Iris Dataset Project

This project demonstrates basic data analysis and machine learning techniques using the Iris dataset in R. The Iris dataset is a classic dataset used for various statistical and machine learning applications. It contains 150 observations of iris flowers with four features (sepal length, sepal width, petal length, and petal width) and a target variable (species).

Table of Contents

1. Introduction
2. Technologies Used
3. Getting Started
4. Data Exploration
5. Data Visualization
6. Data Preprocessing
7. Train-Test Split
8. Model Building
9. Model Evaluation
10. Conclusion

Introduction

The Iris dataset consists of measurements of four features (sepal length, sepal width, petal length, and petal width) for 150 iris flowers, along with their species (Setosa, Versicolor, and Virginica). This project involves loading the dataset, exploring it, visualizing it, preprocessing the data, building machine learning models, and evaluating their performance.

Technologies Used

• R: A programming language and environment for statistical computing and graphics.
• RStudio: An integrated development environment (IDE) for R.
• ggplot2: A data visualization package for R that allows for creating complex plots from data in a data frame.
• caret: A package for creating predictive models in R.
• rpart: A package for recursive partitioning and regression trees.

Getting Started

These instructions will help you set up and run the project on your local machine.

Prerequisites

Ensure you have R and RStudio installed on your system. You'll also need the following R packages: ggplot2, caret, rpart, and randomForest. Install these packages if you don't have them already.

Running the Project

1. Clone this repository to your local machine.
2. Open the project in RStudio.
3. Run the code in sequence as described in each section below.

Data Exploration

First, load the Iris dataset and explore its structure and summary statistics. This step involves loading the data and examining its structure, summary statistics, and the first few rows to understand its content and format.

Data Visualization

Visualize the data to understand the distribution and relationships between features. This involves creating pair plots to see relationships between variables, scatter plots to examine individual feature relationships, and box plots to visualize the distribution of each feature by species.

Data Preprocessing

Prepare the data for modeling by checking for missing values and normalizing the features if needed. This step ensures that the data is clean and in a suitable format for model training.

Train-Test Split

Split the dataset into training and testing sets to evaluate the model's performance on unseen data. This step is crucial for validating the model and preventing overfitting.

Model Building

Build a simple decision tree model to classify the species of the iris flowers based on their features. This involves training the decision tree using the training data and visualizing the tree structure.

Model Evaluation

Evaluate the decision tree model on the test data using a confusion matrix. This step measures the model's accuracy and other performance metrics to understand how well it performs on unseen data.

Conclusion

This project demonstrated basic data analysis, visualization, and machine learning techniques using the Iris dataset. By following the steps outlined above, you can gain insights into the dataset and build models to predict the species of iris flowers based on their features. Further improvements can be made by exploring additional models, tuning hyperparameters, and performing more advanced preprocessing.

Feel free to contribute to this project by adding more sophisticated analyses or exploring other machine learning algorithms.