hiteshnp / titanic-classification

Titanic Survival Prediction🚢

Titanic-Survival-Prediction

Hello Everyone,

Here is My Classification Project based on Predicting Survival of Passengers.

Dataset

I used Titanic Dataset avaliable on Kaggle.

Link to the Dataset : Titanic Dataset

Problem Statement

• The sinking of the Titanic is one of the most infamous shipwrecks in history.

• On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg.

• Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew.

• While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others.

• In this challenge, we have to build a predictive model that answers the question : “what sorts of people were more likely to survive?” using Passenger Data.

Table of Contents

• Setting up the Enviroment
• Libraries required for the Project
• Getting started with Repository
• Steps involved in the Project
• Conclusion
• Link to the Notebook

Setting up the Enviroment

Jupyter Notebook is required for this project and you can install and set it up in the terminal.

• Install the jupyter notebook - pip install jupyter notebook

• Run the Notebook - jupyter notebook

Libraries required for the Project

NumPy

• Go to Terminal and run this code - pip install numpy

• Go to Jupyter Notebook and run this code from a cell - !pip install numpy

Pandas

• Go to Terminal and run this code - pip install pandas

• Go to Jupyter Notebook and run this code from a cell - !pip install pandas

Matplotlib

• Go to Terminal and run this code - pip install matplotlib

• Go to Jupyter Notebook and run this code from a cell - !pip install matplotlib

Sklearn

• Go to Terminal and run this code - pip install sklearn

• Go to Jupyter Notebook and run this code from a cell - !pip install sklearn

Getting Started

• Clone the repository to your local machine using the following command :

git clone https://github.com/HiteshNP/Titanic-Classification.git

Steps involved in the Project

Data Cleaning

• Removing Null Values in the Age Columns and replacing them with Mean Age by using fillna().mean().

• Dropping Cabin Columns as it contains Many Null Values.

• Dropping Text Columns from our Dataset because our Model only works on Numerical Data.

• Creating Dummies Value for Sex Column and Converting it into a DataFrame and Concatenating it with Orignal DataFrame.

Model Building

• Firstly I have defined Dependent and Independent Variables for our Traning and Testing.

• I have splitted data into Traning and Testing Set by using Train Test Split.

• Then I Trained the Model with X_train and y_train and checked the Score.

• And Finally I predicted the Result from my Trained Model.

Conclusion

• In conclusion, the Titanic Survival Prediction Project was an exciting endeavor where I applied Logistic Regression, Support Vector Machines, Naive Bayes, KNN and Decision Tree to predict the survival of passengers aboard the Titanic.

• The Naive Bayes model achieved Accuracy Score:0.7686,
  Logistic Regression model achieved Accuracy Score:0.7611,
  Decision Tree model achieved Accuracy Score: 0.7425,
  Support Vector Machines model achieved Accuracy Score:0.6604,
  KNN model achieved Accuracy Score:0.6604, indicating a reasonably good level of accuracy in predicting survival outcomes.

• Therefore, by employing a Naive Bayes model, one can attain the highest level of accuracy when predicting survival outcomes.

Link to the Notebook:

Titanic Survival Prediction