It is the use of data and statistics to predict the outcome of the data models. This prediction finds its utility in almost all areas from sports, to TV ratings, corporate earnings, and technological advances. Predictive modeling is also called predictive analytics.
With the help of predictive analytics, we can connect data to effective action about the current conditions and future events. Also, we can enable the business to exploit patterns that are found in historical data to identify potential risks and opportunities before they occur.
Python is used for predictive modeling because Python-based frameworks give us results faster and also help in the planning of the next steps based on the results.
Our tutorial at EDUCBA is tailor-made for people who are willing to work with a framework that delivers the best result in comparison to the rest of the competitive tools in the market.
Our tutorial ensures that you will be able to think with a predictive mindset and understand well the basics of the techniques used in prediction. Critical thinking is very important to validate models and interpret the results.
Hence, our tutorial material emphasizes hardwiring this similar kind of thinking ability. You will have good knowledge about the predictive modeling in python, linear regression, logistic regression, the fitting model with a sci-kit learn library, the fitting model with stat model library, ROC curves, backward elimination approach, stats model package, etc. Learn how to analyze and visualize data using Python libraries.
What you’ll learn:
- Our tutorial ensures that you will be able to think with a predictive mindset and understand well the basics of the techniques used in prediction.
- Critical thinking is very important to validate models and interpret the results. Hence, our tutorial material emphasizes hardwiring a similar kind.
- You will have good knowledge about the predictive modeling in python, linear regression, and logistic regression.
- Learn the fitting model with a sci-kit learn library, the fitting model with stat model library, ROC curves, backward elimination approach, and stats model package.
- To get started with Predictive Modelling with Python a solid foundation in statistics is much appreciated. It takes a good amount of understanding to interpret those numbers to understand whether the numbers are adding up or not. Along with the above-mentioned knowledge, one must know to code in Python. Knowing SQL also acts as a complementary skill set. Even if someone is not well equipped with the above-mentioned skill, it should not act as a hindrance as everything is possible with an honest effort and strong will.
Who this tutorial is for:
- This Predictive Modeling with Python tutorial can be taken up by anyone who shares a decent amount of interest in this field. The earlier someone starts the further they can reach. In the case of students who are pursuing a tutorial in statistics, or computer science graduates it is a very good opportunity to direct your career in that direction. As this is a demanding skill every IT professional is looking for a good switch and entering the domain of predictive analysis. After successfully having hands-on with Predictive Analysis you get open up career opportunities within job roles like that of a Data Analyst, Data Scientist, Business Analyst, Market Research Analyst, Quality Engineer, Solution Architect, Programmer Analyst, Statistical Analyst, Statistician, etc.