Cleaning : we'll fill in missing values.Data extraction : we'll load the dataset and have a first look at it.In this section, we'll be doing four things. I - Exploratory data analysisĪs in different data projects, we'll first start diving into the data and build up our first intuitions.
Hope you've got everything set on your computer. This tutorial is available on my github account. If you also have a suggestion on how this notebook could be improved, please reach out to me.
If you have a question about the code or the hypotheses I made, do not hesitate to post a comment in the comment section below. This is my first attempt as a blogger and as a machine learning practitioner. This distribution is available on all platforms (Windows, Linux and Mac OSX). sklearn for machine learning and predictive modelingĪ very easy way to install these packages is to download and install the Conda distribution that encapsulates them all.Numpy for multidimensional array computing.Matplotlib and seaborn for data visualization.Pandas for data manipulation and ingestion.The main libraries involved in this tutorial are: Throughout this jupyter notebook, I will be using Python at each level of the pipeline. To make this tutorial more "academic" so that anyone could benefit, I will first start with an exploratory data analysis (EDA) then I'll follow with feature engineering and finally present the predictive model I set up. This post is the opportunity to share my solution with you. As I'm writing this post, I am ranked among the top 4% of all Kagglers. I have been playing with the Titanic dataset for a while, and I have recently achieved an accuracy score of 0.8134 on the public leaderboard. The Titanic challenge hosted by Kaggle is a competition in which the goal is to predict the survival or the death of a given passenger based on a set of variables describing him such as his age, his sex, or his passenger class on the boat.
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Septem33min read How to score 0.8134 🏅 in Titanic Kaggle Challenge
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