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Applied Ordinal Encoding for `ShelveLoc`.\n  - Applied One-Hot Encoding for `Urban` and `US`.\n  - Applied Label Encoding for the target variable `Sales`.\n\n### Exploratory Data Analysis (EDA)\n- Analyzed feature distributions and their relationships through visualizations such as KDE plots and bar charts.\n\n### Model Building\n- Trained a Random Forest Classifier with 500 trees and a maximum depth of 10.\n- Evaluated performance using accuracy, classification report, and confusion matrix.\n\n### Execution Time\n- 5.31 seconds.\n\n## Business Impact\n\n- **Strategic Insights**: Provided actionable insights for better marketing strategies and resource allocation.\n- **Decision Support**: Enhanced understanding of sales drivers and customer segmentation.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshubhamsoni98%2Fclassification-with-random-forest-1","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshubhamsoni98%2Fclassification-with-random-forest-1","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshubhamsoni98%2Fclassification-with-random-forest-1/lists"}