Titanic Machine Learning Model

Overview

This project focuses on building a machine learning model to predict the survival of passengers aboard the Titanic based on various features such as age, gender, ticket class, etc.

Dataset

The dataset used in this project is the famous Titanic dataset, which contains information about passengers including whether they survived or not. The datasets are available in the file.

Files

• train.csv: The dataset containing information about passengers.
• Titanic_project.ipynb: Jupyter Notebook containing the code for data analysis, outlier analysis, exploratory data analysis (EDA), and building the machine learning model.
• requirements.txt: File containing the Python packages required to run the Jupyter Notebook.

Data Analysis

• Exploratory data analysis (EDA) is performed to understand the structure and characteristics of the dataset.
• Outlier analysis is conducted to identify and handle outliers in the data.

Machine Learning Model

• A machine learning model is built using all 3 boosting methods; ADA Boosting, Gradient Boosting and XGboosting.
• All 3 models are trained on a portion of the dataset and evaluated using appropriate metrics such as accuracy, precision, recall, and F1-score.
• Hyperparameter tuning is performed to optimize the model's performance.

Results

• The performance of the machine learning model is assessed.
• The model's predictions are compared with actual survival outcomes to evaluate its effectiveness.
• Around %80 accuracy is achieved with all 3 boosting methods. Then comparison chart for different metrics are given at the end with conclusion.

• Usage

1. Clone the repository:

   git clone https://github.com/Mamoth111/Ml_titanic_3model_comparison.git