{"id":28387913,"url":"https://github.com/mayank341/datascienceproject_titanicdataanalysis","last_synced_at":"2025-10-04T07:44:20.064Z","repository":{"id":287840520,"uuid":"965964295","full_name":"mayank341/DataScienceproject_titanicdataanalysis","owner":"mayank341","description":"Exploring the Titanic dataset with data visualization and ML models to predict survival — classic Kaggle project.","archived":false,"fork":false,"pushed_at":"2025-04-26T20:09:54.000Z","size":443,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-05-31T02:26:28.059Z","etag":null,"topics":["aiml","datascience-machinelearning"],"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/mayank341.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":"2025-04-14T07:32:29.000Z","updated_at":"2025-04-26T20:09:57.000Z","dependencies_parsed_at":"2025-04-14T08:38:47.113Z","dependency_job_id":"685c3072-9a8d-48d9-a717-28bcf32bf201","html_url":"https://github.com/mayank341/DataScienceproject_titanicdataanalysis","commit_stats":null,"previous_names":["mayank341/datascienceproject_titanicdataanalysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/mayank341/DataScienceproject_titanicdataanalysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mayank341%2FDataScienceproject_titanicdataanalysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mayank341%2FDataScienceproject_titanicdataanalysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mayank341%2FDataScienceproject_titanicdataanalysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mayank341%2FDataScienceproject_titanicdataanalysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mayank341","download_url":"https://codeload.github.com/mayank341/DataScienceproject_titanicdataanalysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mayank341%2FDataScienceproject_titanicdataanalysis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":278283492,"owners_count":25961310,"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","status":"online","status_checked_at":"2025-10-04T02:00:05.491Z","response_time":63,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["aiml","datascience-machinelearning"],"created_at":"2025-05-30T19:36:07.705Z","updated_at":"2025-10-04T07:44:20.059Z","avatar_url":"https://github.com/mayank341.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# DataScienceproject_titanicdataanalysis\n\n# 🛳️ Titanic Survival Prediction - Data Science Project\n\nThis repository contains a Jupyter Notebook (`_titanic.ipynb`) for a classic machine learning and data science project based on the Titanic dataset. The goal is to predict passenger survival using various machine learning techniques.\n---\n\n## 📁 Project Structure\n\n## 📊 Dataset Information\n\nThe dataset used is from [Kaggle’s Titanic: Machine Learning from Disaster](https://www.kaggle.com/c/titanic). It includes details about the passengers such as:\n\n- PassengerId\n- Survived (Target variable)\n- Pclass (Ticket class)\n- Name\n- Sex\n- Age\n- SibSp (Siblings/Spouses aboard)\n- Parch (Parents/Children aboard)\n- Ticket\n- Fare\n- Cabin\n- Embarked (Port of Embarkation)\n\n---\n\n## 🧪 Project Workflow\n\nThe notebook covers the following steps:\n\n1. **Importing Libraries**  \n   Basic data analysis and ML libraries like `pandas`, `numpy`, `matplotlib`, `seaborn`, `sklearn`.\n\n2. **Data Loading \u0026 Exploration**  \n   Load CSV data, explore structure, identify missing values, and visualize key features.\n\n3. **Data Cleaning \u0026 Feature Engineering**\n   - Handling missing data (e.g., Age, Embarked, Cabin)\n   - Encoding categorical variables (Sex, Embarked)\n   - Creating new features (e.g., FamilySize, IsAlone)\n\n4. **Exploratory Data Analysis (EDA)**  \n   - Correlation heatmap\n   - Survival rate comparisons by class, sex, age\n   - Visualizations using `seaborn` \u0026 `matplotlib`\n\n5. **Model Building**\n   - Train/Test split\n   - Algorithms: Logistic Regression, Decision Trees, Random Forest, KNN, SVM\n   - Model evaluation using accuracy, confusion matrix, cross-validation\n\n6. **Prediction**\n   - Predict on test data (if available)\n   - Export results for submission\n   - \n## 📈 Results\nAccuracy measures how often the model correctly predicts whether a passenger survived or not. It is calculated as:\nAccuracy = (Number of Correct Predictions) / (Total Predictions)\nFor example, if the model predicts correctly for 82 out of 100 passengers, the accuracy is 82%.\n\nThe notebook includes model evaluation and comparison. The best-performing model can be selected for final predictions based on accuracy or cross-validation scores.\n---\n## 🔧 Installation\n\nTo run the notebook locally:\n\n1. Clone this repository  \n   ```bash\nhttps://github.com/mayank341/DataScienceproject_titanicdataanalysis/edit/main/README.md\n   cd DataScience_Titanic\n\n   #\n\n\n# 📘 Explanation of Each Section:\n\n1. **Project Title \u0026 Overview**\n   - A catchy title (`🛳️ Titanic Survival Prediction`) and a brief intro describing what the repo is about.\n\n2. **Project Structure**\n   - Shows how your repo is organized, which is helpful for new contributors.\n\n3. **Dataset Info**\n   - Describes the data source and variables, crucial for understanding what you're working with.\n\n4. **Workflow**\n   - Detailed step-by-step outline of what your notebook does—makes your work reproducible and clear to readers.\n\n5. **Results**\n   - Mentions model evaluations. You can also add charts or accuracy metrics here if desired.\n\n6. **Installation**\n   - Instructions on how to run the notebook on someone else's system. This ensures anyone can use it easily.\n\n7. **Learn More**\n   - Resources for further reading.\n\n8. **Contributing**\n   - Invites collaboration and bug reports.\n\n9. **License**\n   - Defines how others can use your code. Default is MIT, but you can change it.\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmayank341%2Fdatascienceproject_titanicdataanalysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmayank341%2Fdatascienceproject_titanicdataanalysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmayank341%2Fdatascienceproject_titanicdataanalysis/lists"}