Predictive analytics is a branch of advanced analytics that uses statistical techniques, machine learning algorithms, and data mining to analyze historical data and make predictions about future events or trends. It helps organizations to identify patterns, understand trends, and anticipate outcomes, which can lead to better decision-making and more efficient operations. Predictive analytics is used in a variety of industries, including finance, healthcare, marketing, and manufacturing. By using predictive analytics, organizations can gain insights into customer behaviour, optimize marketing campaigns, detect fraud, and improve supply chain management, among other benefits.

In this course with 8+ hours of content, we will be covering all the important basics of Predictive analytics and then we will start with all the important concepts about Python and Object Oriented Programming.

Python is a general-purpose language used in major areas like data science, machine learning and web development.

This course covers almost all the essential concepts of Python. As a beginner, it is essential to have a basic clear and thorough understanding of the concepts. This course focuses on these points and we have tried our best to deliver it in a way which will help you to get the basics right as well as concepts clear.

The key concepts covered in this course are:

1. Built-in data types like list, dictionary, tuple, set, and string with their functionalities.

2. List comprehension, lambda function and decorators in Python

3. Object-oriented programming in Python

4. Creating an application using all the learned concepts

5. Predictive Analytics

6. A predictive model using Random Forest Algorithm to predict a winner between two given Pokemon.

Does this course cover all the concepts of Data Science and Web development?

I will be honest with you, the course does not cover all the concepts of Web Development (although we have different courses for that we will discuss it some other day), however, it does cover about basics of Data Science and Machine Learning.

This is a non-refundable course.

Includes free updates related to this course.