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Intro
Developed a wine quality prediction model using Random Forest classifier, leveraging various chemical properties to predict wine quality accurately. The project aimed to predict the quality of wine based on its chemical properties, utilizing a comprehensive dataset from the UCI Machine Learning Repository.
Tools
Link
Features
Data Loading: Loaded the dataset using Pandas for data manipulation.
Data Exploration: Performed initial exploration to understand data distribution and check for missing values.
Visual Analysis: Created visualizations (histograms, bar plots) to explore feature relationships.
Correlation Matrix: Generated a heatmap to visualize feature correlations.
Data Preprocessing: Converted quality ratings into binary labels, split data into training and test sets.
Random Forest Classifier: Trained the model using Random Forest, including optional hyperparameter tuning.
Model Evaluation: Evaluated model performance using accuracy score on the test set.
Predictive System: Built a system to predict wine quality based on input chemical properties.
Build Approach
The approach began with data collection and preparation, followed by exploratory data analysis and visualization to understand feature relationships. Data preprocessing steps included label binarization and train-test splitting. The core of the project involved building and training a Random Forest classifier, optimizing its performance through hyperparameter tuning. Model evaluation was conducted using accuracy scores, and a predictive system was developed for practical application.
Results
The Random Forest model achieved high accuracy in predicting wine quality. The visual analysis provided insights into feature importance and correlations. The predictive system allows for easy input of chemical properties to determine wine quality, making it a valuable tool for winemakers and quality control.
Conclusion
The dataset used in this project is sourced from the UCI Machine Learning Repository. Special thanks to the original dataset creators for providing the data. For more details on my work and other projects, please checkout my portfolio website. If you have any questions or need a similar AI models generated and trained or system for your business, feel free to contact me.
