GitHub repository: nitinkumar388/-Logistic-Regression-vs-SVM-with-Titanic-Dataset
Primary language: Jupyter Notebook
Created: 2021-06-10T14:33:33Z
Description:  #  Logistic Regression with Titanic Dataset  In statistics, the logistic model (or logit model) is used to model the probability of a certain class or event existing such as pass/fail, win/lose, alive/dead or healthy/sick.  Logistic regression doesn’t predict the continuous values. Logistic regression predicts whether something is True or False.   When the probability is grater or equal than 0.5, binary value is 1, when the probability is less than 0.5, binary value is 0. So, the person I just mentioned above will be classify as 1, alive. Model returns 1 (True).  https://machinelearningmastery.com/logistic-regression-for-machine-learning/    SVM, I’ll begin by using a linear kernel before using the Gaussian kernel (or the RBF kernel).  A Support Vector Machine (SVM) is a machine learning algorithm that is commonly used for classification problems. It’s based on the idea of finding a hyperplane that divides the dataset into classes. The support vectors are the points nearest the hyperplane – these are the critical points within the dataset.   ## Acknowledgements   - [Awesome Readme Templates](https://awesomeopensource.com/project/elangosundar/awesome-README-templates)  - [Awesome README](https://github.com/matiassingers/awesome-readme)  - [How to write a Good readme](https://bulldogjob.com/news/449-how-to-write-a-good-readme-for-your-github-project)     ## Appendix  The data has been split into two groups:  training set (train.csv) test set (test.csv) The training set should be used to build your machine learning models. For the training set, we provide the outcome (also known as the “ground truth”) for each passenger. Your model will be based on “features” like passengers’ gender and class. You can also use feature engineering to create new features.  The test set should be used to see how well your model performs on unseen data. For the test set, we do not provide the ground truth for each passenger. It is your job to predict these outcomes. For each passenger in the test set, use the model you trained to predict whether or not they survived the sinking of the Titanic     ## Authors  - [@nitinkumar388](https://github.com/nitinkumar388)     ## Badges  Add badges from somewhere like: [shields.io](https://shields.io/)  [![MIT License](https://img.shields.io/apm/l/atomic-design-ui.svg?)](https://github.com/tterb/atomic-design-ui/blob/master/LICENSEs) [![GPLv3 License](https://img.shields.io/badge/License-GPL%20v3-yellow.svg)](https://opensource.org/licenses/) [![AGPL License](https://img.shields.io/badge/license-AGPL-blue.svg)](http://www.gnu.org/licenses/agpl-3.0)     ## Contributing  Contributions are always welcome!  See `contributing.md` for ways to get started.  Please adhere to this project's `code of conduct`.