📊 R Script for Data Analysis

Welcome to this data analysis project! Below is a summary of the tasks and analyses conducted using R across multiple days.

🛒 Association Rule Mining with GrocBinary24.csv

Day 4 Tasks

1. Frequent Itemsets:

   • 🔍 Identified all frequent itemsets with a minimum support of 30%.

2. Association Rules:

   • 📏 Extracted rules with at least 40% support and 60% confidence.

3. Advanced Rules:

   • ⚖️ Found rules with at least 30% support, 70% confidence, and lift > 1.

🚗 Analyzing the auto-mpg.csv Dataset

Day 4 Tasks

1. Data Loading and Initial Exploration:

   • 📄 Loaded the dataset and displayed the first few rows.
   • 📊 Showed the number of rows, columns, and summary statistics.
   • 🏷️ Listed the column names.

2. Working with Factors:

   • 🔄 Converted the cylinders column to a factor with descriptive labels.

3. Visualizations:

   • 📈 Histogram of Acceleration: Plotted to show the distribution of acceleration.
   • 📉 Histogram of MPG: Plotted to show the distribution of miles per gallon.
   • 📊 Barplot of MPG: Visualized the miles per gallon as a bar plot.
   • 🔢 Frequency Count of Cylinders: Counted and plotted the frequency of each cylinder type.

4. Boxplots:

   • 📦 MPG Distribution: Created a boxplot for MPG.
   • 🚗 MPG by Cylinders: Boxplot of MPG grouped by the number of cylinders.

5. Pair Plots:

   • 🔗 MPG vs. Displacement: Pair plot to explore relationships between MPG and displacement.
   • 🔗 MPG, Displacement, and Horsepower: Pair plot to explore relationships among MPG, displacement, and horsepower.

---

📊 Decision Trees with C5.0

Day 7 Overview

This section focuses on decision trees using the C5.0 algorithm. We will explore concepts like accuracy, sensitivity, and specificity of classifiers using different training/test splits on multiple datasets.

Requirements

Make sure you have the following libraries installed:

• caret: For creating and evaluating classification models.
• C50: To implement the C5.0 algorithm for decision trees.
• modeldata: To use sample datasets for training and testing.

install.packages("caret")
install.packages("C50")
install.packages("modeldata")

Load the libraries:

library(caret)
library(C50)
library(modeldata)

🔍 Problems and Solutions

This repository focuses on solving common problems such as:

• Training and testing C5.0 models with different data splits.
• Analyzing accuracy, sensitivity, and specificity for training and test sets.
• Evaluating models with and without rules.
• Comparing model performance across various training partitions: 40%, 50%, 60%, 70%, and 80%.

---

🔧 How to Use the Analysis Scripts

1. Run the Association Rule Mining:

   • Load the GrocBinary24.csv dataset and execute the analysis.

2. Analyze the auto-mpg.csv Dataset:

   • Load the dataset and run the visualizations and boxplots.

3. Implement Decision Trees:

   • Follow the requirements to set up the environment and execute the C5.0 decision tree analyses.

---