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Kaggle](https://www.kaggle.com/datasets/uciml/biomechanical-features-of-orthopedic-patients/data)\n- Each sample in the dataset represents a patient with six biomechanical attributes:\n  - Pelvic incidence\n  - Pelvic tilt\n  - Lumbar lordosis angle\n  - Sacral slope\n  - Pelvic radius\n  - Grade of spondylolisthesis\n\n---\n\n## 🧰 Libraries Used\n\n- `numpy`\n- `pandas`\n- `matplotlib`\n- `seaborn`\n- `scikit-learn`\n\n---\n\n## 🔍 Exploratory Data Analysis (EDA)\n\n- Checked for imbalanced output classes\n- Pairplot visualizations for feature relationships\n- Correlation matrix to assess feature relationships\n- Box plots for each feature\n- Distribution plots to visualize the spread of values in each column\n\n---\n\n## 🧼 Data Preprocessing\n\n- Checked for missing values\n- Standardized features using `StandardScaler`\n- Balanced the dataset using `RandomOverSampler`\n- Split the dataset into training and testing sets using `train_test_split`\n\n---\n\n## 🤖 Classifiers Used in Voting Ensemble\n\n- **K-Nearest Neighbors (KNN)**\n- **Gaussian Naive Bayes**\n- **Bernoulli Naive Bayes**\n- **Decision Tree**\n\n\n---\n\n## 📉 Testing Metrics\n\n- **Accuracy Score**\n- **Confusion Matrix**\n\n\n---\n\n## 📈 Results\n\n\n| Model           | Accuracy (Train) | Accuracy (Validation) |\n|----------------|------------------|------------------------|\n| Decision Tree  | 0.9441           | 0.9556                 |\n| Naive Bayes    | 0.9058           | 0.9315                 |\n| KNN | 0.9137  | 0.9153                 |\n\n\n\n\n\n![image](https://github.com/user-attachments/assets/bf5d9461-6d6d-4a89-a5d5-bac084490a67)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyahiazakaria445%2Fensemble-learning-voting-classifier","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fyahiazakaria445%2Fensemble-learning-voting-classifier","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyahiazakaria445%2Fensemble-learning-voting-classifier/lists"}