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

Classification using Pyspark, DataBricks, and Koalas

S
Sanket Last Updated : 30 Oct, 2023
6 min read

Introduction

The volume of data collected worldwide has drastically increased over the past decade. Nowadays, data is continuously generated if we open an app, perform a Google search, or simply move from place to place with our mobile devices. This data generation has boosted business decision-making and operational efficiencies whilst advancing the field of predictive analytics. As a result, businesses and organizations must manage, store, visualize, and analyze massive volumes of important data.

Several specialized big data software and architecture solutions have been developed to handle this level of complexity and volume. This article will use big data tools like PySpark MLlib and Koalas to solve a classification problem.

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

Table of contents

What is PySpark and Koalas?

Big data platforms are especially created to handle enormous volumes of data that enter the system at high velocities and in a broad variety. These big data platforms often include a variety of servers, databases, and business intelligence tools that enable data scientists to manipulate data to discover trends and patterns.

PySpark is one such tool to handle large-scale data processing. MLlib is Spark’s machine learning (ML) library and is a wrapper over PySpark. To store and process data, this library employs the data parallelism technique.

Data scientists use pandas to explore data. Although it is one of the best tools for handling and analyzing data, it cannot handle large amounts of data. When their data volume increases, they need to choose and learn a new system from scratch, such as Apache Spark, to adapt and transform their current workload. Koalas bridges the gap by providing pandas-like APIs that work with Apache Spark. Because Koalas supports many operations that are difficult to execute with PySpark, such as visualizing data directly from a PySpark DataFrame, it is valuable not only for pandas users but also for PySpark users.

How to Use Koalas with Pyspark?

Carrying out exploratory data analysis, feature engineering, and model construction are the norm while developing machine learning models. I’ll try to touch on the aforementioned topic with Koalas. Pandas API is reportedly used by the majority of Data scientists for feature engineering and EDA tasks. I’ll show how to utilize Koalas to complete these tasks and PySpark to create the model.

I’ll use the Bank marketing campaigns dataset. It is a dataset that describes the outcomes of Portugal’s bank marketing activities. Campaigns were generally conducted through direct phone conversations, offering bank clients the opportunity to put a term deposit. If the client decides to put in a deposit after all marking efforts, the target variable is marked ‘yes,’ otherwise ‘no.’ We will create a model to predict the client’s potential to sign up for a bank term deposit.

Step 1: Import the Necessary Libraries

To install PySpark, you can read the official documentation.

To install Koalas use:

! pip install koalas
import databricks.koalas as ks
import os
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import confusion_matrix, classification_report
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use(‘ggplot’)
plt.rcParams[‘figure.figsize’] = (15, 10)

PySpark is not present on the system path by default. You can add it to the system path at runtime to make it importable. Use findspark to do this task.

import findspark
findspark.init()
findspark.find()

Now you can import PySpark.

from pyspark.ml.evaluation import BinaryClassificationEvaluator
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.classification import RandomForestClassifier , LogisticRegression

Step 2: Loading the Dataset using Koalas

data = ks.read_csv('bank-additional-full.csv',sep=";")
data.head(10)
koala | pyspark

Step 3: Data Preprocessing

We can easily use Koalas with Seaborn to generate graphs by converting the Koalas dataframe to a NumPy array.

data['y'] = data['y'].apply(lambda x: 0 if x=='no' else 1)
sns.countplot(data['y'].to_numpy())

plt.title('Deposit Distributions n (0: No || 1: Yes)', fontsize=14)
deposit distributions | pyspark

Similar to examining the data with Pandas, we can do the same with Koalas.

Moving further, we will create a list of categorical variables by selecting the columns with an object data type. Once we have a set of categorical variables, we will use get_dummies() to turn them into indicator variables.

