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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

Exploratory Data Analysis Using Einblick

U
UPPU RAJESH KUMAR Last Updated : 03 Apr, 2023
5 min read

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

Introduction

Exploratory Data Analysis, or EDA, examines the data and identifies potential relationships between variables using numerical summaries and visualisations. We use summary statistics and graphical tools to get to know our data and understand what we may deduce from them during EDA.

To do EDA,  we employ various technologies, including python and pandas profiling. However, to use any of these tools effectively, a person must be familiar with coding, and learning these tools takes a lot of time and effort.

What if we can make visualisations with drag and drop without much coding? How cool it would be if we could make summary statistics without writing any code? Einblick can do all these things and much more without much hassle.

In this article, we will discuss Einblick and do exploratory data analysis with Einblick on the tips dataset.

What is Einblick?

Einblick is a tool that can be used to explore datasets, create machine-learning models and make predictions. It can also be used to deploy data apps, and the best thing is that this tool can be used collaboratively with many team members in real-time collaboration.

 

We can use this tool to clean our data and explore and visualise the data. We can also use this to manipulate our data and create aggregate tables. Einblick also offers an AutoML solution that can be used to create machine-learning models and make predictions. We can encode data, extract text features, and perform clustering analysis. All this can be done without any code. All we need to do is drag and drop the appropriate elements.

But that doesn’t mean it is completely no-code. It also offers an option to create python cells so that we can do any complex data manipulations. We will see how to make visualisations and explore the dataset in the subsequent sections. We will use the tips dataset for this demo.

How to do EDA using Einblick?

Now that we have seen Einblick, let’s start working on this. Firstly, go to the official website and signup for a free account. You will see a dashboard that looks like the one below.

Click the ‘New canvas’ button that is visible on the screen. It will create a new canvas where you can do all the operations. It looks like the one below.

 

We can see two options, ‘Dataframes’ and ‘Operations’. Dataframes are used to add the data frames from appropriate data connectors. Operations are used to perform different functions on the dataset. We will use the tips dataset for this demo.

Click the plus symbol beside the Dataframes option and upload the dataset. Once the upload is complete, drag the dataset to the canvas, i.e. to the right of the screen, which is blank, and click the ‘table’ option to see the data frame. We can see the data frame with all the data displayed and the number of rows and columns.

Now let us describe the dataset using the ‘profiler’ option. Under the ‘Operations’ menu, click the ‘Core’ option. You can see the ‘Profiler’ option. Drag and drop it into the canvas, i.e., into the right side of the screen. Once you drop it into the canvas, drag the under arrow mark visible on the data frame and attach it to the plus symbol on the ‘Profiler’ tile. After this, Einblick works in the background and describes all the columns in the dataset, as shown below.

As we can see, all the columns are described along with their null and unique values. For Numerical variables, summary statistics like mean, maximum, and minimum are also described. All this without writing a single piece of code. We just uploaded a dataset, dragged and dropped appropriate tiles, and got this. Now let’s plot some graphs and see the trends in the dataset.

Firstly, we will plot a scatter plot between the attributes ‘total_bill’ and ‘tips’. To do this, under the ‘Operations’ menu, click the ‘Visualization’ menu and drag the ‘Chart’ option into the canvas. Now connect the data frame to this chart tile as we did previously. Now we need to choose the type of plot we want. Since it is a scatter plot, click the chart type on the chart tile and choose scatterplot. Also, choose the x and y axes. Einblick will automatically plot the graph for us. It looks like the one below.

We can see the scatter plot between the total_bill and tip. Now let’s see some univariate plots. Firstly we will plot the countplot of the attribute ‘sex’. Drag the ‘Chart’ option into the canvas as we did in the previous step. Now, choose the vertical bar chart. Choose ‘sex’ as the x-axis and aggregate as the ‘count’ as we want to see the countplot. Now choose the y-axis as ‘sex’ so we will get a countplot as shown below.

As shown in the above graph, males are higher than females. Similarly, let’s see the countplots of the attributes ‘smoker’ and ‘day’. Create a ‘chart’ option as we did before and choose the ‘vertical bar’ type of chart. Choose the x-axis as ‘smoker’ and the aggregate as ‘count’. Choose the y-axis as ‘smoker’. Einblick will create a countplot of the ‘smoker’ attribute.

Similarly, create another ‘chart’ tile in the canvas and repeat all the steps we did in the previous one. But now choose ‘day’ as the x-axis and ‘count’ as the aggregate ‘day’ as the y-axis. We will see the countplot. The result will look like the one below.

We can see that the non-smokers are higher than the smokers and the count on Saturday is the highest, which means the most customers visited on that day.

If you observe, we never used any code. It just drags and drops as we do in Tableau or PowerBI. But this is much more simple to visualise data and get insights rapidly. So far, we have performed univariate analysis by creating three plots that analyze a single variable, in addition to describing the data and creating a bi-variate plot, as shown above, in a simple manner.

Conclusion

EDA is an essential task in machine learning model building. It gives us quick insights into what our data is. So having good tools that simply give us results is necessary. Einblick does this job perfectly without any hassle. Einblick is

  • Simple to use
  • No to low code tool
  • Offers a wide range of operations to be performed on the dataset
  • Offers real-time collaboration among the users

Try this simple tool for your next EDA project and surprise everyone. Feel free to comment below if you have any questions.

Image-1 source: Einblick Website

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

AutoMLblogathondatasetsEDAmachine learning
U
UPPU RAJESH KUMAR

Data Science Enthusiast. Interested in NLP, computer vision.

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