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https://github.com/anidipta/r
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- Host: GitHub
- URL: https://github.com/anidipta/r
- Owner: Anidipta
- License: apache-2.0
- Created: 2024-08-18T15:51:18.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-10-21T19:18:42.000Z (3 months ago)
- Last Synced: 2024-10-22T06:59:28.994Z (3 months ago)
- Language: R
- Size: 1.35 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 📊 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.
```r
install.packages("caret")
install.packages("C50")
install.packages("modeldata")
```
Load the libraries:
```r
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.
---