Interview Prep
Career
GenAI
Prompt Engg
ChatGPT
LLM
Langchain
RAG
AI Agents
Machine Learning
Deep Learning
GenAI Tools
LLMOps
Python
NLP
SQL
AIML Projects

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

SweetViz Library – EDA in Seconds

Ronil
Ronil Last Updated : 01 May, 2021
5 min read
This article was published as a part of the Data Science Blogathon.

Human beings are visual creatures, It means that the human brain processes image 60,000 times faster than text, and 90 percent of information transmitted to the brain is visual. Visual information makes it easier to collaborate, and generate new ideas that impact organizational performance. That’s the only reason that data analyst spends their maximum time in data visualization.

Introduction to EDA : 

EDA is a data analysis process, where multiple techniques are used to better understand the dataset being used. It is basically used to identify a human error, missing values, or outliers. It extracts useful variables and removes useless variables. It is used to understand the relationship between variables or features. It ultimately maximizes your insights into a dataset and minimizes potential errors later in the process.

What is SweetViz Library?

SweetViz Library is an open-source Python library that generates beautiful, high-density visualizations to kickstart EDA with just two lines of code. Output is a fully self-contained HTML application. The system is built around quickly visualizing target values and comparing datasets. Its goal is to help quick analysis of target characteristics, training vs testing data, and other such data characterization tasks.

 

Some of the features of SweetViz Library

  1. It shows how a target value(dependent features) relates to other features.
  2. It integrates associations for numerical (Pearson’s correlation), categorical (uncertainty coefficient), and categorical-numerical (correlation ratio) datatypes seamlessly, to provide maximum information for all data types.
  3. It automatically detects numerical, categorical, and text features, with optional manual overrides.
  4. Summary information about the type, unique values, missing values, duplicate rows, most frequent values.
  5. Numeric Analysis such as min/max/range, quartiles, mean, mode, standard deviation, sum, median absolute deviation, coefficient of variation, kurtosis, skewness.

Installation : 

pip install sweetviz

Dataset Description : 

Here I have used House Price Prediction Dataset. You can download it from here.

Here’s a brief version of what you’ll find in the data description file.

  • SalePrice – the property’s sale price in dollars. This is the target variable that you’re trying to predict.
  • MSSubClass: The building class
  • MSZoning: The general zoning classification
  • LotFrontage: Linear feet of street-connected to the property
  • LotArea: Lot size in square feet
  • Street: Type of road access
  • Alley: Type of alley access
  • LotShape: General shape of the property
  • LandContour: Flatness of the property
  • Utilities: Type of utilities available
  • LotConfig: Lot configuration
  • LandSlope: Slope of property
  • Neighborhood: Physical locations within Ames city limits
  • Condition1: Proximity to main road or railroad
  • Condition2: Proximity to main road or railroad (if a second is present)
  • BldgType: Type of dwelling
  • HouseStyle: Style of dwelling
  • OverallQual: Overall material and finish quality
  • OverallCond: Overall condition rating
  • YearBuilt: Original construction date
  • YearRemodAdd: Remodel date
  • RoofStyle: Type of roof
  • RoofMatl: Roof material
  • Exterior1st: Exterior covering on house
  • Exterior2nd: Exterior covering on house (if more than one material)
  • MasVnrType: Masonry veneer type
  • MasVnrArea: Masonry veneer area in square feet
  • ExterQual: Exterior material quality
  • ExterCond: Present condition of the material on the exterior
  • Foundation: Type of foundation
  • BsmtQual: Height of the basement
  • BsmtCond: General condition of the basement
  • BsmtExposure: Walkout or garden level basement walls
  • BsmtFinType1: Quality of basement finished area
  • BsmtFinSF1: Type 1 finished square feet
  • BsmtFinType2: Quality of second finished area (if present)
  • BsmtFinSF2: Type 2 finished square feet
  • BsmtUnfSF: Unfinished square feet of basement area
  • TotalBsmtSF: Total square feet of basement area
  • Heating: Type of heating
  • HeatingQC: Heating quality and condition
  • CentralAir: Central air conditioning
  • Electrical: Electrical system
  • 1stFlrSF: First Floor square feet
  • 2ndFlrSF: Second floor square feet
  • LowQualFinSF: Low quality finished square feet (all floors)
  • GrLivArea: Above grade (ground) living area square feet
  • BsmtFullBath: Basement full bathrooms
  • BsmtHalfBath: Basement half bathrooms
  • FullBath: Full bathrooms above grade
  • HalfBath: Half baths above grade
  • Bedroom: Number of bedrooms above basement level
  • Kitchen: Number of kitchens
  • KitchenQual: Kitchen quality
  • TotRmsAbvGrd: Total rooms above grade (does not include bathrooms)
  • Functional: Home functionality rating
  • Fireplaces: Number of fireplaces
  • FireplaceQu: Fireplace quality
  • GarageType: Garage location
  • GarageYrBlt: Year garage was built
  • GarageFinish: Interior finish of the garage
  • GarageCars: Size of garage in car capacity
  • GarageArea: Size of garage in square feet
  • GarageQual: Garage quality
  • GarageCond: Garage condition
  • PavedDrive: Paved driveway
  • WoodDeckSF: Wood deck area in square feet
  • OpenPorchSF: Open porch area in square feet
  • EnclosedPorch: Enclosed porch area in square feet
  • 3SsnPorch: Three season porch area in square feet
  • ScreenPorch: Screen porch area in square feet
  • PoolArea: Pool area in square feet
  • PoolQC: Pool quality
  • Fence: Fence quality
  • MiscFeature: Miscellaneous feature not covered in other categories
  • MiscVal: $Value of miscellaneous feature
  • MoSold: Month Sold
  • YrSold: Year Sold
  • SaleType: Type of sale
  • SaleCondition: Condition of sale

