Marketing Analytics: Predicting Customer Churn in Python
Course Report
Find detailed report of this course which helps you make an informed decision on its relevance to your learning needs. Find out the course's popularity among Careervira users and the job roles that would find the course relevant for their upskilling here. You can also find how this course compares against similar courses and much more in the course report.
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Course Features
Duration
4 hours
Delivery Method
Online
Available on
Limited Access
Accessibility
Mobile, Desktop, Laptop
Language
English
Subtitles
English
Level
Intermediate
Teaching Type
Self Paced
Video Content
4 hours
Course Description
Highlights
Pedagogy
Top 10 Percentile
Rating & Reviews
Top 30 Percentile
Parameters
Pedagogy
Acquire all major Python Programming skills in this course for seamless integration into your daily life. Develop a versatile skill set, allowing you to confidently apply what you've learned in various practical scenarios, enhancing your daily experiences and overall proficiency. An exceptional course in Python Programming, this stands out for its Self Paced learning approach. Learners have the flexibility to progress at their own speed, tailoring the experience to their individual needs.
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Rating & Reviews
This highly acclaimed course is among the top-rated in Python Programming, boasting a rating greater than 4 and an overall rating of 5.0. Its exceptional quality sets it apart, making it an excellent choice for individuals seeking top-notch learning experience in Python Programming.
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Course Overview
Virtual Labs
International Faculty
Post Course Interactions
Hands-On Training,Instructor-Moderated Discussions
Skills You Will Gain
Prerequisites/Requirements
Data Manipulation with pandas
What You Will Learn
Learn how to use Python to analyze customer churn and build a model to predict it
You’ll learn how to explore and visualize your data, prepare it for modeling, make predictions using machine learning, and communicate important, actionable insights to stakeholders
By the end of the course, you’ll become comfortable using the pandas library for data analysis and the scikit-learn library for machine learning
Course Instructors
Course Reviews
Average Rating Based on 3 reviews
100%