The Titanic Analysis project focuses on the analysis of the Titanic dataset, performing data processing using NumPy and Pandas. The project aims to provide insights into the dataset through Univariate Analysis using Seaborn, Bivariate Analysis, correlation matrix generation, fitting a linear regression model, and creating a pair plot of the dataset.
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Data Processing: Utilizing NumPy and Pandas for efficient data processing on the Titanic dataset.
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Column Transformation: Converting columns into NumPy arrays for further analysis.
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Univariate Analysis: Exploring the distribution of individual variables in the dataset using Seaborn.
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Bivariate Analysis: Analyzing the relationship between two variables to uncover patterns and trends.
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Correlation Matrix: Generating a correlation matrix to understand the linear relationship between variables.
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Linear Regression Model: Fitting a linear regression model to the dataset for predictive analysis.
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Pair Plot: Creating a pair plot to visualize pairwise relationships in the dataset.
The project is implemented using Python, with a focus on leveraging NumPy, Pandas, and Seaborn for efficient data manipulation, analysis, and visualization. The code is structured to facilitate readability and understanding.
To get started with the project, follow this:
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Clone the repository to your local machine:
git clone https://github.com/your-username/titanic-analysis.git
The Titanic dataset for providing a historical context for analysis.
NumPy, Pandas, and Seaborn developers for creating essential tools in data science and visualization.