https://github.com/sanjiv856/machine_learning_scikit-learn

Repository for machine learning in Python using Scikit-learn.
https://github.com/sanjiv856/machine_learning_scikit-learn

pipelines python scikit-learn sklearn titanic-kaggle titanic-survival-prediction

Last synced: 17 days ago
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Repository for machine learning in Python using Scikit-learn.

• Host: GitHub
• URL: https://github.com/sanjiv856/machine_learning_scikit-learn
• Owner: sanjiv856
• Created: 2024-08-20T21:26:20.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-08-24T08:14:58.000Z (3 months ago)
• Last Synced: 2024-10-17T12:41:30.491Z (about 1 month ago)
• Topics: pipelines, python, scikit-learn, sklearn, titanic-kaggle, titanic-survival-prediction
• Language: Jupyter Notebook
• Homepage:
• Size: 479 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Titanic Survival Prediction

This project implements a machine learning pipeline to predict the survival of passengers on the Titanic using various classification algorithms. The project involves data preprocessing, feature engineering, model training, hyperparameter tuning, and model evaluation.

## Project Structure

- `data/` - Directory containing the dataset (`train.csv` and `test.csv`).

- `python_scikit-learn_titanic.py` - Main script containing the code for loading data, preprocessing, training models, and generating submissions.

## Running the Code

### Feature Engineering & Preprocessing:

Feature engineering is applied to create new features such as Family_Size, Is_Alone, Title, Age_Group, etc.

Preprocessing pipelines are defined for numerical and categorical features.

### Model Training & Hyperparameter Tuning:

Several classifiers are trained and tuned using GridSearchCV, including:

- Random Forest

- Extra Trees

- XGBoost

- Decision Tree

- Logistic Regression

- Gaussian Naive Bayes

- K-Nearest Neighbors

Best models and their parameters are saved as .pkl files.

### Key Libraries Used

- pandas - Data manipulation and analysis.

- numpy - Numerical computations.

- matplotlib & seaborn - Data visualization.

- scikit-learn - Machine learning library for model building and evaluation.

- xgboost - Implementation of gradient boosting algorithm.

### Feature Engineering

The following features are engineered:

- Family_Size - Number of family members onboard.

- Is_Alone - Binary feature indicating if the passenger was alone.

- Title - Extracted from passenger names.

- Age_Group - Binned age groups.

- Ticket_Number, Ticket_Location - Extracted from ticket information.

- Cabin_Alphabet, Cabin_Recorded - Extracted from cabin information.

### Hyperparameter Tuning

Hyperparameters are tuned using GridSearchCV with cross-validation to find the best model configuration.

### Feature Importance

Feature importance is plotted for the top 20 features for each model.