🌺 Iris Data Analysis

Multiples machine learning approaches using the iris dataset

📝 Description

This repository contains a data analysis of the iris dataset using multiple machine learning approaches in Python and Jupyter Notebook. The iris dataset is a multivariate dataset introduced by Ronald Fisher in 1936. It consists of 150 samples from three species of Iris (setosa, virginica, and versicolor) with four measured features: sepal length, sepal width, petal length, and petal width. The dataset is commonly used for data mining, classification, clustering, and algorithm testing purposes.

📁 Files

The following files are included:

• iris.csv - The dataset used for the analysis
• iris_data_analysis.ipynb - The Jupyter Notebook containing the analysis
• data_analysis.py - The Python script containing the analysis

📊 Analysis

The following machine learning approaches were used:

• Decision Trees
• Logistic Regression
• K-Nearest Neighbors
• Support Vector Machines
• Random Forests
• K-Means
• Hierarchical Clustering

The following machine learning metrics were used:

• Pandas
• Numpy
• Matplotlib
• Seaborn
• Scikit-learn

The following files are included:

• iris.csv - The dataset used for the analysis
• iris_data_analysis.ipynb - The Jupyter Notebook containing the analysis
• data_analysis.py - The Python script containing the analysis

💻 Installation

This project requires Python v3.6+ to run.

Install the dependencies and devDependencies and start the server.

pip install -r requirements.txt

📚 References

• Datacamp - For the course Supervised Learning with scikit-learn
• Kaggle - For the dataset Iris Species
• UCI Machine Learning Repository - For the dataset Iris Data Set

📜 License

This project is licensed under the MIT License - see the LICENSE.md file for details