This project focuses on analyzing the famous Titanic dataset using linear regression and logistic regression models. The goal is to predict whether a passenger survived or perished based on various features such as age, gender, passenger class, and more.
The Titanic dataset is a well-known dataset in the field of data science and machine learning. It contains information about passengers aboard the Titanic, including their demographic details and whether they survived the tragic event. The dataset is widely used for exploring data analysis techniques and building predictive models.
This project utilizes two popular regression algorithms: linear regression and logistic regression.
Linear Regression: Linear regression is a statistical approach used to model the relationship between a dependent variable and one or more independent variables. In this project, linear regression is applied to explore the correlation between certain features and the passengers' age, fare, or any other continuous variable of interest.
Logistic Regression: Logistic regression is a classification algorithm used to predict binary outcomes, such as whether a passenger survived or not. By fitting the logistic regression model to the Titanic dataset, we can determine the influence of different factors on the survival probability.
Data Preprocessing: The initial step involves data cleaning, handling missing values, and transforming categorical variables into numerical representations. This ensures the data is suitable for analysis and model training.
Exploratory Data Analysis: Exploring the dataset helps to gain insights into the distribution of variables, identify patterns, and discover any relationships between the features and the target variable (survival).
Model Training and Evaluation: The dataset is divided into training and testing sets. Linear regression is used to analyze continuous variables, while logistic regression is applied to predict survival outcomes. The models are trained on the training set and evaluated using appropriate metrics to assess their performance.
Results and Conclusion: The project concludes with a summary of the findings, including insights gained from the data analysis and the predictive power of the regression models. Additionally, any limitations or future improvements that can be made to enhance the accuracy of the models are discussed.
To get started with this project, you can clone the repository and access the Jupyter Notebook or Python script that contains the code and detailed implementation steps. The dataset used in this project is included, along with any necessary dependencies.
Feel free to explore the code, modify it, and experiment with different regression techniques to further enhance your understanding of the Titanic dataset.