This project aims to predict the survival of passengers on the Titanic using machine learning techniques.
The Titanic Survival Prediction project is a classic example used to demonstrate the application of machine learning algorithms. The goal is to predict whether a given passenger survived the Titanic disaster based on various features such as age, gender, and class.
train.csv: The training dataset that contains features and the target variable (Survived).
test.csv: The test dataset that contains features without the target variable.
gender_submission.csv: A sample submission file in the correct format.
Data Loading and Exploration: Load and explore the dataset to understand its structure and features.
Data Preprocessing: Handle missing values, encode categorical variables, and perform feature scaling.
Data Visualization: Visualize the data to gain insights and identify patterns.
Model Training: Train various machine learning models to predict the survival of passengers.
Logistic Regression
Random Forest
Support Vector Machine (SVM)
Model Tuning: Optimize model hyperparameters using techniques like Grid Search or Random Search.
Model Evaluation: Evaluate the performance of the models using metrics such as accuracy, precision, recall, and F1-score.
Submission: Generate predictions on the test set and prepare the submission file.
Python: The main programming language used.
Pandas: For data manipulation and analysis.
NumPy: For numerical computations.
Matplotlib and Seaborn: For data visualization.
Scikit-learn: For machine learning model training and evaluation.
KNIME: For visual workflow and additional data processing.
The best performing model was the Random Forest with an accuracy of [your accuracy].
Hyperparameter tuning improved the model's performance significantly.
Clone the repository: bash Copy code git clone https://github.com/yourusername/titanic-survival-prediction.git Navigate to the project directory: bash Copy code cd titanic-survival-prediction Install the required dependencies: bash Copy code pip install -r requirements.txt Run the Jupyter Notebook: bash Copy code jupyter notebook Open and execute the notebook Titanic_Survival_Prediction.ipynb.
This project is licensed under the MIT License.
Kaggle for providing the Titanic dataset. The contributors and maintainers of the libraries and tools used in this project.