CPG Customer Lifetime Value Prediction

• Conducting EDA on Transactional Data - Part 01

• Conducting EDA on Transactional Data - Part 02

• Preparing Data

• Training Regression Models

• Training a Model for Customer Segmentation

• Predicting Customer Segment

• Computing Clusterwise Average Feature Data

CPG Demand Forecasting

• Conducting Store Product Data EDA

• Forecasting Store Product Demand

• Analyzing Store Product Demand Forecasting

• Predicting Demad Using AutoML Regression Models

CPG Market Basket Analysis

• Conducting EDA on Customer Invoice Data

• Cleaning Invoice Data

• Preparing Data for Clusters

• Evaluating Customer Clusters

• Generating Cluster Wise FPG Rules

• Recommending Product for Cluster Using FPG

CPG On-Shelf Availability

• Conducting EDA for On-Shelf Availability Data

• Preparaing OSA Data

• Preparing Out Of Stock PhantomInv And SafetyStock Data

• Preparing Out Of Stock Identify InventoryEvents Data

• Conducting EDA for On-Shelf Availability Data

• Predicting On-Shelf Availablility Using Time Series Model

CPG Sales Quantity Forecasting

• Forecasting Quantity Using Arima

• Forecasting Quantity Using Prophet

• Forecasting Quantity Using Arima Trial

• Forecasting Quantity for Sub-Groups Using Arima

Churn Prediction

• Performing Feature Engineering on Sales Transaction Data

• Performing EDA on Sales Transaction Data - 1

• Performing EDA on Sales Transaction Data - 2

• Creating and Training Churn Classification Model

• Predicting Customer Churn

Connectors

• Reading File from URL

• Saving to Elastic Search

• Reading from HIVE Database

• Reading JSON from URL

Customer 360

• Preparing Customer Credit Card Data

• Predicting Customer Churn

• Creating EDA Report on Customer Churn Data

Customer Segmentation

• Performing EDA on Transactional Data

• Cleaning Transactional Data

• Performing Feature Engineering Campaign Data

• Creating Cluster on Customer Data Using K-means

• Clustering Customer Call Charge Data

• Clustering Customer Call Duration Data

• Clustering Customer Call Volume Data

• Analyzing Clusters

Data Engineering

• Performing Data Profiling on Employee Data

• Performing Schema Validation

• Performing Address Validation

• Performing Multi-level Data Validation

• Performing Complex ETL on Customer Data

• Performing Advance Data Preparation

• Performing Data Validation on Housing Data

• Performing Complex ETL on Customer Data

• Performing Simple ETL on Customer Data

• Transforming Housing Data

• Joining Multiple Datasets

• Analyzing NYC Taxi Average Speed

• Reading and Writing to HIVE Table

• Removing Duplicate Rows

• Performing Data Wrangling on Transactional Data

• Manipulating Housing Data Using SQL

• Preparing Data Using SCALA

• Performing Data Cleaning on Titanic Data

• Flattening And Exploding JSON Data

Data Preparation

• Handling Missing and Categorical Data

• Extracting Date Time Fields

• Reading and Processing JSON from a URL

• Removing Duplicate Customer Records

• Performing Data Cleaning on Titanic Dataset

• Performing Data Quality on Email Fields

• Reading CSV File via REST and Parsing

• Converting String to Timestamp

• Removing Rows with Null Values

• Removing Special Characters Using Regex

• Filtering Columns

• Concatenating Multiple Columns

• Cleaning the Data

• Searching and Replacing Text

• Casting Datatype

• Cleaning and Transforming the Data

• Extracting Text Using OCR

• Performing Data Profiling on Housing Data

• Removing Duplicate Rows

• Selecting, Dropping, and Renaming Columns

• Manipulating String Columns

• Imputing Null Values

• Identifying Unique Values in Columns

Data Profile Explore

• Performing Data Exploration on Housing Data

• Performing Data Profiling on Loan Data

• Performing Data Profiling on Sales Data

• Generating Correlation Matrix on Sales Data

• Detecting Outliers on WHO Data

• Cleaning WHO Data

• Performing Data Profiling on Loan Data Part-01

• Performing Data Profiling on Loan Data Part-02

Data Quality

• Performing Data Profiling on Employee Dataset

• Validating Column Values to Be not Null

• Validating Column Values to Be Null

• Validating Column With Unique Values

• Validating Table Row Count to be in Given Range

• Performing Data Quality on Housing Data

• Validating Column Values to be in Given Range

• Performing Data Cleaning and Data Quality on Employee Data

• Validating Multiple Columns

• Reading Flat File

