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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.