The most famous competition over the kaggle . In this Repository my intention is to basically explain to every beginner but how to begin with Kaggle very first time. So this repository is for those who just begin their Machine Learning Journey. In this notebook i try to take down every single topic with a very naive approach so every beginner can grasp it very easily.
Let's Begin
The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships.
One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class.
In this challenge, 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.
Code in the repo is for the one who just begin with kaggle's Titanic competition.This basically explains all the basic concept ,
How to approach the any competition over kaggle and how to start with it. This notebook will not make you an expert but too damn sure it will guide you to How to get started and rest is up to you ..
Quick Start: View a static version of the notebook in the comfort of your own web browser
Python3NumpyPandasMatplotlibSupervised LearningMachine Learning AlgorithmClassification Algorithms
Run this using jupyter notebook. Just type jupyter notebook in the main directory and the code will pop up in a browser window.
Show a simple example of an analysis of the Titanic disaster in Python using a full complement of PyData utilities. 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.
- Data Handling
- Importing Data with Pandas
- Cleaning Data
- Exploring Data through Visualizations with Matplotlib
Supervised Machine learning Techniques: + Logit Regression Model + Plotting results + Support Vector Machine (SVM) using 3 kernels + Basic Random Forest + Plotting results
- K-folds cross validation to valuate results locally
- Output the results from the IPython Notebook to Kaggle
Competition Website: kaggle
The output of the code is to predict the survival(0-Die,1-Survive) of the passenger who borded on the Titanic.
Vikram singh
Get in tuch :