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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

Making Exploratory Data Analysis Sweeter with Sweetviz 2.0

Kopal
Kopal Last Updated : 24 Oct, 2024
4 min read

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

What is Exploratory Data Analysis?

The very first step in data science is exploratory data analysis, aka EDA. All types of data models do not fit all data types, so it is better to thoroughly analyze the data before proceeding further. For example, mathematical operations cannot be applied to categorical data, or the issue of missing values in the dataset must be addressed.
 
The accuracy of the data model directly depends on the quality of the data. However, in the real-world, data is collected from various sources and must be handled accordingly to reduce the repercussions.
EDA being the initial step of data mining, helps in getting insight into data without any assumptions. This helps in forming the hypotheses. The fundamental ingredients of EDA are data summarization, data description and inferences, and data visualization.
 
The traditional way of doing EDA in Python involves tools such as NumPy, Pandas, Scipy, and Matplotlib. However, a quick sneak peek into data can be done using Sweetviz.

Introduction to Sweetviz 2.0

Sweetviz 2.0 is an open-source pandas-based library to perform the primary EDA task without much hassle or with just two lines of code. It also generates a summarised report with great visualizations.

Installation using pip

The basic command to install a package using pip is:

pip install sweetviz

Alternatively, use the following command inside Notebook/Colab

!pip install sweetviz

 

How to Use?

Let us get our hands dirty and start writing the code… (Feel free to use the source code here)

For this purpose, we will be using the Student Performance dataset (get it here). It is mixed data, that is, both numerical and categorical data are present. The dataset consists of 1000 students record with a total of eight features, viz.,

  1. gender: categorical
  2. race/ethnicity: categorical
  3. parental level of education: categorical
  4. lunch: categorical
  5. test preparation course: categorical
  6. math score: numerical
  7. reading score: numerical; and
  8. writing score: numerical

We will be using pandas for reading the csv (Comma Separated Values) file.

import pandas as pd
data=pd.read_csv('StudentsPerformance.csv')
# Printing data insights
print(data.info())
# Printing few data samples
print(data.head(5))

Sweetviz has a powerful function called analyze() that helps in analyzing the data at a glance.

# Analyzing data
report=sv.analyze(data)
# Generating report
report.show_html('eda_report.html')

Bang! Our report is ready in a split second.

The function show_html() generates a detailed report consisting of the following details:

 

sweetviz 2.0 page
sweetviz 2.0 image

If show_html() function is not supplied with any parameter, by default, it generates a file named ‘SWEETVIZ_REPORT.html’.

Apart from this, we can compare two datasets side-by-side. To have a glance at this, we would split the dataset into two halves.

# Spliting the data into two datasets
data1=data[0:400]
data2=data[400:]

Now, let us compare both of them side-by-side using

report_comp=sv.compare([data1,'DATA1'],[data2,'DATA2']) 
report_comp.show_html('Compare_Results.html')
Sweetviz 2.0 compare

If the function compare() is left with default parameters, refer to the two datasets by Dataframe and Compared, respectively.

Sweetviz 2.0 compare()

 

We can also perform target analysis, but currently, it only supports numerical or binary targets, rather than categorical targets. Let’s consider math scores as a target:

report_comp=sv.compare(data1,data2,'math score')
report_comp.show_html('Compare_Results.html')
DataFrame

 

Version 1.0 vs 2.0

Although working with Sweetviz makes EDA hassle-free, it does introduce some difficulties as well.

Firstly, the reports generated using the base OS module; hence it is incompatible with custom environments such as Google Colab. Secondly, the reports are in HTML format; therefore, the graphs cannot be plotted inline.

However, in version 2.0, these issues have been taken care of by a new feature show_notebook() which embeds the visualizations in the notebooks using an iframe. Also, you’ll be able to save a report in HTML format that could be accessed later.

Playing with Reports

The additional yet optional parameters make this task easy.

Version 2.0 allows the user to manipulate the appearance of the report with some parameters as –

 report_comp.show_html(filepath='report.html',
open_browser=True,
layout='vertical',
scale=0.7)
comparison

 

Another feature introduced in version 2.0 called show_notebook() displays the report within the notebook environment rather than any browser-based external environment using an IFRAME HTML element. Typical usage of this feature is demonstrated by the following lines of code –

report_comp.show_notebook(w=None, h=None,
scale=None,
layout='vertical',
filepath='E:/sweetviz_report.html')
Jupyter

 

where,

w and h: signifies the width and height of the window. It can be defined in absolute pixels (e.g. 400) or relatively (e.g. 70%).

filepath: saves the file in the directory with the name specified which can be accessed later

layout: there are two layout modes available, viz., widescreen and vertical

scale: this defines the scale of the report within the window. It is a fractional number ranging from 0 to 1.

Conclusion

In this short article, I outlined how to load data using pandas and have quick insights on the data using Sweetviz 2.0 in just a couple of lines of code.

 

References

  1. https://pypi.org/project/sweetviz/ [accessed: Jan-05-2021]
  2. https://www.kaggle.com/spscientist/students-performance-in–exams?select=StudentsPerformance.csv [accessed Jan-05-2021]

 

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Kopal
Kopal

Apart from being a programmer, I am a full-time researcher exploring into cutting-edge technologies, constantly expanding my knowledge horizons. An insatiable learning enthusiast, I actively seek out new challenges and opportunities for growth. Additionally, I write blogs where I share my insights on various programming and deep learning concepts.

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