{"id":25441935,"url":"https://github.com/arif-miad/obesity-level-classification-using-machine-learning","last_synced_at":"2026-05-01T18:32:37.561Z","repository":{"id":277580119,"uuid":"932876339","full_name":"Arif-miad/Obesity-Level-Classification-using-Machine-Learning","owner":"Arif-miad","description":null,"archived":false,"fork":false,"pushed_at":"2025-02-14T17:34:29.000Z","size":2281,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-05-15T19:09:39.338Z","etag":null,"topics":["classification","exploratory-data-analysis","feature-engineering","machine-learning","matplotlib","numpy","pandas","python","sklearn"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Arif-miad.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-02-14T17:23:43.000Z","updated_at":"2025-02-14T17:34:32.000Z","dependencies_parsed_at":"2025-02-14T18:39:47.591Z","dependency_job_id":null,"html_url":"https://github.com/Arif-miad/Obesity-Level-Classification-using-Machine-Learning","commit_stats":null,"previous_names":["arif-miad/obesity-level-classification-using-machine-learning"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Arif-miad%2FObesity-Level-Classification-using-Machine-Learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Arif-miad%2FObesity-Level-Classification-using-Machine-Learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Arif-miad%2FObesity-Level-Classification-using-Machine-Learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Arif-miad%2FObesity-Level-Classification-using-Machine-Learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Arif-miad","download_url":"https://codeload.github.com/Arif-miad/Obesity-Level-Classification-using-Machine-Learning/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254404345,"owners_count":22065641,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["classification","exploratory-data-analysis","feature-engineering","machine-learning","matplotlib","numpy","pandas","python","sklearn"],"created_at":"2025-02-17T13:16:04.305Z","updated_at":"2026-05-01T18:32:35.655Z","avatar_url":"https://github.com/Arif-miad.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Obesity Level Classification using Machine Learning\n\n## 📌 Overview\nThis project aims to predict obesity levels in individuals from **Mexico, Peru, and Colombia** based on their eating habits and physical conditions. The dataset consists of **2111 records** with **17 attributes**, including details like BMI, family history, eating habits, physical activity, and transportation mode.\n\n## 📊 Dataset Features\n- **Gender:** Male/Female\n- **Age, Height, Weight:** Physical attributes\n- **Eating Habits:** Frequency of high-caloric food, vegetable intake, water consumption, snacking behavior\n- **Lifestyle Factors:** Smoking, alcohol consumption, exercise frequency, screen time, transportation mode\n- **Target Variable:** **Obesity Level** (7 categories: Insufficient Weight, Normal Weight, Overweight I \u0026 II, Obesity I, II \u0026 III)\n\n---\n## 🚀 Project Workflow\n\n### **1️⃣ Exploratory Data Analysis (EDA)**\n- **Univariate Analysis:** Distribution of numerical \u0026 categorical features\n- **Bivariate Analysis:** Relationship between variables using scatterplots, box plots, and bar plots\n- **Correlation Analysis:** Heatmaps to identify feature relationships\n- **Outlier Detection:** Identifying extreme values in numerical features\n\n### **2️⃣ Data Preprocessing \u0026 Feature Engineering**\n- Encoding categorical variables using **Label Encoding**\n- Feature creation: **Body Mass Index (BMI)**\n- Train-test split (80-20 ratio)\n- Scaling numerical features using **StandardScaler**\n\n### **3️⃣ Machine Learning Model Training (10 Models)**\nWe implemented and compared **10 classification models:**\n- Logistic Regression\n- Random Forest\n- Gradient Boosting\n- Support Vector Machine (SVM)\n- K-Nearest Neighbors (KNN)\n- Decision Tree\n- Naïve Bayes\n- XGBoost\n- LightGBM\n- CatBoost\n\n### **4️⃣ Model Evaluation \u0026 Performance Metrics**\nEach model was evaluated using:\n- **Accuracy, F1 Score, ROC-AUC Score**\n- **Confusion Matrix for Misclassification Analysis**\n- **Classification Report for Precision, Recall, F1-Score**\n- **Feature Importance Analysis** for tree-based models\n\n### **5️⃣ Model Comparison \u0026 Optimization**\n- Performance comparison across models\n- Hyperparameter tuning using **GridSearchCV \u0026 RandomizedSearchCV**\n\n---\n## 📌 Results \u0026 Insights\n- **Best Performing Model:** Identified based on accuracy \u0026 ROC-AUC\n- **Feature Importance:** Key factors influencing obesity prediction\n- **Impact of Lifestyle Factors:** Strong correlation with obesity levels\n\n---\n## 📂 Repository Structure\n```\n📦 Obesity_Level_Classification\n├── 📁 data                 # Dataset \u0026 Preprocessed Files\n├── 📁 notebooks            # Jupyter Notebooks for EDA \u0026 Modeling\n├── 📁 models               # Trained Machine Learning Models\n├── 📜 obesity_classification.py   # Main Code Implementation\n├── 📜 README.md            # Project Documentation\n```\n\n---\n## 📌 Kaggle Notebook \u0026 LinkedIn Profile\n🔗 **Kaggle Notebook:** [Check it out here](https://www.kaggle.com/code/arifmia/predicting-obesity-levels-using-machine-learning)\n\n🔗 **LinkedIn Profile:** [Connect with me](www.linkedin.com/in/arif-miah-8751bb217)\n\n---\n## 💡 Future Improvements\n- **Deep Learning Approach:** Experimenting with Neural Networks\n- **More Features:** Incorporating dietary habits, sleep patterns, and medical history\n- **Deployment:** Creating a web-based prediction tool using Flask or Streamlit\n\n### ⭐ **If you found this helpful, don't forget to star the repository!** ⭐\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farif-miad%2Fobesity-level-classification-using-machine-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Farif-miad%2Fobesity-level-classification-using-machine-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farif-miad%2Fobesity-level-classification-using-machine-learning/lists"}