{"id":21294560,"url":"https://github.com/al-ghaly/titanic-machine-learning","last_synced_at":"2025-03-15T17:12:36.003Z","repository":{"id":173748042,"uuid":"651228251","full_name":"al-ghaly/Titanic-Machine-Learning","owner":"al-ghaly","description":"Bunch of Machine Learning Classification Models to predict the if a passenger is most likely die","archived":false,"fork":false,"pushed_at":"2023-06-10T09:41:15.000Z","size":454,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-22T06:48:06.642Z","etag":null,"topics":["classification-algorithm","data-science","machine-learning","machine-learning-algorithms"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Titanic-Machine-Learning\n## By MOHAMED ALGHALY\n\n### Applied Models:\n1. **Logistic Regression**\n2. **Decision Tree**\n3. **Random Forest**\n4. **Support Vector Machine (SVM)**\n5. **Ada Boost**\n6. **Gradient Boosting**\n7. **Naive Bayes**\n8. **K-Nearest Neighbor (KNN)**\n\n--- \n\n# I have made the data preprocessing dynamic to enable flexible modeling\n## I implemented the \u003ci\u003etransform\u003c/i\u003e function to clean the data\n### You will just have to specify any parameter to overwrite the default data cleaning as follows:\n![Screenshot (248)](https://github.com/al-ghaly/Titanic-Machine-Learning/assets/61648960/f6587744-2c35-4c0c-a9c4-eca2330e3083)\n![Screenshot (249)](https://github.com/al-ghaly/Titanic-Machine-Learning/assets/61648960/d4777614-8d75-4c64-b2f0-6fb6a91302aa)\n![Screenshot (250)](https://github.com/al-ghaly/Titanic-Machine-Learning/assets/61648960/124b936d-6615-495c-97d1-ad758ab45ee8)\n* ### method: how to handle missing values\n* ### inplace: whether to clean the data as a new dataframe or into the same one\n* ### drop_features: whether or not we want to drop useless features\n* ### combine_rel \u0026 remove: how to handle multicollinearity\n---\n## Attached Files\n* ### train.csv: \n  * The dataset to model (Labeled)\n* ### test.csv: \n  * The dataset to test your model on (UnLabeled)\n* ### Titanic.ipynb: \n  * The Jupyter Notebook for the project\n* ### Titanic.html: \n  * The project's report\n* ### Predictions.csv: \n  * The predections the model made on the unlabeled test data\n* ### scenarios.png: \n  * The possible scenarios to clean the data 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