Titanic_Survival

Completed by Sonakshi Chauhan.

Overview: This project is using the Titanic Dataset to create a model that will

-return a conditional survival probabily of a passenger -Help you comapre and contrast all the Classification models based on accuracy -data vizualizations given a condition on a numerical variable from the dataset.

Problem Statement: Build a model that will return a passengers survival chance given a passengers detail as input.

Data: Titanic Kaggle Challenge

Deliverables: Probability

Ahoy! Let's Sail

Topics Covered

1. Statistical Modeling
2. Imputation of Missing values
3. Probability
4. Various Classification Techniques

Tools Used

1. Scikit-learn
2. Google Colab

Installation and Usage

Ensure that the following packages have been installed and imported.

pip install numpy
pip install pandas
pip install seaborn

Jupyter Notebook - to run ipython notebook (.ipynb) project file

Follow instruction on https://docs.anaconda.com/anaconda/install/ to install Anaconda with Jupyter. Alternatively: VS Code can render Jupyter Notebooks

Notebook Structure

The structure of this notebook is as follows: -Imports -Data Loading -Data Pre-processing -Data Analysis -Data Vizualization -Encoding -Supporting Target and Features -Spliting Data -Model Training -Testing and Prediction

Data Pre-Processing

->observing the data above we found it had missing columns and rows ->We dropped the 'Cabin' column as it had highest number pf missing values ->We manipulated the 'Age' and 'Embarked Column'

Data Analysis

->Prediction has to be made depending on the survival number ->Here we analyze the number of survived people according to different classes

Data Vizualizations

->Here we vizualize our data to have a better understanding of highest survivval rates are from which category.

#Categorial Encoding ->Here we encode all the values numerically so as to ensure similarity in data types

#Supporting Target and Features ->Here we divide data into dependent and independent variables mainly 'Y' having the dependent value and 'X' having independent values

#Splitting our Dataset into Train and Test Set ->Using sklearn library we split our dataset into train and test

Model Training

->First we scale our train and test set values -> Here we train multiple classification models to choose which one is more accurate -> We find RandomForest more accurate and move ahead with it.

#Prediction and accuracy ->This is the final step where we test and make predictions on our model

#Conclusion ->We built a Classifier using Random Forest technique to predict titanic survival rates

Contact: sonakshichauhan1402@gmail.com

Project Continuity

This is project is complete

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.