Predictive Modeling with Python Course

Think with a predictive mindset and understand well the basics of the techniques used in prediction with this predictive modelling with python course.

Description

Predictive Modeling 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 modelling 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 and which are found in historical data to identify potential risks and opportunities before they occur. Python is used for predictive modelling because Python-based frameworks give us results faster and also help in the planning of the next steps based on the results.

Our course 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 course material emphasizes hardwiring this similar kind of thinking ability. You will have good knowledge about predictive modelling 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.

In this course, you will get an introduction to Predictive Modelling with Python. You will be guided through the installation of the required software. Data Pre-processing, which includes Data frame, splitting dataset, feature scaling, etc. You will gain an edge on Linear Regression, Salary Prediction, Logistic Regression. You will get to work on various datasets dealing with Credit Risk and Diabetes.

What you’ll learn

  • Learn 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.
  • You will be guided through the installation of the required software. Data Pre-processing, which includes Data frame, splitting dataset, feature scaling, etc. You will gain an edge on 

Requirements

  • 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 skillset
  • 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 course is for:

  • This Predictive Modeling with Python Course 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 course in statistics, or computer science graduates it is a very good opportunity to direct your career in that direction. As this is a much demand skill every IT professional is looking for a good switch and entering the domain of predictive analysis.
  • Data Analyst, Data Scientist, Business Analyst, Market Research Analyst, Quality Engineer, Solution Architect, Programmer Analyst, Statistical Analyst, Statistician

Harshit Srivastava

My name is Harshit Srivastava and I am a Digital marketer + Blogger + managing online marketing strategies for brands big and small. I’m a specialist in brand development, website design, online community management and ongoing web marketing strategy. Whether you need a few hours of consultation, a new website, or ongoing help with your online presence, I can help you with that. I have an innovative spirit, I enjoy what I do, and I bring positive, creative energy to my work!

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