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In this project, a powerful K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms are leveraged to predict passenger survival in the Titanic dataset. By employing Stratified K-Fold Cross-Validation, we ensure that the data is divided into folds while maintaining the same distribution of survival and non-survival instances in each fold.
My work using Microsoft Excel using various functions such as IFs and VLOOKUP with the help of Titanic dataset courtesy of Kaggle. - iamAdrianC/Excel_and_Titanic_Dataset
Titanic-Dataset. The sinking of the Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 ...
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Oct 8, 2023 · AishwaryaHoysal24 / Titanic_Classification. Titanic classification predicts survival based on passenger data. Using machine learning algorithms, it analyzes features like age, gender, and class in a labeled dataset. The goal is to create a model that accurately classifies new data, reflecting the likelihood of survival in a Titanic-like scenario.
sibsp: # of siblings / spouses aboard the Titanic; parch: # of parents / children aboard the Titanic; ticket: Ticket number; cabin: Cabin number; embarked: Port of Embarkation C = Cherbourg, Q = Queenstown, S = Southampton; Total rows and columns. We can see that there are 891 rows and 12 columns in our training dataset.
Performed Logistic Regression ( for Classification between two classes that is Survived and Dead) for Titanic Dataset. We here shape and arrange our TRAIN and TEST datasets. Later we split the TRAIN dataset into x_train,x_test,y_train,y_test and apply Logistic Regression algorithm. Algorithm gives 0.79 score on x_test,y_test.
