Titanic Dataset EDA and Logistic Regression Model

This repository contains the Exploratory Data Analysis (EDA) and a logistic regression model for the Titanic dataset. The goal of this analysis is to understand the underlying patterns in the data and build a model to predict the survival of passengers.

Table of Contents

• Dataset
• Installation
• EDA Steps
  • Loading the Data
  • Data Cleaning
  • Data Visualization
  • Feature Engineering
• Logistic Regression Model
  • Model Training
  • Model Evaluation
• Results
• Conclusion
• Contributing
• License

Dataset

The dataset used in this analysis is the Titanic dataset, which is available on Kaggle. The dataset provides information on the passengers aboard the Titanic, including whether they survived or not.

Installation

To run this analysis, you need to have Python and the following libraries installed:

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

EDA Steps

Loading the Data

The first step is to load the dataset into a pandas DataFrame.

Data Cleaning

Data cleaning involves handling missing values, converting data types, and correcting erroneous values.

• Handling Missing Values: Identify and fill or drop missing values.
• Converting Data Types: Ensure all columns are of the appropriate data type.
• Correcting Erroneous Values: Check for and correct any obvious data entry errors.

Data Visualization

Visualizing the data helps in understanding the distribution of variables and the relationships between them.

• Univariate Analysis: Histograms, bar plots, and box plots to explore individual variables.
• Bivariate Analysis: Scatter plots, correlation matrices, and pair plots to examine relationships between variables.

Feature Engineering

Feature engineering involves creating new features or modifying existing ones to improve model performance.

• Creating New Features: Example - Extracting the title from the 'Name' column.
• Modifying Existing Features: Example - Converting categorical variables to numerical ones.

Logistic Regression Model

Model Training

We split the dataset into training and testing sets, trained a logistic regression model, and evaluated its performance.

Model Evaluation

We evaluated the model using accuracy, confusion matrix, and classification report.

Results

The results of the EDA and logistic regression model include:

• Insights gained from data visualizations.
• Summary statistics of key variables.
• New features created during the feature engineering process.
• Model performance metrics: accuracy, confusion matrix, and classification report.

Conclusion

The EDA provides a solid foundation for building machine learning models. The logistic regression model achieved promising results, and further improvements can be made by exploring additional features and more complex models.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request or open an Issue to discuss any changes.

License

This project is licensed under the MIT License - see the LICENSE file for details.