- Review the report to gain a deeper understanding of the work.
- The dataset is sourced from Kaggle.
- PCA was used for feature extraction, while ANOVA, RFE, and K-Best were applied for feature selection.
- Familiarize yourself with the code.
- Modify it to suit your specific requirements.
-
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"Prediction-of-Bike-Sharing-Demand" is a project that uses machine learning to maximize urban mobility. To forecast demand for bike sharing, it uses models such as Linear Regression, Lasso Regression, Ridge Regression, Decision Tree, Random Forest, Gradient Boosting, and XGBoost.
SumaiyaShejin/Prediction-of-Bike-Sharing-Demand
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"Prediction-of-Bike-Sharing-Demand" is a project that uses machine learning to maximize urban mobility. To forecast demand for bike sharing, it uses models such as Linear Regression, Lasso Regression, Ridge Regression, Decision Tree, Random Forest, Gradient Boosting, and XGBoost.
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