Titanic Survival Prediction

Titanic-Machine-Learning-from-Disaster This repository contains a machine learning project for predicting survival of passengers who travelled on Titanic Ship in 1912. Problem Description- This project highlights my approach to the introductory machine learning competition on Kaggle website- Titanic: Machine Learning from Disaster [1]. The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships. One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class. This project analyses which people were likely to survive. In particular, tools of machine learning have been used to predict which passengers survived the tragedy. Project Description This project has been made in Python v3.4. It uses various data processing, visualisation and machine learning packages such as numpy, pandas, matplotlib, scikit-learn etc. which should be installed if the code is run on a local machine. The project uses a 5 step process (general procedure) for it's predicting task which is as follows [2]: Perform a statistical analysis of the data and look over it's characteristics such as data type of columns, number of instances, correlation of each attribute with the output variable, finding mean and other information about data, correlation matrix etc. After performing statistical analysis, do a visual analysis by plotting the data. Do analyse the scatter_matrix, plot box plots etc. so as to know which attributes are relevant and which are not. Remove irrelevant attributes from the dataset for further analysis. Make a list of all machine learning algorithms that can give good prediction results and spot check each one of them (apply each one of them on the dataset) to find which one is better for prediction. Use k-fold cross validation to calculate performance characteristics of each of the learners (accuracy, precision, recall, area under ROC curve etc.). Take some of the good performing algorithms and perform a grid search/ randomised search over it's hyperparameters to find the optimal hyperparameters for the prediction task. Ensure that the optimal hyperparameters do not overfit the data, by performing k-fold cross validations on learners using these tuned hyperparametes as well. Use an ensemble or Voting Classifier on the above selected algorithms to achieve better performance or use any one of the above algorithm directly to perform predictions. Keep iterating over the above steps again and again and tune them according to the need so as to achieve better performance. File Description titanic_predictor - contains python code for predicting survival. my_solution.csv - contains sample output file generated from algorithm. train.csv- contains training data test.csv - contains testing data for making predictions readme.md - for guide to this project.

Titanic Voting Classifier with 88 per achivement

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🚀 Titanic survival predictor using Random Forest, Logistic Regression, and Voting Classifier ensemble model.

This repository contains my work on the classic Kaggle Titanic Machine Learning competition. Using Python and scikit-learn, I built several machine learning models (Logistic Regression, SVC, Random Forest, and an ensemble Voting Classifier) to predict which passengers survived the Titanic disaster.

An end-to-end Machine Learning pipeline predicting Titanic survival using a Voting Classifier (SVM + Random Forest). Features a FastAPI backend, Docker containerization, and a live web interface deployed on Render.

Titanic - Voting Classifier

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By applying voting classifier got upto 82.089% accuracy

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This project builds a machine learning model to predict passenger survival on the Titanic using the classic Kaggle dataset. The workflow includes comprehensive data preprocessing, feature engineering, and ensemble modeling.

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Using scikit-learn Pipeline and ColumnTransformer to organize the modeling workflow, performing cross-validated hyperparameter tuning (GridSearchCV / RandomizedSearchCV) for LR, SVC, KNN, Decision Tree, and Random Forest, and evaluating an ensemble model via VotingClassifier.

Using Titanic dataset from kaggle to perform a deep-dive EDA with Voting Classifier as machine learning presentation

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Proyek ini bertujuan untuk mengklasifikasikan penumpang kapal Titanic berdasarkan fitur-fitur seperti usia, jenis kelamin, kelas tiket, dan lainnya, guna memprediksi apakah penumpang tersebut selamat atau tidak.

Applying Advanced Classification on titanic dataset and will using Decision Tree Classifier, Random Forest Classifier, AdaBoost, Hyperparameter Tuning by GridSearchCV, Tuning by Randomize Search, Voting Classifier

predict whether the people in titanic was dead or alive using voting classifier and displaying it using flask

A Voting Classifier model (Random Forest + XGBoost) with a Streamlit GUI to predict passenger survival on the Titanic.

Using Titanic dataset from kaggle to perform a deep-dive EDA with Voting Classifier as machine learning presentation

A Random Forest classifier implemented from scratch to predict Titanic survivors. Includes manual construction of decision trees, feature sampling, and ensemble voting.

🚢 Titanic Survival Prediction This project analyzes the Titanic dataset to predict passenger survival using machine learning models. It includes EDA, feature engineering, data preprocessing, and classification models (Logistic Regression, Random Forest, KNN) with a Voting Classifier.

This project uses machine learning to predict Titanic passengers' survival using feature engineering, data preprocessing, model selection, and bagging. A Voting Classifier and Bagging Classifier combine the predictive power of multiple models for better accuracy and robustness. Suggestions for potential future improvements are also included.

In this repository I tried to predict if someone survived the Titanic shipwreck using all the classification algorithm(Logistic Regression,Decision Tree,Random Forest,SVM,KNN,XGBOOST,Voting Classifier).Also performed hyper parameter tuning

Reviewed the Titanic dataset and predicted the survival of the customers by the use of various classification algorithm and then putting it together by the help of Voting Classifier algorithm provided by Scikit Learn.

Given various features, we predict if a person would survive the Titanic. Implementation of various binary classification algorithms such SVM, KNN, Random Forests etc along with GridSearchCV and Voting Classifier to find the best model given the training data set.

Predicting Titanic passenger survival using machine learning. Includes data preprocessing, feature engineering, and a pipeline with a Voting Classifier. Evaluated with cross-validation (~78–79% accuracy). Developed as my third and final Hexsoftwares internship project using Python and scikit-learn.