T-SURVIVAL-PREDICTION-MODEL

Titanic Dataset Analysis and Machine Learning

This Jupyter Notebook contains an analysis of the Titanic dataset and the development of a machine learning model to predict passenger survival.

Description

The notebook explores the Titanic dataset, performs data cleaning and preprocessing, and builds a machine learning model to predict passenger survival. The analysis includes:

• Exploratory data analysis: Investigating the data to understand the distributions of different variables, identify patterns, and detect potential issues like missing values.
• Data cleaning: Handling missing values and converting categorical variables into numerical representations.
• Feature engineering: Selecting and transforming features to improve model performance.
• Model training: Splitting the data into training and testing sets and training a machine learning model.
• Model evaluation: Evaluating the performance of the trained model using appropriate metrics.

Dataset

The Titanic dataset contains information about passengers on the Titanic, including their demographics, ticket information, and survival status.

Libraries used

• pandas
• seaborn
• scikit-learn

Steps

1. Load the Titanic dataset using pandas.
2. Perform exploratory data analysis to understand the data.
3. Clean the data by handling missing values and converting categorical variables.
4. Engineer relevant features for the machine learning model.
5. Split the data into training and testing sets.
6. Train a machine learning model (the specific model used is not specified in the code).
7. Evaluate the performance of the trained model.

Notes

• The code includes comments explaining each step.
• Further analysis and model optimization can be performed.