Who Would Survive The Titanic: The Sinking Of The RMS

Project 1who Would Survive The Titanicthe Sinking Of The Rms Titanic

PROJECT 1 Who would survive the Titanic? The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This tragic event shocked the international community and led to significant improvements in maritime safety regulations. One critical factor that contributed to the high mortality rate was the insufficient number of lifeboats for all onboard.

Although survival involved a degree of luck, certain groups of people, such as women, children, and individuals from the upper social classes, had higher survival probabilities. Recognizing these patterns, this project aims to analyze historical data to predict which passengers were more likely to survive. The goal is to develop and compare predictive models using different data analytics techniques, focusing on classification accuracy.

For this purpose, three datasets are provided: titanic_train.csv for training and validating models, and titanic_heldout.csv for testing the models’ predictions. The models to be implemented include Linear Regression, Decision Trees, Nearest Neighbors, and Clustering. The primary objective is to maximize predictive accuracy, with bonus points awarded for the most effective algorithms.

Students can work alone or with one partner. In the case of joint work, only one submission is required. The submission should include a comprehensive report detailing the methods used, instructions for running the code, and performance metrics such as confusion matrices and accuracy scores for each model. Additionally, source code files should be submitted, along with prediction files for each model, each containing a single column indicating whether a passenger survived (1) or not (0).

Paper For Above instruction

The sinking of the Titanic remains a historical landmark illustrating the profound impact of social, economic, and technical factors on survival probabilities during maritime disasters. This analysis employs machine learning techniques to identify the key predictors that influenced passenger survival, leveraging the titanic_train.csv dataset to train and validate models, and the titanic_heldout.csv dataset for testing predictive accuracy.

Data preprocessing is crucial in preparing the datasets for modeling. This involves handling missing values, encoding categorical variables such as sex and embarkation port, and normalizing features like age and fare. For instance, age and fare are continuous variables, while sex and embarkation port are categorical. Methods such as one-hot encoding or label encoding are applied to convert categorical data into numerical formats compatible with machine learning algorithms.

The first model employed is Linear Regression. Although primarily used for regression tasks, Linear Regression can be adapted for classification by setting a threshold (usually 0.5) to determine survival prediction. Its simplicity allows for initial baseline performance, but due to its linear nature and assumption of a continuous dependent variable, its accuracy might be limited in classification contexts.

Decision Trees offer a more flexible and interpretable model, capable of capturing non-linear relationships between features and survival outcomes. By recursively partitioning the data based on feature values, decision trees can discover complex patterns such as the higher survival rates among women and children in higher social classes. Parameters such as max_depth and min_samples_split are tuned to prevent overfitting and improve generalization.

The Nearest Neighbors algorithm classifies passengers based on the similarity to nearby instances in feature space. It is a non-parametric method that makes no assumptions about the data distribution, making it suitable for capturing local patterns. The choice of k (number of neighbors) is critical; cross-validation is employed to choose the optimal k, balancing bias and variance.

Clustering, typically an unsupervised approach like K-Means, is used to identify natural groupings within the data that may correspond to survival likelihood. Although clustering does not directly output survival predictions, it provides insights into inherent data structures. These clusters can then be labeled based on the majority survival status within each cluster, serving as a basis for classification.

Model evaluation involves generating confusion matrices, which show true positives, true negatives, false positives, and false negatives. Accuracy metrics quantify overall performance, while precision, recall, and F1-score provide additional insights into model effectiveness, especially considering class imbalance (more non-survivors than survivors).

The results indicate that ensemble methods or combined approaches could further enhance accuracy, but these are beyond the scope of this initial analysis. The project underscores the importance of feature selection, data cleaning, and parameter tuning in developing robust predictive models in social science and safety-critical applications.

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