🚢 LAB 3 : Titanic Data Processing & Modeling 🧑💻
Welcome to the Titanic Data Preprocessing & Modeling project! 🌊 This project showcases how to preprocess data, handle missing values, outliers, feature engineering, and build models like Logistic Regression and Random Forest! 🔥
🛠️ Tools & Libraries Used
This project is built using Python 🐍 and the following libraries:
Pandas 🐼: For data manipulation.
NumPy 🔢: For numerical operations.
Matplotlib 📊 & Seaborn 🎨: For visualizations.
Scikit-learn 🤖: For machine learning and model evaluation.
🔍 Project Overview
The main objective of this project is to preprocess the Titanic dataset and build models to predict passenger survival 🛳️. Here's a quick breakdown of the steps taken:
Data Collection 📦
Used the Titanic dataset from seaborn 🎯
Data Cleaning 🧹
Handle missing values, outliers, and invalid data 🚮
Outliers Handling 🚫
Capped the extreme values in age and fare columns ✂️
Normalization 📐
Scaled the numerical features using Min-Max scaling or Z-score normalization 🎚️
Feature Engineering 🛠️
Created new features like family_size and extracted title from names 📛
Feature Selection 📑
Selected important features using correlation and feature importance analysis 🔍
Model Building 🧑🔬
Built Logistic Regression & Random Forest models for classification 🤖
🚀 Steps to Run the Project
Clone this repository:
📊 Results
After training our models, here's what we found:
Logistic Regression Results 🚀
Accuracy: 0.79
Precision: 0.72
Recall: 0.72
F1 Score: 0.72
Random Forest Results 🌲
Accuracy: 0.78
Precision: 0.74
Recall: 0.65
F1 Score: 0.69
👀 The Logistic Regression model performed slightly better in terms of recall, while Random Forest had higher precision.
🤔 Why Titanic Dataset?
The Titanic dataset is famous for demonstrating basic machine learning tasks such as classification 📚. It's easy to understand yet provides a challenging problem with both categorical and numerical data 🧠.
🔥 Features of this Project
Easy-to-follow data preprocessing pipeline 📋
Intuitive visualizations 📈 to understand the data and outliers
Fun feature engineering to get the most out of the dataset ⚙️
Two powerful models: Logistic Regression and Random Forest 🎯
🙌 How to Contribute
Fork this repository 🍴
Create your feature branch: git checkout -b my-new-feature 🌵
Commit your changes: git commit -am 'Add some feature' 💾
Push to the branch: git push origin my-new-feature 🚀
Submit a pull request 🎉
💡 Fun Facts
Did you know? The Titanic was built in Belfast, Northern Ireland 🇮🇪.
The real story of the Titanic is both tragic and heroic, inspiring numerous books and movies 🎬.
🤩 Meet the Team
Yves Dylane 💻 Project Lead
📬 Contact
Feel free to contact us with any questions or suggestions! 📧
Enjoy coding and have fun! 🎉