Titanic Kaggle Challenge

This project aims to predict passenger survival on the Titanic using machine learning. The dataset is part of the Titanic: Machine Learning from Disaster competition on Kaggle.

Project Overview

In this project, we use the Titanic dataset to predict whether a passenger survived or not based on various features like age, sex, class, and the number of siblings/spouses aboard. The dataset consists of both categorical and numerical data, which we preprocess before training a machine learning model.

Key Steps:

• Data Preprocessing: Clean and prepare the data by handling missing values, encoding categorical variables, and splitting data for training and testing.
• Exploratory Data Analysis (EDA): Visualize and explore the dataset to gain insights into the relationships between the features and survival rate.
• Model Training: Train a Random Forest Classifier model to predict survival based on the features in the dataset.
• Evaluation: Evaluate the model's performance using accuracy score and confusion matrix.

Requirements

To run this project, you need the following Python libraries:

• numpy
• pandas
• seaborn
• matplotlib
• scikit-learn

You can install the required libraries using the following command:

pip install -r requirements.txt