Data Science

• Predicting Wine Quality Using MultiLayer Perceptron Classifier

• Building H20 DRF Model for Bike Sharing

• Saving RF Model on Type of Fault in Steel Plates

• Spam Detection Using TFIDF

• Building Linear Regression Model for Farmers Market

• Building GBT Regression Model for Bike Sharing

• Profiling Telco Churn Data

• Building Model Using Decision Tree Classifier for Telco Churn

• Analyzing Correlation and Summary Statistics for Housing Data

• Building Model Using RFC for Telco Churn

• Developing H20 RFC Model for Telco Churn

• Building H20 K-means Cluster for Bike Sharing Data

• Analyzing Telco Churn Data

• Building K-means Cluster for Housing Data

• Predicting Click-through with GBT Boosting

• Building Spark-ML Pipeline Model on Spam Data

• Predicting Acceleration Of Car Models Using Linear Regression

• Predicting Spam and Evaluating Model

• Training XGBoost Model on IRIS Data

• Building Neural Network Regression Model for Bike Sharing Data

• Building GBT Regression Model for Car Data

• Performing Feature Selection and Importance in WHO Data Analysis

• Building Decision Tree Regression Model on Car Data

• Building K-means Cluster Model on Housing Data

• Demonstrating Parameter Passing Process on Employment Data

• Performing Chi-Square Test for Feature Selection

• Building RFC & Logistic Regression Model for Churn Prediction

• Performing Comprehensive Sales Data Analysis with Various Models

• Generating Covariance and Correlation Matrix on Housing Data

• Generating Cross Tab Matrix on Housing Data

• Performing Dimensionality Reduction Using PCA on Housing Data

• Exploring Housing Data

• Analysing and Decomposition of Bike Sharing Features

• Building H20 DRF Models on Bike Sharing Data

• Building H2O GBM Model on Bike Sharing Data

• Building H20 GLM Model

• Building H2O GLRM Model on Credit Card Fraud Data

• Building H20 Isolation Forest Model on Credit Card Fraud Data

• Building H20 K-means Model on Bike Sharing Data

• Building H20 Neural Network Model

• Buidling H20 XGBoost Model on UCI Credit Card Data

• Building Linear Regression Model on Household Power Consumption Data

• Building K-means Cluster Model on Housing Data

• Building RFC Model for Predicting House Price

• Building Logistic Regression Model for SMS Spam Detection

• Building H20 Isolation Forest Model for Anomaly Detection

• Buidling Linear Regression Model for Storewise Retail Stock Prediction

• Building Logistic Regression Model for Credit Card Fraud Prediction

• Imputing Null Values with Mean, Median & Constant

• Reading CSV and JSON File

• Detecting Outlier for Housing Data

• Detecting Diabetes Using Logistic Regression Model

• Detecting Diabetes Using Logistic Regression Model

• Processing Housing Data

• Scaling Features Using Standard And Min Max Scaler

• Identifying Skewness in Housing Data

• Splitting Dataset Using Stratified Sampling

• Handling Categorical Data with String Indexer and OneHotEncoder

• Sampling Housing Data Using Standard Split Method

• Performing Data Profiling on Bike Sharing Data

• Validating Data Using Cross Validator

• Cleaning and Generating Features in NFL Data

• Implementing Sagemaker XGBoost

• Applying SMOTE Oversampling to Address Imbalanced Data

• Implementing SparkML Pipeline

• Improving Spam Detection with Hyperparameter Tuning

• Predicting House Price Using XGBoost Regression Model

• Predicting Defects in Steel Plates Using Random Forest Classifier

• Predicting Weekly Sales Using Random Forest Regression Model

• Selecting Churn Prediction Model Using Grid Search & Cross-Validation

• Normalizing Housing Data

• Building Perceptorn Network on Iris Data

Financials

• Detecting Credit Card Anomaly Using H2O Isolation Forest

• Credit Card Fraud Auto Encoder - Deep Neural Network

• Exploring Bank Marketing Campaigns Using Categorical Variables

• Creating Random Forest Model on Bank Marketing Campaign Data

• Exploring Bank Marketing Campaigns Using Numerical Variables

• Analyzing Credit Card Data

• Calculating Moving Average on Stocks Data

Generative AI

• Create-VectorDB-Embeddings-for-PDF

• PDF-Translation

• PDF-Summarisation

• ScrapeWebpage-Create-VectorDB-Embeddings

• HuggingFace-Natural-Language-Inference

• HuggingFace-Sentiment-Analysis

Healthcare

• Identifying Diabetic Status in Patients

• Processing Patient Data

Healthcare EHR

• Preparing EHR Patient Risk Data - Part-01

