Logistic Regression in R Studio

You’re looking for a comprehensive Classification modelling course that will teach you everything you need to know in order to create a Classification model in R, correct? You’ve found the right R studio Classification modelling assignment help covering logistic regression, LDA, and kNN!
  • You will be able to do the following after finishing this course:
  • In R, create various Classification modelling models and compare their performance.
  • Practice, discuss, and comprehend Machine Learning concepts with confidence.

KEY FEATURE

In order to facilitate more efficient high-level data processing, the R programming language has an integrated development environment suite called RStudio.

It can process large datasets in memory and draw meaningful conclusions from them. It can run locally or in a web browser via an internet connection to RStudio Server.

Advanced collaboration and security features are available in the commercial edition, which is in addition to the open-source version.

The free version can perform full analytics, from connecting to APIs and ingesting data to making and sharing visualisations.

Logistic Regression in R Studio Assignment Help

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Key Topics

    Logistic Regression in R Studio

    Regression analysis is a statistical tool used to understand relationships between more than one variable. A dependent variable and more than one or one independent variable are included in this type of regression analysis. These independent variables are also known as ‘predictors.’ All of this is done to determine how a change in one variable can affect another. This is simply defined as a reduction in expenses resulting in a greater amount of savings.
    In addition, the analyst determines the ‘statistical significance’ of projected relationships. A number of multiple regression techniques are considered as key to ‘Econometrics’ and find extensive applications in assessing the trends and forecasting. Regression analysis provides a number of insights into customer behaviour while also evaluating various profitability parameters. Regression analysis is used extensively in academic settings. Contact StatisticsOnlineAssignmentHelp.com writers if you need Regression assignment assistance. They offer students the best assignment service in accordance with university guidelines.

    How Will Our Services Benefit You?

    All students who complete this Machine learning basics course receive a verifiable Certificate of Completion. If you are a business manager, executive, or student who wants to learn and apply machine learning in real-world business problems, this course will provide you with a solid foundation by teaching you the most popular machine learning classification techniques, such as Logistic Regression, Linear Discriminant Analysis, and KNN. This course covers all of the steps that should be taken when using classification techniques to solve a business problem.
    Most courses only teach how to run the analysis, but we believe that what happens before and after running analysis is even more important, i.e. before running analysis, you must have the correct data and perform some pre-processing on it. And, after running the analysis, you should be able to judge how good your model is and interpret the results in order to actually help your business.

    Following are the topics covered by our experts:

    1. Basics Of Statistics

    • Describing data Graphically
    • Measures of Centers
    • Measures of Dispersion

    2. Getting Started with R and R Studio

    • Basics of R and R studio
    • Packages in R
    • Inputting data part 1: Inbuilt datasets of R
    • Inputting data part 2: Manual data entry
    • Inputting data part 3: Importing from CSV or Text files
    • Creating Barplots in R
    • Creating Histograms in R

    3. Gathering Business Knowledge

    • Data Exploration
    • The Data and the Data Dictionar
    • Importing the dataset into R
    • Univariate analysis and EDD
    • EDD in R
    • Outlier Treatment in R
    • Missing Value imputation in R
    • Project Exercise 4
    • Seasonality in Data
    • Variable transformation in R
    • Dummy variable creation: Handling qualitative data
    • Dummy variable creation in R

    4. Classification Models

    • Three Classifiers and the problem statement
    • Why can’t we use Linear Regression?
    • Logistic Regression
    • Training a Simple Logistic model in R
    • Results of Simple Logistic Regression
    • Logistic with multiple predictors
    • Training multiple predictor Logistic model in R
    • Confusion Matrix
    • Evaluating Model performance
    • Predicting probabilities, assigning classes and making Confusion Matrix

    Linear Discriminant Analysis

    1) Test-Train Split

    • More about test-train split
    • Test-Train Split in R

    2) K-Nearest Neighbors classifier

    • K-Nearest Neighbors in R

    3) Understanding The Results

    • Understanding the results of classification models
    • Summary of the three models

    What All Can You Learn From Our Logistic Regression In R Studio Experts

    • Learn how to interpret the results of a Logistic Regression model and turn them into useful information.
    • In R studio, you can learn about linear discriminant analysis and the K-Nearest Neighbors technique.
    • Learn how to solve real-world problems using various classification techniques.
    • Implement a Machine Learning algorithm to predict future outcomes based on past data.
    • Expertise in data collection and preprocessing for the Machine Learning logistic regression problem
    • The course includes a complete DIY project to put your lecture learnings into action.
    • Data visualisation in R before and after analysis
    • How to perform fundamental statistical operations in R

    Logistic Regression in R Studio Homework Help

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