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https://github.com/danicaalana/wine-dataset-decision-tree
This project is developed as part of Digital Skill Fair (DSF) 35.0 - Data Science by Dibimbing. I am using Wine Recognition Dataset from scikit-learn, which is the results of a chemical analysis of wines grown in the same region in Italy by three different cultivators.
https://github.com/danicaalana/wine-dataset-decision-tree
data data-analysis-python data-science decision-tree-classification machine-learning python scikit-learn wine-dataset
Last synced: 3 days ago
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This project is developed as part of Digital Skill Fair (DSF) 35.0 - Data Science by Dibimbing. I am using Wine Recognition Dataset from scikit-learn, which is the results of a chemical analysis of wines grown in the same region in Italy by three different cultivators.
- Host: GitHub
- URL: https://github.com/danicaalana/wine-dataset-decision-tree
- Owner: DanicaAlana
- Created: 2025-01-24T13:13:37.000Z (27 days ago)
- Default Branch: main
- Last Pushed: 2025-02-17T03:18:39.000Z (4 days ago)
- Last Synced: 2025-02-17T04:24:13.330Z (4 days ago)
- Topics: data, data-analysis-python, data-science, decision-tree-classification, machine-learning, python, scikit-learn, wine-dataset
- Language: Jupyter Notebook
- Homepage:
- Size: 4.87 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# **DSF 35.0 Data Science Portfolio**
## **Wine Recognition Classification Using Decision Tree**
This project is developed as part of Digital Skill Fair (DSF) 35.0 - Data Science by Dibimbing. I am using Wine Recognition Dataset from scikit-learn, which is the results of a chemical analysis of wines grown in the same region in Italy by three different cultivators. There are thirteen different measurements taken for different constituents found in the three types of wine. You can access the dataset through this link: https://scikit-learn.org/1.5/datasets/toy_dataset.html#wine-recognition-dataset.
**Goals:**
1. Exploring the Wine dataset to understand the structure and basic information.
2. Building a prediction model using the Decision Tree Classifier algorithm.
3. Evaluating model performance based on predictions against test data.
4. Visualizing analysis results to understand data patterns or model performance.
**Insights:**
1. Can identify certain patterns in the data, such as what physical characteristics are most influential in determining the type of wine.
2. Demonstrate how good the Decision Tree Classifier model is at predicting wine types. Metrics such as accuracy will be used to measure the performance of the model.
3. Identify which features are most important in predicting wine types.
**Conclusion**
The trained Decision Tree model was able to classify wine types with very high accuracy. These results show that Decision Tree is an effective model for wine type classification, with an accuracy of 94%.
If you have any questions, suggestions or feedbacks, please do not hesitate to reach me out through Email or LinkedIn: [email protected] or https://www.linkedin.com/in/danicaas