Titanic_Survival_Prediction

The goal of this project is to predict whether a passenger survived or not based on features such as their age, gender, ticket class, and more, with a focus on the Logistic Regression model for classification.

Project Workflow

1.Data Preprocessing:

• Loading the dataset
• Handling missing values
• Encoding categorical variables

2.Exploratory Data Analysis (EDA):

• Visualizing the relationship between features and survival

Model Training and Evaluation:

• Training machine learning models such as Logistic Regression, Decision Trees, or Random Forest

• Evaluating model performance using metrics like accuracy and F1-score

  Predictions:

• Generating survival predictions on the test set

  Run the Notebook

• Clone the repository or download the notebook file.

• Ensure you have Python installed along with the required dependencies.

• Open the notebook in Jupyter Notebook or any compatible environment.

• Follow the cells step-by-step to execute the workflow.