Categories: Big Data

Big Data & Predictive Modelling

Talk about big data and things that appear first in an engineer’s mind is Hadoop & related technology. The key thing that is getting missed time and again by many developers’ working on Big Data is a sense of reading/understanding/learning the data and designing algorithms to achieve different objectives such as derivations, predictions etc.

Predictive Modelling

 

One of the key aspect of data science which is also key to Big Data is Predictive Modelling. I wanted to do some quick research and develop an understanding around this topic. However, while researching, it was found that the topic does include some complex underlying mathematical models which will surely be very hard to be understood by 80% of Software Engineers.

Lets try and understand basics of Predictive Modelling.

Predictive modelling is nothing but a process in which a model can be created/used to predict the probability of an outcome based on a set of input data. For example, lets take a very simple example. Companies do publish their plans to set up one or more plants/factories in a certain region. This can be simply used to predict that there are more jobs going to be created in that region. This prediction can be further used to predict money liquidity in that region leading to further investments of different sorts such as real estate, hospitals, schools etc. This data can be used by businesses to plan their investment in that region.

Recently, I have been working on a project where the objective is to come out with different models to predict growth in a region based on investments. Additionally, I have also been researching different models to predict company growth and next moves based on their past and present investments.

There are different models based on which predictive modelling is done. Some of the following is listed on wikipedia page which I shall be detailing out in due course of time:

  1. Group method of data handling
  2. Naive Bayes
  3. Majority Classifier
  4. Support vector machines
  5. Logistic regressions
  6. K-nearest neighbor algorithm

Big Data know for four V’s is certainly a candidate for predictive modelling owing to the volume, variety, velocity & veracity of the data. For software service providers vouching to have expertise in Big data and not having expertise to play with data may not add lot of value to big data implementation projects.

Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.

Recent Posts

Agentic Reasoning Design Patterns in AI: Examples

In recent years, artificial intelligence (AI) has evolved to include more sophisticated and capable agents,…

3 weeks ago

LLMs for Adaptive Learning & Personalized Education

Adaptive learning helps in tailoring learning experiences to fit the unique needs of each student.…

4 weeks ago

Sparse Mixture of Experts (MoE) Models: Examples

With the increasing demand for more powerful machine learning (ML) systems that can handle diverse…

1 month ago

Anxiety Disorder Detection & Machine Learning Techniques

Anxiety is a common mental health condition that affects millions of people around the world.…

1 month ago

Confounder Features & Machine Learning Models: Examples

In machine learning, confounder features or variables can significantly affect the accuracy and validity of…

1 month ago

Credit Card Fraud Detection & Machine Learning

Last updated: 26 Sept, 2024 Credit card fraud detection is a major concern for credit…

1 month ago