Welcome to the Titanic Survival Detection project! This project focuses on analyzing the Titanic dataset to predict passenger survival. It involves data preprocessing, exploratory data analysis, and building a machine learning model to predict survival probabilities.
The sinking of the 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, resulting in the deaths of a large number of passengers and crew. In this project, we aim to analyze the Titanic dataset, which contains information about passengers aboard the Titanic, to predict which passengers survived the disaster. The dataset used in this project is the famous titanic-dataset.csv, which is widely used for machine learning and data analysis purposes. It contains various features such as passenger class, age, sex, fare, and whether the passenger survived or not.
This section involves cleaning the dataset, handling missing values, and converting categorical variables into numerical format.
Here, we explore the dataset to gain insights into the relationships between different features and the survival outcome. Visualizations such as bar plots, and correlation matrices are used for analysis.
In this section, we build a predictive model using machine learning algorithms such as logistic regression. The model is trained on a portion of the dataset and evaluated on another portion to assess its performance.
The final model achieves 87% on the test set, demonstrating its effectiveness in predicting passenger survival.
Through this project, I have successfully analyzed the Titanic dataset and built a machine-learning model to predict passenger survival. This project serves as a demonstration of data preprocessing, exploratory data analysis, and predictive modeling techniques.
Thank you for your interest in the Titanic Survival Detection project!
For any inquiries or feedback regarding this project, please contact:
Avile Bantwini (LinkedIn)
Email: bantwiniavile@gmail.com