🚀 Logistic Regression and Naive Bayes Classification in R

This repository contains R scripts for performing logistic regression and Naive Bayes classification on various datasets. The scripts demonstrate data loading, visualization, model training, prediction, and evaluation.

🗃️ Files

• logistic_regression.r: This script performs logistic regression on two datasets: admissions.csv and diabetesmodel.csv.
• kmean_algorithm.r: This script performs k-means clustering on the Iris dataset.
• naive_bayes.r: This script performs Naive Bayes classification on the golf_df.csv dataset.

Dependencies

The scripts require the following R packages:

• ggplot2
• caTools
• e1071
• class

You can install these packages using the following commands:

install.packages("ggplot2")
install.packages("caTools")
install.packages("e1071")
install.packages("class")

Usage

Logistic Regression

The logistic_regression.r script demonstrates logistic regression on two datasets:

1. Admissions Dataset (admissions.csv)

   • Visualizes the relationship between GPA and admission status.
   • Trains a logistic regression model to predict admission status based on GPA and rank.
   • Evaluates the model using a confusion matrix and calculates accuracy.

2. Diabetes Dataset (diabetesmodel.csv)

   • Visualizes the relationship between glucose levels and diabetes outcomes.
   • Trains a logistic regression model to predict diabetes outcomes based on various health metrics.
   • Evaluates the model using a confusion matrix and calculates accuracy.

K-Means Clustering

The kmean_algorithm.r script performs k-means clustering on the Iris dataset:

• Loads the Iris dataset and applies k-means clustering with 3 clusters.
• Visualizes the clustering results and compares them with the actual species classification.

Naive Bayes Classification

The naive_bayes.r script demonstrates Naive Bayes classification on the golf_df.csv dataset:

• Loads the dataset and trains a Naive Bayes model to predict the target variable based on the first four columns.
• Evaluates the model using a confusion matrix and makes predictions on new data.

Example

To run the logistic regression script, use the following command in R:

source("logistic_regression.r")

To run the k-means clustering script, use the following command in R:

source("kmean_algorithm.r")

To run the Naive Bayes classification script, use the following command in R:

source("naive_bayes.r")