https://github.com/hengkoro20/iris-classification-ml
https://github.com/hengkoro20/iris-classification-ml
eda iris-classification machine-learning python
Last synced: 4 months ago
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- Host: GitHub
- URL: https://github.com/hengkoro20/iris-classification-ml
- Owner: Hengkoro20
- Created: 2025-01-28T17:09:08.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-28T17:54:12.000Z (about 1 year ago)
- Last Synced: 2025-08-12T18:45:46.890Z (7 months ago)
- Topics: eda, iris-classification, machine-learning, python
- Language: Jupyter Notebook
- Homepage:
- Size: 22 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 Dataset Classification - Machine Learning
## Description
This project focuses on classifying the Iris dataset using machine learning techniques, specifically decision trees. The dataset is loaded from the `seaborn` library, and various preprocessing and model evaluation steps are performed.
## Goals
1. Load and analyze the Iris dataset.
2. Preprocess data, including handling missing values and label encoding.
3. Train and evaluate decision tree classifiers.
4. Compare different models and interpret feature importance.
5. Visualize the decision tree and performance metrics.
## Insights
1. Decision tree classification is effective for the Iris dataset, achieving high accuracy.
2. Feature importance analysis helps in understanding the influence of each feature on classification.
3. Model pruning and entropy-based splitting enhance the decision tree’s interpretability and performance.
## Steps
- Data loading and basic exploration.
- Data preprocessing: missing value checks and label encoding.
- Splitting dataset into training and testing sets (80:20 ratio).
- Training a decision tree classifier and evaluating performance.
- Comparing different decision tree models with entropy-based criterion and pruning.
- Visualizing the decision tree and analyzing feature importance.
## Model Performance
The best-performing decision tree model achieved:
- **Accuracy:** Above 95%
- **Confusion Matrix & Classification Report:** Indicating strong model performance across all three Iris species.
If you have any suggestions or feedback, feel free to contact me via direct message on LinkedIn or email : hengkorowicaksono@gmail.com and www.linkedin.com/in/hengkoro/
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