Logistic Regression in Python

This course will focus on providing exposure to building a logistic regression model and interpreting the results of the model as well.

  • icon-videos 2 Videos
  • icon-data 1 Coding Case
  • icon-reading 2 Readings
  • icon-quiz 1 Quiz
Logistic Regression in Python (1)
  • 2 bar graph
    Difficulty: Intermediate

    Foundational knowledge or experience in statistics or analytics is recommended.

  • Asset 1
    Duration: Approximately 4 hours

Course Overview

  • Logistic regression is one of the most popular and widely used regression techniques in statistics and machine learning, widely used across industry domains and verticals.
  • This robust technique is often used to describe the relationship between a binary predicted variable and a set of predictor variables.
  • Learn the theoretical concepts behind logistic regression and build and evaluate a robust model on a real-world dataset to solve a business objective.
  • Gain practical experience by applying the coding skills and understanding on real-world business scenarios.

What’s included

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Lifetime Access

Access this project for life once completed

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Flexible Scheduling

Start learning online immediately, at your own pace

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Desktop Only

We recommend completing this project on a desktop

Skills You Will Learn

Logistic Regression

Data Preprocessing

Exploratory Data Analysis

Model Building

Model Evaluation and Validation

Variable Selection

Logistic Modeling

Associated Learning Tracks

Syllabus

Fundamentals of Logistic Regression in Python – Model Building in statsmodels, Model Building in sklearn and Model Evaluation.

How it Works

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