#get the list of categorical columns
cat_col=[col for col in data.columns.values if data[col].dtype=='object']
# sepearting the numerical and categorical feature
df_cat=data[cat_col] 
df_num= data.drop(cat_col,axis=1)

df_cat_dum= ks.get_dummies(df_cat,drop_first=True)

df_features=ks.concat([df_num,df_cat_dum], axis=1)

sns.countplot(data['y'].to_numpy())

plt.title('Deposit Distributions n (0: No || 1: Yes)', fontsize=14)

We will now select features with a correlation greater than a certain threshold.

correlations = df_features.corr()
all_features = correlations['y'].index.to_list()
corr_y = correlations['y'].values
selected_features = []
for i in range(len(corr_y)):
    if abs(corr_y[i]) > 0.01:
        selected_features.append(all_features[i])
        print(all_features[i])
df_features = df_features[selected_features]

Now we will scale the features using a MinMax scaler.

from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
data_standardised = scaler.fit_transform(df_features.values)
df_std = ks.DataFrame(data_standardised)
df_std.columns = selected_features

Creating Feature Vectors

Now that we have created the features with the help of Koalas, we will convert the Koalas DataFrame into a Spark Data frame. Then, using PySpark’s VectorAssembler, we’ll build a feature vector to train the model.

df =df_std.to_spark()

features = df_features[selected_features].columns.to_list()

features.remove('y')


vectorAssembler = VectorAssembler(inputCols = features, outputCol = "vectors")

vpp_sdf = vectorAssembler.transform(df)


#to see data in jupyter notebook 

from IPython.display import HTML

v = vpp_sdf.select('vectors').toPandas().head()

HTML(v.to_html())
pyspark

Train Test Split

splits = vpp_sdf.randomSplit([0.7,0.3])
train_df = splits[0]
test_df = splits[1]

Training PySpark Model

We will use MLlib’s Random forest classifier. It takes two arguments: featuresCol (the feature vector) and labelCol (the label to predict).

rfc = RandomForestClassifier(labelCol="y", featuresCol="vectors")
rfc_model = rfc.fit(train_df)
predictions = rfc_model.transform(test_df)

Evaluate the Model

We have two options for evaluating the model: utilize PySpark’s Binary classification evaluator, convert the predictions to a Koalas dataframe and use sklearn to evaluate the model. One advantage of using the latter is that we can easily visualize the results.

Using PySpark, we can find the area under ROC with the help of this code:

evaluator = BinaryClassificationEvaluator(labelCol="y", rawPredictionCol="prediction", metricName='areaUnderROC')
predictionAndTarget = predictions.select("y", "prediction")
evaluator.evaluate(predictionAndTarget)

0.5777082763196241

Convert a Spark DataFrame to a Koalas DataFrame and simply compute the confusion matrix.

#convert spark dataframe to koalas dataframe
kdf_predictions = ks.DataFrame(predictions)
cm = confusion_matrix(kdf_predictions['y'].to_numpy(),kdf_predictions['prediction'].to_numpy())
sns.heatmap(cm, annot=True, cmap='viridis',fmt='g')
Spark DataFrame
classification_report(kdf_predictions['y'].values,kdf_predictions['prediction'].values)
pyspark

Conclusion

Koalas can help scale our Pandas code on Spark without learning a new framework from scratch by swapping out one package with another. In this article, we build a binary classifier using Pyspark. The following are some major takeaways from the article:

  • We learned how to use Koalas to process the data.
  • We created feature vectors using PySpark’s VectorAssembler.
  • Finally, we used a Random Forest classifier to train our model and evaluated the model using different methods.

Frequently Asked Questions

Q1. What is Koalas in Databricks?

A. Koalas is an open-source Python library in Databricks that provides a pandas-like interface for working with big data using Apache Spark, making it more accessible to Python users.

Q2. What is the difference between Databricks Koalas and pandas?

A. Databricks Koalas bridges the gap between pandas and big data tools like PySpark. While pandas is for small to medium-sized data, Koalas allows you to work with larger datasets using a similar API.

Q3. Is Koalas better than PySpark?

A. Koalas and PySpark serve different purposes. Koalas is ideal for users familiar with pandas who want to work with big data, while PySpark is a comprehensive big data framework with more extensive capabilities.

Q4. What is the difference between PySpark, Databricks, and Koalas?

A. PySpark is an open-source Apache Spark library for distributed data processing. Databricks is a cloud-based platform for big data analytics. Koalas is a library within Databricks, bridging pandas and PySpark for easier big data analysis in Python.

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

S
Sanket

I am a decision analyst with knowledge and research experience in the field of data science. Passionate about building models that fix problems. Relevant skills include machine learning, data visualization, problem solving, programming, and creative thinking.

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

Rishab
Rishab

Very insightful thanks sir

123

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