Learn with case studies on Advertisement Dataset, Diabetes Dataset, Credit Risk using Logistic Regression in R Studio
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:
Gain a solid understanding of logistic regression, a statistical method used for binary classification tasks.
Learn how to preprocess and explore real-world datasets, such as advertisement and diabetes datasets, to prepare them for logistic regression analysis.
Explore various techniques for feature scaling, dimension reduction, and model fitting to optimize logistic regression models for accurate predictions.
Understand how to evaluate the performance of logistic regression models using key metrics like confusion matrices, ROC curves, and area under the curve (AUC).
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.
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.
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