• Preparing EHR Patient Risk Data - Part-02

• Exploring EHR Patient Risk Data

• Identifying Patient Risk Using RF Model

Healthcare EHR Patient Risk

• Preparing Patient Risk Data - Part 01

• Preparing Patient Risk Data - Part 02

• Exploring Patient Risk Data

• Identifying Patient Risk Using Classification Model

Healthcare Optimize Hospital Operation

• Exploring Hospital Patient Data EDA

• Classifying Severity of Illness

• Classifying Patients Number of Stays in Hospital

IT Operations

• Analyzing Apache Logs

NLP

• Fetching Name Using OpenNLP

Price Elasticity

• Predicting Volume on New Product Prices and Creating Profit Parabola

• Creating Product Wise Linear Model and Calculating Profit

• Performing Data Profiling on Transactional Data

Product Recommendation

• Performing EDA on Customer Invoice Data

• Performing Data Cleaning on Invoice Data

• Preparing Data for Clustering

• Segmenting and Profiling Customers

• Creating Custer-wise Association Rule Using FPG

• Recommending Cluster-wise Product Using FPG

Quality Control

• Performing EDA on Human Error Data

• Performing EDA on Raw Material Degradation

• Performing EDA on Sensor Failure Data

• Training Model to Identify Human Error

• Training Model to Identify Material Degradation

• Training Model for Sensor Failure Detection

• Predicting Next Day Product Price

• Product Demand Prediction on Input Dataset

• Training Model for Product Demand Forecasting

• Predicting Final Quality

Read/Write Files

• Reading CSV File

• Saving Output in JSON Format

• Saving Output in Parquet Format

• Reading Images from PDF File and Perform OCR

• Reading PDF File

• Reading Excel File

• Reading Multi-line JSON File

• Reading Parquet File

• Analyzing Retail Order Cancellation

Recommendations

• Predicting Movie Rating

• Performing Data Profiling on Movie Ratings Dataset

• Performing Data Exploration on Movie Ratings Dataset

Retail

• Preparing Retail Data - Part 01

• Preparing Retail Data - Part 02

• Demonstrating Complex Customer ETL

• Removing Duplicates from Retail Data

• Analyzing Clickstream Data

• Analyzing Retail Data

Sales Forecasting

• Building Machine Learning Model

• Cleaning Sales Data

• Performing Data Profiling on Sales Data

• Calculating Cumulative Weekly Sales for All Departments

• Cleaning Stores' Sales Data

• Calculating Store-wise Average Weekly Sales

• Analyzing Stores Type Data Using Bubble Chart

• Combining Sales, Store And Features Datasets

• Performing Data Validation and Indexing on Weekly Sales Data

• Performing Feature Selection & Correlation on Weekly Sales Data

Streaming

• Streaming Bike Sharing Data Using Kafka

• Streaming JSON Events using File Streaming

• Structured Streaming Using File Sink

• Streaming Data

• Streaming Flight Delay Data Using Kafka

• Streaming Socket TextStream

• Streaming Structured Data and Joining CSV Files

• Streaming Structured Data and Joining Hive Table

• Streaming Structured Data

• Recovering Hive Partition

Timeseries

• Analyzing Monthly Passengers Distribution

• Analyzing Product Stock Data

• Forecasting Air Passengers Using Prophet Model

• Forecasting Air Passengers with Seasonality Using Prophet Model

• Forecasting Air Passengers Using SARIMAX Model

• Forecasting Air Passengers Using Multivariate Prophet Model

• Forecasting Safety Stock for Inventory Using Prophet Model

• Forecasting US Quaterly GDP using Multivariate VAR Model

• Generating Stock Forecasts

• Forecasting Daily Temperature

• Performing Feature Engineering for Time Series Data

• Predicting Earthquake Using Random Forest

• Forecasting Air Passengers Using ARIMA Model

• Forecasting Air Passengers Using Multivariate SARIMAX Model

Visualization

• Analyzing Housing Data Using Boxplot and Subplots

• Exploring Yearly Monthly and Weekly Distribution Graphs

• Creating Subplots Graph on Housing Data

• Analyzing Telco Customer Churn

• Documenting Using Print Rich Text Node

• Printing Output Using PrintNrows Node

• Analyzing Housing Data Using Line Graph

• Analyzing Housing Data Using Graph Group by Column

• Plotting Gauge Graph on Product Data

• Plotting Bubble Graph on Product Data

• Plotting Boxplot on Housing Data

• Plotting Subplots on Housing Data

• Plotting Boxplot on Housing Data

• Plotting Graph Column Values by Count

• Exploring NYC Taxi Average Speed

• Plotting Graph Values on Train Data

• Plotting Gauge & Bubble Graph on NYC Taxi Data