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Reading list

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
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

The Clever Ingredient that decides the rise and the fall of your Machine Learning Model- Exploratory Data Analysis

Megha
Megha Last Updated : 24 Nov, 2020
5 min read

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

Introduction

Well! We all love cakes. If you take a deeper look at the baking process, you will notice how the proper amalgamation of the several ingredients and one clever leavening agent -Baking Powder can decide the rise and the fall of your cake.

`Baking the cake` might sound off-track in the technical article but I believe it to be quite relatable and a delicious analogy to understand the importance of EDA in the Data Science Pipeline.

When Baking the cake is to the Data Science Pipeline then Clever Leavening Agent(Baking Powder) is to Exploratory Data Analysis.

Before your mouth starts watering for a Cake as mine already is, Let’s Understand.

 

What exactly is Exploratory Data Analysis? 

Exploratory Data Analysis is an approach for Data Analysis that employs a variety of techniques to-

  • Gain intuition about the data.
  • Conduct sanity checks. (To be sure that insights we are drawing are actually from the right dataset).
  • Find out where data is missing.
  • Check if there are any outliers.
  • Summarize the data.

Let’s take the famous `BLACK FRIDAY SALES` case study to understand, Why do we need EDA.

Exploratory Data Analysis -Black Friday Sales Data

 

The core problem is to understand customer behavior by predicting the purchase amount. But isn’t it too abstract and leaves you baffling on what to do with the data, especially when you have so many different products with various categories.

Before reading further, give a little thought to this question- Would you put all the ingredients available in the kitchen as it is in the oven to bake the cake?

Obviously, The answer is no! Before you take the entire dataset as it is in consideration to bake it in the Machine Learning Model, you would want to

  1. Draw out important Insights
    1. Variable identification (whether data contains Categorical or Numerical variables or a mix of both).
    2. The behavior of variables (whether variables have 0-10 or 0-1million values).
    3. Relationship between variables (How variables are dependent on each other).

  2. Check Data Consistency

    1. To ensure all data present. (If we have collected  data for three years, any week missing can be a problem in later stages.)
    2. Are there any missing values present?
    3. Are there any outliers in the dataset? (eg: a person with age 2000 years is definitely an anomaly)

     

  3. Feature Engineering
    1. Feature Engineering(To create new features from the existing raw features in the dataset).

     

** EDA in an essence can break or make any machine learning model.**

 

Steps In Exploratory Data Analysis

Exploratory Data Analysis process

There are 5 steps in EDA :->

  1. Variable Identification:  In this step, we identify every variable by discovering its type. According to our needs, we can change the datatype of any variable.Exploratory Data Analysis - Data type
    ~Statistics play an important role in Data Analysis. It is a set of rules and concepts for analysis and interpretation of the data. There are different types of Analysis that need to be done as per requirements.~Let’s study them
  2. Univariate Analysis: In Univariate Analysis, we study individual characteristics of every feature/variable available in the dataset. There are two types of features – Continuous and Categorical. In the image below, I have given a cheat sheet of various graphical techniques that can be applied to analyze them. Exploratory Data Analysis - Univariate analysis table

    Continuous Variable:

    To showcase Univariate analysis on one of the Continuous variables of the Black Friday Sale Dataset- `Purchase`, I have created a function which takes Data as input and Plot a KDE graph explaining the characteristics of the feature.
    Exploratory Data Analysis - Univariate analysis code
    Exploratory Data Analysis - purchase data

    Categorical Variable

    To showcase Univariate analysis on the Categorical variables of the Black Friday Sale Dataset- `City_Category` and `Marital_Status`, I have created a function that takes Data and Features as input which returns a count plot explaining the frequency of the categories in the feature.
    Univariate Analysis categorical data Code

    Univariate Analysis Category

  3. Bivariate Analysis: In Bivariate Analysis, we study the relationship between any two variables which can be categorical-continuous,categorical-categorical, or continuous-continuous( as shown in the cheatsheet given below along with the graphical techniques used to analyze them).
    Bivariate Analysis Table
    In Black Friday Sales, we have categorical independent variables and continuous target variables, So we can do categorical-continuous Analysis to understand the relationship between them.
    Bivariate Analysis
    Box Plot

    Inference:
    From the above two analysis, we have observed in Univariate Analysis that a number of customers are maximum in city category B. But Bivariate Analysis when done between `City_Category` and `Purchase` shows a different story that average Purchase is maximum from city category C. Hence these inferences can give us better intuition about the data which in turn help in better Data preparation and Feature Engineering of the features.

    It is important to note that just relying on Univariate and Bivariate Analysis can be quite misleading, So to verify the inferences drawn from these two can be validated with Hypothesis Testing. We can do a t-test, chi-square test, Anova which allows us to quantify whether two samples are significantly similar or different from each other. Here I have created a function to analyze continuous and categorical relationships that return t-statistic value.

    Bivariate Analysis Code
    Marital StatusIn Univariate Analysis we observe that there is a significant difference between the number of customers who are married and unmarried. From t-test, we get t-statistic value 0.89 which is greater than significance level i.e 0.05 which shows that there is no significant difference between average purchase of singles and married .

  4.  Missing Value Treatment : Primary reason for this step is to find out if there is any specific reason why these values are missing and how we treat them. Because If we don’t treat them, then they can interfere with the pattern running in the data which in turn can degrade the model’s performance. Some of the ways in which missing values can be treated are :- Filling them with mean, median, mode and you can use imputers.
  5. Outlier Removal : It is essential that we understand the presence of outliers as some of the predictive models are sensitive to them and we need to treat them accordingly.

 

End Notes

In this article, I have briefly discussed the importance of EDA in the Data Science pipeline and steps that are involved in proper analysis.I have also showcased how wrong or incomplete analysis can be quite misleading and can considerably affect the performance machine learning models.

“If you don’t roast your data, you are just another person with an opinion.”;)

 

bivariate analysisblogathonunivariate analysis
Megha
Megha

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This piece of knowledge is amazing.

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Thank you for this content, I was googling this information from many days.

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