Hello. They hosted a Kaggle competition in Nov 2017 to predict the probability that a driver will initiate an auto insurance claim in the next year. setwd("~/Kaggle/Big mart sales") train test # +++++ My Todo List +++++ # 1. Let’s load the data and start with a simple scatter plot, without any customizations and see what we got. While in ggplot, it automatically sets the title with a bigger bolded font and the caption with a smaller font. Libraries. To label the points, we’ll need to use ‘annotate’ twice, to draw the arrow and to writing the text. Hence, I have not attempted to write how the insights relate to the real-world business, The objective was to run through the R code and further my understanding of key statistical concepts, New packages and functions that I learnt while writing this post, In the histogram plot, I used the special variable, As per the documentation, these are special variables computed from the aesthetics, Alternative way of using them is by calling them within the, Shapiro-Wilk test works for sample size < 5000, Both tests expect the distribution to be perfectly normal, Phi coefficient is used for binary-binary variables. Making Plots With plotnine (aka ggplot) Introduction. The other is a language and environment for statistical computing; one has more resources for deep learning while the other for statistical models, to name a few differences. In some ways, they feel very similar but also not at all. Remaining 26 features are either continuous or ordinal are have been plotted below, How about using statistical tests for normality. jonocarroll / so-answers.R. Kaggle conducts industry-wide surveys to assess the state of data science and machine learning. Plotting a default scatter plot is almost the same in ggplot and Matplotlib, but the chart produced by ggplot has way more elements. Copy and Edit 534. Another awesome feature of ggplot2 is its link with the plotly library. Hi all! - SuicideRateLineGraph The data can be downloded from kaggle. Its API is similar to ggplot2, a widely successful R package by Hadley Wickham and others. Created Oct 21, 2016. The House Prices: Advanced Regression Techniques challenge asks us to predict the sale price of a house in Ames, Iowa, based on a set of information about it, such as size, location, condition, etc. Some of the prediction algorithms require the continuous features to follow a normal distribution. I also want to annotate two points with both an arrow and a text. Visit the interactive graphic section of the gallery for more. :the condition has length > 1 and only the first element will be used comparing a vector with a scalar=> R automatically takes the first element of var In the comments, I was asked how to resize the plots in a Jupyter notebook. The point is: you shouldn’t choose which to use based on their data visualization packages, but it’s interesting to know their differences nonetheless. In Matplotlib, we have to set its coordinates manually. While R’s package also added a background color, x and y labels, gridlines, minor ticks, and a legend. 75% of all female passengers survived whereas less than 25% of male passengers survived. Understand the Data # # # +++++ # Understand of Data # ===== # 1. Today I will analyze the San Francisco Crime Data which can be found at Kaggle. The data set for this post can be found on Kaggle. We will go through step by step from data import to final model evaluation process in machine learning. A Medium publication sharing concepts, ideas and codes. In both tools, we have some properties that can be customized when creating the elements and others that need to be customized later. Copying is the first step to be the great because you have to look at creative works to create something creative. We’ve made it to the best part. The main reason to participate in the competition was to do a big picture walk and to gain a high level understanding of all the concepts involved in predictive … Shows the suicide rates for US, US men, US women, Global, Global men, and Global Women. 103. Then a vector list nm is created with the names of the columns from the data frame X, using the names() function. Do the features follow a normal distribution ? Since it has complete integration with Pandas, we could even plot directly from the data frame like: legendary.plot(kind=’scatter’, x=’Attack’, y=’Defense’, color=’#2ACC74'). The World Happiness Report 2016 Update, which ranks 156 countries by … Now let’s try moving the legend in both graphs to the bottom of the plot, and display the keys in a single row. For ggplot, we’ll remove the y-axis minor ticks' gridlines. Matplotlib requires more code and can be more complicated, but it also gives you more control for plotting your chart. The function is defined to take in X data (where X represents the data frame to be assigned when applying the function in the last step) and ignore NA values. This is my first run at a Kaggle competition. 99% of observations have zero values for the features. Importing and cleanign the data for analysis. Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. The Kaggle Titanic competition is a great way to learn more about predictive modeling, and to try out new methods. This survey, which ran from August 7th to August 25th of 2017, was an “industry-wide survey to establish a comprehensive view of the state of data science and machine learning” with data from 16,716 … For this, we’ll turn to Kaggle. With ggplot, we need to first draw the arrow with a start, end, and properties, then we have to write the texts with their position and properties, which makes annotating slightly more complicated. The main difference is that Matplotlib requires x limits for the horizontal line and y limits for the vertical line. We only have ‘title.’ The subtitle and caption should be plotted as ‘text.’. Kaggle conducts industry-wide surveys to assess the state of data science and machine learning. The main difference is that ggplot has a ‘theme’ that can change most of those properties and keep those changes grouped. Abdul Majed Raja recently wrote a nice post analyzing gender diversity within the Kaggle Survey data. Both are continuous and are used to detect curvilinear relationships. Features without these designations are either continuous or ordinal. ggplot(data) + aes(PromoInterval, Sales, fill = PromoInterval) + geom_boxplot( outlier.size = 1, outlier.colour = " blue ") ggplot( data [ ! So just by plotting a default chart, we can already tell that ggplot is more about customizing existing elements, while Matplotlib is more about creating visualizations from scratch. In this article, you will learn how to save a ggplot to different file formats, including: PDF, SVG vector files, PNG, TIFF, JPEG, etc.. You can either print directly a ggplot into PNG/PDF files or use the convenient function ggsave() for saving a ggplot.. The payoff is definitely worth it.