- Develop a predictive model using machine learning algorithms to accurately assess and predict the quality of red wines based on various chemical properties and attributes.
- Evaluate and compare the performance of different machine learning techniques to determine the most effective approach for red wine quality prediction, providing insights for potential applications in the wine industry.
- Dataset Link:- Click to get the Dataset
- Enhanced Product Quality: Accurate red wine quality prediction will lead to improved product quality and consistency, enhancing the winery's reputation and customer satisfaction.
- Cost Optimization: Optimal resource allocation and reduced wastage through predictive modeling will result in cost savings for wineries, improving overall operational efficiency.
- Market Competitiveness: Consistent production of high-quality red wines will give wineries a competitive advantage, allowing them to stand out in the market and attract more customers.
- 📚 Importing Libraries: - To perform Data Manipulation,Visualization & Model Building.
- ⏳ Loading Dataset: - Load the dataset into a suitable data structure using pandas.
- 🧠 Basic Understaning of Data: - Generate basic informations about the data.
- 🧹 Data Cleaning: - To clean, transform, and restructure the data in order to make it suitable for analysis.
- 📊 Exploatory Data Analysis: - To identify trends, patterns, and relationships among the variabels.
- 📈 Feature Selection: - To identify most relevant features for model building.
- ⚙️ Data Preprocessing: - To transform data for creating more accurate & robust model.
- 🎯 Model building:- To build predictive models, using various algorithms.
- ⚡️ Model evaluation: - To analyze the Model performance using metrics.
- 🍀 Stacking Model:- To develop a stacked model using the top performing models.
- 🎈 Conclusion: - Conclude the project by summarizing the key findings.
- High Training and Testing Accuracies: The model achieved a high accuracy score near to 90% on the training data, and 87% on the testing data.
- High F1 Score, Recall, and Precision: The model achieved high F1 score, recall, and precision values, all more than 0.8.
- High AUC value more than 0.8: It states that the model demonstrates a reasonably good discriminatory power.
- Overall Model Performance: The model demonstrates strong performance across multiple evaluation metrics, indicating its effectiveness in making accurate predictions and capturing the desired outcomes.
- 💻 Python
- 💻 HTML
- 🐼 Pandas
- 📊 Matplotlib
- 📈 Seaborn
- 📈 Statistics
- 🤖 Scikit-learn
- 🧠 Machine Learning
- 📓 Jupyter Notebook
- 🔗 GitHub
- The project has reached completion, successfully meeting the predefined goals and purposes.
- All project objectives have been accomplished, including end-to-end execution from data collection and preprocessing to model development and evaluation.
Contributions are welcome! If you have any suggestions, bug fixes, or feature additions, please open an issue or submit a pull request.
For any questions or inquiries, please contact kumod.aws@gmail.com or you can contact me on LinkedIn.
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