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.
Competition Description🌐
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.
Overview👀
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.
Dependencies🏗️
Python3
Numpy
Pandas
Matplotlib
Supervised Learning
Machine Learning Algorithm
Classification Algorithms
Algorithm✨
- importing the required libraries
- Read and input the training and test data from the CSV files
- Exploring the data for null values
- Exploratory analysis to check for the influence of the various features over the output
- Cleaning the training and test data
- processing the categorical values and other unwanted features
- Separating and assigning the features and output parameters for the training and test dataset
- Fitting the logistic model using the training data
- calculate the accuracy using evaluation metrics
- Use the trained logistic model to predict the survival of passengers in the test data
- Output the predicted data into a CSV file
Data Visualisation📊
Ploting the survival rate
Ploting Age graph
Ploting Survival Based on Sex
Ploting Survival Based on Passenger Class
Ploting Survival Based on Sibling
Ploting Survival Based on Parents
