valerie6048 / titanic-project Goto Github PK

In this final project, the goal is to build a machine-learning model and dashboard for the Titanic Survival Prediction Model based on some parameters. The Dashboard will be built in Streamlit and the model will be using random forest.

• Load the dataset and explore its structure.
• Clean the data by handling missing values, interpolating data, and removing outliers.
• Perform feature engineering if needed.

• Visualize the distribution of Titanic Passenger parameters/features.
• Understand the relationships between different features.
• Analyze statistical summaries, visualizations, and correlations.

• Use machine learning algorithms (e.g., Random Forest, XGBoost, KNN, Logistic Regression) to predict Passenger Survivability.
• Train the models on a training set and evaluate them on a validation set.

• Assess model performance using metrics such as accuracy, confusion matrices, and classification reports.
• Compare different algorithms to select the most suitable one.

• Create a Streamlit dashboard for an interactive user interface.
• Allow users to input new data, and the trained model predicts Passenger Survivability.
• Integrate visualizations, including count plots and heatmaps, into the Streamlit app.

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

Titanic Dataset

• Summarize findings and insights gained from the data.
• Discuss recommendations or further steps, such as refining the model, exploring additional features, or improving data quality.