Logistic Regression for Predictive Modeling with R
Logistic Regression for Predictive Modeling with R, available at $19.99, has an average rating of 4.5, with 27 lectures, based on 6 reviews, and has 5017 subscribers.
You will learn about Know in detail about logistic regression analysis and its benefits Know about the different methods of finding the probabilities and Understand about the key components of logistic regression Learn how to interpret the modeling results and present it to others Know how to interpret logistic regression analysis output produced by R This course is ideal for individuals who are Anyone who is interested in modeling data and estimate the probabilities of given outcomes. It is particularly useful for Anyone who is interested in modeling data and estimate the probabilities of given outcomes.
Enroll now: Logistic Regression for Predictive Modeling with R
Summary
Title: Logistic Regression for Predictive Modeling with R
Price: $19.99
Average Rating: 4.5
Number of Lectures: 27
Number of Published Lectures: 27
Number of Curriculum Items: 27
Number of Published Curriculum Objects: 27
Original Price: $89.99
Quality Status: approved
Status: Live
What You Will Learn
- Know in detail about logistic regression analysis and its benefits
- Know about the different methods of finding the probabilities and Understand about the key components of logistic regression
- Learn how to interpret the modeling results and present it to others
- Know how to interpret logistic regression analysis output produced by R
Who Should Attend
- Anyone who is interested in modeling data and estimate the probabilities of given outcomes.
Target Audiences
- Anyone who is interested in modeling data and estimate the probabilities of given outcomes.
Welcome to the course “Logistic Regression for Predictive Modeling”! In this course, we will delve into the powerful statistical technique of logistic regression, a fundamental tool for modeling binary outcomes. From analyzing advertisement data to predicting credit risk, you’ll gain hands-on experience applying logistic regression to real-world datasets. Get ready to unlock the predictive potential of your data and enhance your analytical skills!
Section 1: Introduction
This section provides an overview of logistic regression, a powerful statistical technique used for modeling the relationship between a binary outcome and one or more independent variables.
Section 2: Advertisement Dataset
Exploration of a dataset related to advertisements, covering topics such as data preprocessing, feature scaling, and fitting logistic regression models to predict outcomes.
Section 3: Diabetes Dataset
Analysis of a diabetes dataset, including logistic regression modeling, dimension reduction techniques, confusion matrix interpretation, ROC curve plotting, and threshold setting.
Section 4: Credit Risk
Examining credit risk through a dataset involving loan status, applicant income, loan amount, loan term, and credit history. Students learn how to split datasets for training and evaluation purposes.
In this course, students will:
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Gain a solid understanding of logistic regression, a statistical method used for binary classification tasks.
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Learn how to preprocess and explore real-world datasets, such as advertisement and diabetes datasets, to prepare them for logistic regression analysis.
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Explore various techniques for feature scaling, dimension reduction, and model fitting to optimize logistic regression models for accurate predictions.
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Understand how to evaluate the performance of logistic regression models using key metrics like confusion matrices, ROC curves, and area under the curve (AUC).
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Apply logistic regression to practical scenarios, such as credit risk assessment, by analyzing relevant features like dependents, applicant income, loan amount, loan term, and credit history.
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Gain hands-on experience with data manipulation, model building, and evaluation using tools like Python, pandas, scikit-learn, and matplotlib.
Overall, students will develop the skills and knowledge necessary to apply logistic regression effectively in various domains, making data-driven decisions and predictions based on binary outcomes.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction to Logistic Regression
Chapter 2: Advertisement Dataset
Lecture 1: Advertisement Dataset
Lecture 2: Raw Column
Lecture 3: Feature Scaling
Lecture 4: Fitting Logistic Regression Model
Lecture 5: Classifier Scoefficients
Lecture 6: Classifier Scoefficients Continue
Lecture 7: Make Confusion Matrix
Lecture 8: Logistic Regression Training Set
Chapter 3: Diabetes Dataset
Lecture 1: Diabetes Dataset
Lecture 2: Diabetes Dataset – Logistic Regration Model
Lecture 3: Making a Model
Lecture 4: Dimension Reduction
Lecture 5: Confusion Matrix
Lecture 6: Reduce Number of False Positives
Lecture 7: Plot Roc Curv
Lecture 8: Setting Threshold
Lecture 9: Area Under Curve
Chapter 4: Credit Risk
Lecture 1: Credit Risk
Lecture 2: Dataset Loan Dollar Status
Lecture 3: Dependents
Lecture 4: Applicant Income
Lecture 5: Applicant Income Continue
Lecture 6: Loan Amount
Lecture 7: Loan Amount Term
Lecture 8: Credit History
Lecture 9: Spliting Dataset
Instructors
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EDUCBA Bridging the Gap
Learn real world skills online
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- 4 stars: 4 votes
- 5 stars: 2 votes
Frequently Asked Questions
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