https://github.com/fadlani-aditya/iris-plant-classification
This project focuses on classifying different species of Iris flowers using the Random Forest algorithm. The dataset, sourced from Scikit-learn, contains four key features: sepal length, sepal width, petal length, and petal width, which are used to predict the flower species (Setosa, Versicolor, and Virginica).
https://github.com/fadlani-aditya/iris-plant-classification
agriculture data-science iris-dataset machine-learning python scikit-learn supervised-learning
Last synced: about 2 months ago
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This project focuses on classifying different species of Iris flowers using the Random Forest algorithm. The dataset, sourced from Scikit-learn, contains four key features: sepal length, sepal width, petal length, and petal width, which are used to predict the flower species (Setosa, Versicolor, and Virginica).
- Host: GitHub
- URL: https://github.com/fadlani-aditya/iris-plant-classification
- Owner: fadlani-aditya
- Created: 2025-02-27T15:19:23.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-27T15:46:48.000Z (over 1 year ago)
- Last Synced: 2025-06-11T16:26:41.151Z (about 1 year ago)
- Topics: agriculture, data-science, iris-dataset, machine-learning, python, scikit-learn, supervised-learning
- Language: Python
- Homepage:
- Size: 1.12 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
## iris-plant-classification
# Background Problem
In this project, I aimed to classify Iris plant species using machine learning. The problem lies in accurately distinguishing between Setosa, Versicolor, and Virginica based on their sepal and petal dimensions. Misclassification can affect botanical research, automated plant identification, and precision agriculture.
Situation: The dataset contains various measurements of Iris flowers, and the challenge is to build a model that can accurately classify them.
Task: The goal is to develop a Machine Learning model using Random Forest to classify Iris species with high accuracy. The project involves data preprocessing, model training, and performance evaluation.
# Data Set Characteristics
- **Number of Instances:** 150 (50 samples for each class)
- **Number of Attributes:** 4 numerical, predictive attributes + 1 target class
- **Attribute Information:**
- **Sepal Length** (cm)
- **Sepal Width** (cm)
- **Petal Length** (cm)
- **Petal Width** (cm)
- **Class Label:**
- Iris-Setosa
- Iris-Versicolor
- Iris-Virginica
- **Missing Values:** None
- **Class Distribution:** Each class represents **33.3%** of the dataset
- **Dataset Creator:** R.A. Fisher
- **Dataset Donor:** Michael Marshall *(MARSHALL%PLU@io.arc.nasa.gov)*
- **Date Released:** July 1988
# Insight
From the analysis, I discovered several key insights:
✅ The Random Forest model achieved 100% accuracy, indicating that the dataset is well-structured and separable.
✅ Feature importance analysis showed that petal length and petal width are the most critical factors in species classification.
✅ No misclassifications were observed in the confusion matrix, confirming that the model performs optimally on this dataset.
# Advice
This project can be further expanded into other Machine Learning applications, such as:
🔹 Implementing real-time flower classification using computer vision.
🔹 Experimenting with other ML algorithms (e.g., SVM, Neural Networks) to compare performance.
🔹 Deploying the model as an interactive web app for users to classify flowers based on input measurements.
#DataScientist #Python #MachineLearning #IrisPlant #Agriculture 🚀