Streamlit Link :- Link

Project Description

Titanic Survival Prediction

• Objective: The goal of this project is to predict the survival of passengers on the Titanic based on their attributes such as age, fare, class, sex, and other relevant features.
• Tools: Python, Pandas, NumPy, Scikit-Learn, Jupyter Notebook, Streamlit

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• Flowchart

Below is a simplified flowchart for the Titanic Survival Prediction project:

Start
  |
  V
Data Collection
  |
  V
Data Preprocessing
  |  - Handle Missing Values
  |  - Encode Categorical Features
  |  - Create New Features (Age Groups)
  V
Feature Engineering
  |  - Select Relevant Features
  |  - Prepare Features for Model Training
  V
Model Training
  |  - Train Model with Selected Features
  |  - Use Algorithms like Random Forest, Logistic Regression
  V
Model Evaluation
  |  - Evaluate Model Performance (Accuracy, Precision, Recall)
  |  - Tune Model Parameters
  V
Deployment
  |  - Build Web App using Streamlit
  |  - Integrate Model for Real-time Prediction
  V
End

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Detailed Steps

1. Data Collection:

   • Obtain the Titanic dataset from Kaggle or another trusted source.

2. Data Preprocessing:

   • Handle Missing Values: Fill missing values in columns like Age, Embarked, etc.
   • Encode Categorical Features: Convert features like Sex, Embarked, and Title into numerical values.
   • Create New Features: Generate age group features (Child, Teen, Adult, Middle_Age, Old) based on the Age column.

3. Feature Engineering:

   • Select Relevant Features: Choose features like Pclass, Fare, Age Group, Sex, Embarked, Family_member, and Title.
   • Prepare Features: Ensure features are in the correct format for model training.

4. Model Training:

   • Train Model: Use machine learning algorithms such as Random Forest, Logistic Regression, etc., to train the model.
   • Use Algorithms: Choose the best algorithm based on performance metrics.

5. Model Evaluation:

   • Evaluate Performance: Use metrics like accuracy, precision, recall, and F1-score to evaluate the model.
   • Tune Parameters: Optimize model parameters to improve performance.

6. Deployment:

   • Build Web App: Use Streamlit to create an interactive web application.
   • Integrate Model: Load the trained model into the web app for real-time predictions.