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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Spilling the Beans on Visualizing Distribution

Priyanka
Priyanka Last Updated : 10 Dec, 2020
5 min read

This article was published as a part of the Data Science Blogathon.

visualizing distribution

Introduction to distribution and drawing visual reference

Distribution in the English language dictionary has been defined as the way in which something is spread over an area. The good part about definitions, in general, is that they draw their strengths from languages and word meanings. If you are able to identify statistically significant key words hiding in definitions, share your comments. Write about how you understand them in the comments section or here.

 

Definitions in Statistics

Distribution: is defined as the possible values for a variable and how often they occur[1].

The word with statistical significance hiding in that definition is variable.

Variable has two meanings [2]

  • A variable is an attribute that describes a person, place, thing, or idea.
  • The value of the variable can “vary” from one entity to another.

Distribution is like finding patterns from spiled beads. This article will draft a visual description and overlay statistics over it.

Distribution is like beans spilled from a bag. Quite literally. Step back and imagine a dataset with lots of numbers on it. If each number represented a bean, numeric data will be like beans that spilled from that bag. Another parallel that comes to mind is from my days at coffee trading. The bag of beans that came from the estate had beans of all sizes. The first effort would be to sort them first by shape then by size.

Datum: Datum in statistics means one item of information, one fact, one statistic. On its own a datum, the singular form of the more commonly used “data.” In English, it means a piece of information.

Datum is a lot like the spilled bean. It can have a value large and small. Sorting is any process of arranging items systematically. Systematic stems from the word system. The column header and row index create the system. The intersection of the row and column gives meaning to what is otherwise just a number. Visually the spread-sheet is ready.

Conventional primary data collection would start with defining the column header. Row index gets generated as we collect the data points. Transpose of that is also valid but the process is the same. The spreadsheet we constructed in the previous para is a corollary to conventional primary data collection methods. End state to both the approach is the standard spreadsheet. Rows columns and numbers. Fewer set of a datum can be managed using the familiar excel sheets and .csv files. Larger collections of datum will relay concepts of Big data.

 

The analysis is Interplay

Once the data set is available, phase 2 begins. Analyzing data would be an exercise of dealing with and the interplay between numbers. Tying that to headers and building a narrative. Visualization using tools and libraries.

Data analysis is a method in which data is collected and organized so that one can derive helpful information from it. Remember that the datum is sitting in a row or column. More preciously datum is sitting at the intersection of rows and columns.

 

As an analyst what do you do?

You can compare the values that are within the column. Call it univariate analysis. Where all comparisons and conclusions are for everything that is within a column. Uni meaning one, single column heading that all datum will refer to. Similarly, comparisons of 2 columns will be called bivariate. Two or more will be referred to as multivariate.

Giving a column name and collecting data points below is one of the most popular methods of recording. Probably it is rooted in how the human eye moves across a flat surface when reading off it. Compare heat maps of reading patterns. 2 of the 4 most popular reading patterns had a higher concentration in the column format. Regardless of the arrangement, the story is not different if the analysis was being done row-wise. Anything tied to a single header would continue to be called univariate and so on.

 

Let’s overlay statistics on a bag of spilled beans: 

The purpose of the effort is to describe, summarizes that data, and finds patterns. Some ways you can describe patterns found in univariate data include central tendency (mean, mode and median) and dispersion: range, variance, maximum, minimum, quartiles (including the interquartile range), and standard deviation[3]. Visualization tools would include Frequency tables, Bar graphs, pie charts, histograms, box plots, and frequency polygons.

Coffee beans getting compared to other coffee beans for size will be a univariate analysis. Coffee beans compared to coffee beans for size and shape will be bivariate analysis. Coffee bean and rice grain getting compared for size will be bivariate analysis.

Interestingly a random spread of anything statistics says will have a pattern. If it was coffee beans spilled all over, you can see and tell. Data is a little more complicated to see and tell. You would need to rely on mathematical theories and machine learning models to figure it out. Something like spilling the beans, making a mess, and vanishing from the scene. Now to someone else who does not know need to figure things out.

The one who tries to figures it out is the analyst. Most answers will come in the form of this was ‘probably’ that. To figure out the datum we now extensively rely on machine learning models. These models rely on the theory of probability. They would either cluster or classify. If you are a student of machine learning (ML) head over to the comments section and share your definitions of the hidden keywords. The theory of normal distribution has some interesting insights on any bag of beans that got spilled. It says Normal distribution, also known as the Gaussian distribution, is a probability distribution that is symmetric about the mean. Data near the mean are more frequent in occurrence than data far from the mean. In graph form, normal distribution will appear as a bell curve.[4] If you know the average size of the coffee bean, then the theory of normal distribution will help you figure out what percentage of those beans will be what size ( approximately). Cool eh!

Let’s refer to questions relating to size. For the humble coffee bean, it will be called size and for numeric data, it will be valuable. For sake of simplicity let’s say all beans that got spilled from the bag had a positive value. Normalization is a necessary consideration when attempting bivariate analysis. Say the bag of beans had coffee beans and rice. Normalization would be a technique to bring the size of a datum to a common scale. It is like applying a mathematical formula to be able to compare coffee beans and rice grain. Very different from each other. Different range of size among many others. Normalize columns that have the size of both rice and coffee. Normalization is the magic wand that helps you add rice and coffee beans and still make sense of the exercise.

 

End Notes

It is anomalies like these where we mix coffee with rice, that makes data science interesting and complex. Intelligence lies in what can be connected. The tools are there to help.

So enjoy figuring out how to add rice and coffee. There is a whole host of python libraries and exploratory data analysis to figure what the spill is all about.

The author of this article will encourage you to share your own definitions of statically significant keywords. Head over to the comments section here or here.

[1] https://365datascience.com/explainer-video/distribution-in-statistics/
[2] https://stattrek.com/statistics/dictionary.aspx?definition=variable
[3] https://www.statisticshowto.com/univariate/#:~:text=Univariate%20analysis%20is%20the%20simplest,finds%20patterns%20in%20the%20data.
[4] https://www.investopedia.com/terms/n/normaldistribution.asp
Priyanka
Priyanka

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