Gain practical insights into predictive modelling by implementing Predictive Analytics algorithms
on public datasets with Python
Data science is changing the way we go about our daily lives at an unprecedented
pace. The recommendations you see on e-commerce websites, the technologies that
prevent credit card fraud, the logic behind airline itinerary and route selections, the
products and discounts you see in retail stores, and many more decisions are largely
powered by data science. Futuristic sounding applications like self-driving cars,
robots to do household chores, smart wearable technologies, and so on are becoming
a reality, thanks to innovations in data science.
Predictive analytics is a branch of data science, used to predict unknown future
events based on historical data. It uses a number of techniques from data mining,
statistical modelling and machine learning to help make forecasts with an acceptable
level of reliability.
Python is a high-level, object-oriented programming language. It has gained
popularity because of its clear syntax and readability, and beginners can pick up
the language easily. It comes with a large library of modules that can be used to
do a multitude of tasks ranging from data cleaning to building complex predictive
modelling algorithms.