https://github.com/al-ghaly/titanic-machine-learning

Bunch of Machine Learning Classification Models to predict the if a passenger is most likely die
https://github.com/al-ghaly/titanic-machine-learning

classification-algorithm data-science machine-learning machine-learning-algorithms

Last synced: about 1 year ago
JSON representation

Bunch of Machine Learning Classification Models to predict the if a passenger is most likely die

• Host: GitHub
• URL: https://github.com/al-ghaly/titanic-machine-learning
• Owner: al-ghaly
• License: mit
• Created: 2023-06-08T19:46:05.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2023-06-10T09:41:15.000Z (almost 3 years ago)
• Last Synced: 2025-01-22T06:48:06.642Z (over 1 year ago)
• Topics: classification-algorithm, data-science, machine-learning, machine-learning-algorithms
• Language: Jupyter Notebook
• Homepage:
• Size: 443 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# Titanic-Machine-Learning

## By MOHAMED ALGHALY

### Applied Models:

1. **Logistic Regression**

2. **Decision Tree**

3. **Random Forest**

4. **Support Vector Machine (SVM)**

5. **Ada Boost**

6. **Gradient Boosting**

7. **Naive Bayes**

8. **K-Nearest Neighbor (KNN)**

--- 

# I have made the data preprocessing dynamic to enable flexible modeling

## I implemented the transform function to clean the data

### You will just have to specify any parameter to overwrite the default data cleaning as follows:

![Screenshot (248)](https://github.com/al-ghaly/Titanic-Machine-Learning/assets/61648960/f6587744-2c35-4c0c-a9c4-eca2330e3083)

![Screenshot (249)](https://github.com/al-ghaly/Titanic-Machine-Learning/assets/61648960/d4777614-8d75-4c64-b2f0-6fb6a91302aa)

![Screenshot (250)](https://github.com/al-ghaly/Titanic-Machine-Learning/assets/61648960/124b936d-6615-495c-97d1-ad758ab45ee8)

* ### method: how to handle missing values

* ### inplace: whether to clean the data as a new dataframe or into the same one

* ### drop_features: whether or not we want to drop useless features

* ### combine_rel & remove: how to handle multicollinearity

---

## Attached Files

* ### train.csv: 

  * The dataset to model (Labeled)

* ### test.csv: 

  * The dataset to test your model on (UnLabeled)

* ### Titanic.ipynb: 

  * The Jupyter Notebook for the project

* ### Titanic.html: 

  * The project's report

* ### Predictions.csv: 

  * The predections the model made on the unlabeled test data

* ### scenarios.png: 

  * The possible scenarios to clean the data