Let’s begin : 

### importing required libraries
import pandas as pd
import sweetviz 
### reading training dataset
train = pd.read_csv("train.csv")
### reading testing dataset
test = pd.read_csv("test.csv")

Let’s analyze our dataset using Sweetviz Library : 

my_report = sweetviz.analyze([train, "Train"],target_feat='SalePrice')

Here “sweetviz.analyze()” is just a function that analyzes the data frame and we have to pass the target feature as a parameter. Always remember the parameters should be in form of a list. You can check out the documentation and play with different parameters.

sweetviz library import

Let’s see the whole report : 

### create a whole report in form of HTML file
my_report1.show_html('Report.html')

Whenever you will hover on a particular feature, it will show full detail about that feature. I hover on the dependent feature and look at what I got.

sweetviz library report

Here dependent feature is highlighted in black color and on the right-hand side full information about the dependent variable is mentioned. All the information about the dataset is basically shown in this HTML file. If our input features are numeric features then sweetviz will show these details.

In the case of categorical features, it will show these details.

sweetviz library dependent feature

Association: Hovering our mouse over the “Associations” button in the summary will make the Associations’ graph appear on the left-hand side.

association

Here in this problem statement, we are using a huge dataset and it took only somewhere around 10 to 12 seconds to visualize the whole dataset.

Let’s compare the Training and Testing dataset using Sweetviz : 

### in case of testing data pass seperate list for testing data as parameter
my_report1 = sweetviz.compare([train, "Train"], [test, "Test"], "SalePrice")
my_report.show_html('Report2.html')

For your better understanding, I have added long screenshots. In the case of comparing training and testing data each and every feature is compared with testing data. Whenever you will perform it practically that time you will understand it in a better way.

Conclusion 

It easily gives us a significant jump-start when you start looking at a new dataset. It’s worth pointing out I also find it useful later in the analysis process, for example during feature generation, to get a quick overview of how new features play out.

EndNote 

I hope you enjoyed this article. Any question? Have I missed something? Please reach out on my LinkedIn or drop a response below. And finally, … it doesn’t go without saying,

Thank you for reading!

Cheers!

Ronil

SweetViz Library is an open-source Python library that generates beautiful, high-density visualizations to kickstart EDA with just two lines of code.

Ronil
Ronil

Free Courses

Generative AI 4.7

Generative AI - A Way of Life

Explore Generative AI for beginners: create text and images, use top AI tools, learn practical skills, and ethics.
Large Language Models 4.5

Getting Started with Large Language Models

Master Large Language Models (LLMs) with this course, offering clear guidance in NLP and model training made simple.
Prompt Engineering 4.6

Building LLM Applications using Prompt Engineering

This free course guides you on building LLM apps, mastering prompt engineering, and developing chatbots with enterprise data.
LlamaIndex 4.8

Improving Real World RAG Systems: Key Challenges & Practical Solutions

Explore practical solutions, advanced retrieval strategies, and agentic RAG systems to improve context, relevance, and accuracy in AI-driven applications.
RAG systems using LlamaIndex 4.7

Microsoft Excel: Formulas & Functions

Master MS Excel for data analysis with key formulas, functions, and LookUp tools in this comprehensive course.

Recommended Articles

Responses From Readers

Clear

Write for us

Write, captivate, and earn accolades and rewards for your work
  • Reach a Global Audience
  • Get Expert Feedback
  • Build Your Brand & Audience
  • Cash In on Your Knowledge
  • Join a Thriving Community
  • Level Up Your Data Science Game
imag

We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.

Show details