The Titanic dataset is one of the most iconic datasets in machine learning and data science. It offers an opportunity to explore data preprocessing, feature engineering, and classification models by predicting the survival of passengers based on various features such as age, gender, class, and more.
This repository provides the dataset, essential resources, and guidance for working with Python, Pandas, and NumPy to analyze and build machine learning models.
The main goal is to predict whether a passenger survived the Titanic disaster using machine learning techniques. This involves:
- Preprocessing the data to handle missing values.
- Performing exploratory data analysis (EDA).
- Building and evaluating classification models.
- Handling Missing Values:
- Imputed missing ages using median values grouped by passenger class and gender.
- Filled missing values in categorical features (e.g., Embarked) with the most frequent category.
- Feature Encoding:
- Converted categorical variables (e.g., Gender, Embarked) into numerical representations using one-hot encoding.
- Analyzed survival rates across different features like Pclass, Sex, and Embarked.
- Visualized correlations.
- Chose KNN for its simplicity and ability to classify passengers based on their proximity in feature space.
| Model | Accuracy | Precision | Recall |
|---|---|---|---|
| KNN | 0.84 | 0.82 | 0.81 |
- Python
- Pandas & NumPy: Data manipulation and analysis.
- Matplotlib & Seaborn: Visualizations and insights.
Working on the Titanic dataset provided valuable insights into:
- The importance of preprocessing and EDA in machine learning.
Feel free to explore the dataset and my implementation in detail. If you have any questions or suggestions, don’t hesitate to reach out! 😊