Kaggle Competition | Machine Learning Project : Titanic Survival Exploration
This project contains create decision functions that attempt to predict survival outcomes from the 1912 Titanic disaster based on each passenger’s features, such as sex, age, sibsp, embarrked and cabin class which accurately predict the outcomes for at least 80% of the passengers in the provided data. You can also view features of dataset on Kaggle.
In this contest, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy.
This Kaggle Getting Started Competition provides an ideal starting place for people who may not have a lot of experience in data science and machine learning.
Goal for this Notebook:
Show a simple example of an analysis of the Titanic disaster in Python. This is aimed for those looking to get into the field or those who are already in the field and looking to see an example of an analysis done with Python.
This Notebook will show basic examples of:
Data Handling
- Importing Data with Pandas
- Cleaning Data
- Exploring Data through Visualizations with Matplotlib and Seaborn
Data Analysis
- Supervised Machine learning Techniques:
- K Nearest Neighbour
- Gaussian Naive Bayes
- Support Vector Machine (SVM)
- Decision Tree Classifier
- Plotting results.