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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
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

An Efficient way of performing EDA- Hypothesis Generation

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

Introduction-

One who knows how to improvise and can deal with all kinds of situations is a winner, right? Similarly, if a Data Scientist knows how to adapt and improvise in various situations then he can solve any kind of business problem using Data!Let’s say you have a problem in hand but you have strict deadlines and there is not enough time to understand it. To be honest there is never enough time to understand your data. But, it is essential to understand the problem well. The more you dig in, the clearer you will be in terms of the approach you are going to take to deal with the problem.

Once you are done with understanding the problem statement and have your strategy in place, then only look into data. Never dive into the ocean without doing preparation. If you directly look into your data, you would try to find as many patterns possible in it and that process will never end. You won’t know when to stop and will get stuck instead.

Don’t worry, Hypothesis Generation is there to help you out!

 

What is Hypothesis Generation?

As mentioned in this article,
Hypothesis generation is an educated “guess” of various factors that are impacting the business problem that needs to be solved using machine learning.

In short, you are making wise assumptions as to how certain factors would affect our target variable and in the process that follows, you try to prove and disprove them using various statistical and graphical tools.

This process will save a lot of time. Let me illustrate that with an example.

Example-

The Problem Statement that we are going to solve is-

“Being a Data Scientist in an E-Learning Platform, you have been employed to determine the Course Sales for the upcoming few months so that we find out what our Cash flow is gonna be. This will help us in the allocation of our resources to pay our instructors, for our operations and also, for our expansion.”

Ideally, before looking into the data, just spend one hour and understand the problem statement well. Think about all the factors that would affect the sales of our courses and then look into the data- this will help you in the feature engineering process.1

This is how the Dataset looks like–

Hypothesis Generation - Data
(Random Snippet from the Dataset)

There are 9 features (removing ID) and the target variable- Sales.

First, perform the Univariate analysis to understand each variable. I hope that’s pretty straightforward and not that much time-consuming?

But, the process of Bivariate and Multivariate Analysis which follows could take a lot of time if you try to take up all the possible combinations (⁹C₂=36 combinations for just bivariate) and try to find a relationship between them. Especially when you’re dealing with a hackathon dataset, this process should ideally be performed quickly!

Now, this is how I carried out Bivariate analysis-
While I was doing the Univariate Analysis, I compiled a set of questions which I would then try to prove and disprove using simple visual tools, you might want to choose another way.

questions to be asked

So, these are the questions that would more or less cover our Bivariate Analysis. The visualizations have been made in Python using Matplotlib and Seaborn. They are pretty easy to build as well as interpret.

 

Q1. Is Short Promotion causing an increase in Sales?

Hypothesis Generation - Short promotion vs sales

Yes, it is! We can see that there is a difference between Mean Sales when Courses were on Short Promotion and when they were not. You may use a t-test to statistically back your analysis as well rather than just visually doing it.

Q2. Do Public Holidays contribute to More Sales?

Public Holiday vs Sales

We can see that the Mean Sales on Non-Holidays is more than on Public Holidays. So, No! Public Holidays do not contribute to More Sales.

Q3. Do the courses which are being Long Promoted have Higher Sales?

Hypothesis Generation - Long promotion vs sales

Again, the mean sales under Long Promotion is less than when it is not under long promotion. So, we can say that Long Promotions do not help increase our sales. Hence, No!

Q4. Is User Traffic More on Public Holidays?

traffic vs public holiday

No! In fact, User Traffic is lesser on Public Holidays. People don’t visit prefer to buy courses on holidays xD

 

Q5. Does Higher User Traffic contribute to Higher Sales?

traffic vs sales

After looking at the scatter plot and obviously the correlation value, we can see that higher User Traffic leads to higher Sales. So, Yes! Higher User Traffic significantly affects our target variable.

Q6. Do Courses with Higher Competition Metric have Lower Sales?

competition metric vs sales

No! Looking at the correlation value (-0.015), we can say that Competition_Metric hardly has any effect on Sales. Instead, it slightly decreases it.

Q7. Does Course Type affect the Sale of a course?

course type vs sales

Yes! The Bar Plot clearly shows that Degrees have much higher sales than Course and Programs.

Q8. Do Course Domains Affect Sales?

course domain vs sales

Yes! Business courses have a much higher mean than others. So, Course Domain affects Sales. But, do you see that the black line going through Business is much bigger than the rest? This implies that the variance of Sales of Business courses is much larger. To further confirm whether Course Domains affect the Sales, you may perform ANOVA or T-test.

 

Q9. Do we observe any pattern of Sales w.r.t. Day No.?

sales vs day

To answer this question we draw two line plots with Sales on the y-axis and Days on the x-axis in the first chart and Weeks in the second chart. We observe a bi-weekly pattern through the first plot and a yearly seasonal pattern through the second.

Voila! This is how our Analysis turned out to be-

Hypothesis Generation Summary

Conclusion-

Wasn’t it quick? Once we performed the Hypothesis generation, it hardly took any time to carry out further analysis.

And that is the beauty of planning, my friends. (Aha! I just gave you a life lesson! xD)

I am sure you would have some different ways of doing EDA and I would love to hear it from you. You can contact me on LinkedIn or we can discuss it in the comments below!

Thanks for staying till the end.

LinkedIn- https://www.linkedin.com/in/iasarthak/
GitHub- https://github.com/iasarthak

blogathonexploratory data analysishypothesis generation
Sarthak
Sarthak

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Responses From Readers

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Anukriti Singh
Anukriti Singh

Love this! Pretty well described, a lot of beginners struggle with it, more so on the time allocation part - thanks for this!

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