{"id":28100130,"url":"https://github.com/busradeveci/titanic-randomforest-v1","last_synced_at":"2025-05-13T18:30:27.920Z","repository":{"id":293084994,"uuid":"982893205","full_name":"busradeveci/titanic-randomforest-v1","owner":"busradeveci","description":"Titanic survival prediction using Random Forest classifier as part of Kaggle's beginner-friendly competition.","archived":false,"fork":false,"pushed_at":"2025-05-13T15:12:44.000Z","size":10,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-05-13T16:32:36.095Z","etag":null,"topics":["beginner-project","classification","data-science","kaggle","machine-learning","python","random-forest","titanic-dataset"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/busradeveci.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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,"zenodo":null}},"created_at":"2025-05-13T15:00:23.000Z","updated_at":"2025-05-13T15:14:41.000Z","dependencies_parsed_at":"2025-05-13T16:32:37.959Z","dependency_job_id":"706394b6-3872-44af-b382-bfbc0aca2a1a","html_url":"https://github.com/busradeveci/titanic-randomforest-v1","commit_stats":null,"previous_names":["busradeveci/titanic-randomforest-v1"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/busradeveci%2Ftitanic-randomforest-v1","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/busradeveci%2Ftitanic-randomforest-v1/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/busradeveci%2Ftitanic-randomforest-v1/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/busradeveci%2Ftitanic-randomforest-v1/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/busradeveci","download_url":"https://codeload.github.com/busradeveci/titanic-randomforest-v1/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254003115,"owners_count":21997837,"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":["beginner-project","classification","data-science","kaggle","machine-learning","python","random-forest","titanic-dataset"],"created_at":"2025-05-13T18:30:27.258Z","updated_at":"2025-05-13T18:30:27.898Z","avatar_url":"https://github.com/busradeveci.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Titanic - Random Forest (v1)\n\nThis repository contains my solution to the classic Kaggle competition: **Titanic - Machine Learning from Disaster**. The goal is to predict which passengers survived the Titanic shipwreck using a classification model.\n\n---\n\n## 📊 Overview\n\n- **Competition**: [Titanic - Machine Learning from Disaster](https://www.kaggle.com/competitions/titanic)\n- **Model**: Random Forest Classifier\n- **Public Score**: `0.76076`\n- **Best Score**: `0.76076` (Version 1)\n\n---\n\n## 📁 Dataset\n\nThe dataset includes passenger details such as age, gender, ticket class, number of siblings/spouses aboard, and fare. These features were used to build the model.\n\n---\n\n## 🧹 Data Preprocessing\n\nThe following preprocessing steps were applied:\n\n- Dropped unnecessary columns: `PassengerId`, `Name`, `Ticket`, `Cabin`\n- Filled missing values:\n  - `Age`: Filled with median\n  - `Embarked`: Filled with mode (`'S'`)\n  - `Fare`: Filled with median (only in test set)\n- Converted categorical variables:\n  - `Sex`: Binary mapping\n  - `Embarked`: One-Hot Encoding\n\n---\n\n## 🤖 Model\n\n- **Algorithm**: `RandomForestClassifier` from `sklearn.ensemble`\n- **Training-Validation Split**: 80% training / 20% validation\n- **Selected Features**:\n  - `Pclass`\n  - `Sex`\n  - `Age`\n  - `SibSp`\n  - `Parch`\n  - `Fare`\n  - One-hot encoded `Embarked`\n\nThe model was trained and evaluated using basic performance metrics.\n\n---\n\n## 📈 Results\n\n- Achieved a public Kaggle score of **0.76076**\n- This was the first version of the model and performed well on the leaderboard.\n\n---\n\n## 🚀 Next Steps\n\nPlanned improvements and experiments:\n\n- Try other models (e.g., Logistic Regression, XGBoost)\n- Perform hyperparameter tuning using GridSearchCV\n- Use feature importance to select or engineer better features\n- Consider using cross-validation for more reliable evaluation\n\n---\n\n## 🔗 Resources\n\n- 📓 Kaggle Notebook: [Titanic - Random Forest v1](https://www.kaggle.com/code/busradeveci/titanic-randomforest-v1)\n- 🏆 Competition Page: [Kaggle Titanic](https://www.kaggle.com/competitions/titanic)\n\n---\n\n## 🧑‍💻 Author\nKaggle: [kaggle.com/busradeveci](https://www.kaggle.com/busradeveci)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbusradeveci%2Ftitanic-randomforest-v1","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbusradeveci%2Ftitanic-randomforest-v1","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbusradeveci%2Ftitanic-randomforest-v1/lists"}