeling.
Predictive modeling makes use of statistics to forecast the outcomes. Data Science and Predictive Modeling, therefore, share the common background of statistics.
Data Science is a pool of data operations that also involves predictive modeling as its sub-part. Predictive modeling largely shares its boundaries with machine learning. Therefore, pattern finding and outcome forecasting are two of the most necessary functionalities of predictive modeling.
There are two main classes in predictive modeling –
- Parametric Predictive Modeling
- Non-Parametric Predictive Modeling
There is another class of predictive modeling called semi-predictive modeling.
1. Parametric Predictive Modeling
Parametric Predictive Modeling involves a finite-dimensional model that has a fixed size. A Parametric Predictive Model is independent of the number of training examples. Therefore, no matter how much data is assigned to the model, it will not alter its requirement for the parameters.
There are two steps involved in parametric modeling –
- Selecting a suitable form for the function.
- Learning the coefficients of the function.
A common example of linear predictive modeling is linear regression:
a0 + a1*x1 + a2*x2 = 0
Here, a0, a1 and a2 are the coefficients of line and x1 and x2 are its inputs.
Some of the common parametric predictive models used in Data Science are –
- Logistic Regression
- Linear Discriminant Analysis
- Naive Bayes
- Artificial Neural Networks
Following are the key advantages of parametric predictive models –
- Predictive Models are easier to implement and understand results.
- They do not require much training data and can perform well with tighter constraints.
- They prove to be a poor fit for the underlying mapping function.
Non-Parametric Predictive Modeling
This type of modeling is not dependent on any parametric boundaries. They do not make strong assumptions about the form of mapping functions. Since they don’t make any assumption, they can freely learn any form of functionality from the training data.
They work best in scenarios where you have a large amount of data but no possession of knowledge. In such cases, non-parametric models learn the functional forms from training data.
In case of non-parametric models, the data is fit according to the construction of a mapping function. This also maintains an ability to generalize the data that is not seen.
The most common example of non-parametric predictive modeling is the k-nearest neighbor algorithm that generates predictions based on the most similar training patterns in the data instance.
The data is such that it does not assume any mapping function other than the patterns that have similar output variable.
Some of the popular nonparametric predictive models are –
- Decision Trees
- K-Nearest Neighbors
- Support Vector Machine
Some of the advantages of non-parametric predictive modeling are –
- Due to the assumed independence in the parametric boundaries, there are no assumptions about the underlying pattern.
- There is much higher performance in predictions.
- There is an ability to fit a large number of functional forms.
Semi-Parametric Predictive Modeling
A semi-parametric predictive model shares the attributes of both parametric and non-parametric model. It possesses both finite and infinite dimensional component.
The semi-parametric model is in contrast to the parametric model that has a well defined finite-dimensional space, as well as a non-parametric model that spans across infinite dimensional space.
A semi-parametric model eliminates the limitations of both parametric and non-parametric predictive modeling. It basically takes the advantages of both these models.
Semi-parametric models make use of smoothing and kernels. One of the most popular semi-parametric models is the Cox proportional hazards model.
Data Science Procedure for Creating Predictive Model
- Creation of Predictive Model – With the help of various software solutions and tools, you can create a model to run algorithms on the dataset.
- Model Testing – In order to gauge the performance of the model, we test it on historical data.
- Model Validation – In order to validate the model, we must be able to run it using visualization tools for better understanding.
- Evaluation – Finally, we evaluate the best fit model and select it as our appropriate solution to the problem.
Summary
Hope now you understood how predictive modeling has transformed the data science industry. I am pretty much sure that you enjoyed this article.
Still, if there is something that creates confusion in your mind about predictive modeling for data science, you can freely ask through comments.