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Sales Forecasting
Workflows

  • 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

  • 01-Combining Sales, Store And Features Datasets

  • 02 - Performing Data Validation and Indexing on Weekly Sales Data

  • ​03 - Performing Feature Selection & Correlation on Weekly Sales Data

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Churn Prediction
Workflows

  • 01 - Performing Feature Engineering on Sales Transaction Data

  • 02 - Performing EDA on Sales Transaction Data - 1

  • ​02 - Performing EDA on Sales Transaction Data - 2

  • 03 - Creating and Training Churn Classification Model

  • 04 - Predicting Customer Churn

Reports

  • Churn Reports

Application

  • Churn Analytical App

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CPG Customer Lifetime Value Prediction
Workflows

  • 01 - Conducting EDA on Transactional Data - Part 01

  • 02- Conducting EDA on Transactional Data - Part 02

  • 03 - Preparing Data

  • 04 - Training Regression Models

  • 05 - Training a Model for Customer Segmentation

  • 06 - Predicting Customer Segment

  • 07- Computing Clusterwise Average Feature Data

Reports

  • RPT Customer CLTV

Application

  • CLTV Prediction App

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CPG Market Basket Analysis
Workflows

  • 01-Conducting EDA on Customer Invoice Data

  • 02-Cleaning Invoice Data

  • 03-Preparing Data for Clusters

  • 04-Evaluating Customer Clusters

  • 05-Generating Cluster Wise FPG Rules

  • 06-Recommending Product for Cluster Using FPG

Reports

  • Market Basket Analysis Report

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CPG On-Shelf Availability
Workflows

  • 01- Conducting EDA for On-Shelf Availability Data

  • 02- Preparaing OSA Data

  • 03- Preparing Out Of Stock PhantomInv And SafetyStock Data

  • 04- Preparing Out Of Stock Identify InventoryEvents Data

  • 05- Conducting EDA for On-Shelf Availability Data

  • 06- Predicting On-Shelf Availablility Using Time Series Model

Reports

  • OSA Report On Shelf Availability

Retail
Workflows

  • 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

Reports

  • Clickstream Data Analysis Report

  • User Engagement Analysis Report

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Product Recommendation
Workflows

  • 01 - Performing EDA on Customer Invoice Data

  • 02 - Performing Data Cleaning on Invoice Data

  • 03 - Preparing Data for Clustering

  • 04 - Segmenting and Profiling Customers

  • 05 - Creating Custer-wise Association Rule Using FPG

  • 06 - Recommending Cluster-wise Product Using FPG

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Customer Segmentation
Workflows

  • 01 - Performing EDA on Transactional Data

  • 02 - Cleaning Transactional Data

  • 03 - Performing Feature Engineering Campaign Data

  • 04 - Creating Cluster on Customer Data Using K-means

  • 04a - Clustering Customer Call Charge Data

  • 04b - Clustering Customer Call Duration Data

  • 04c - Clustering Customer Call Volume Data

  • 05 - Analyzing Clusters

Reports

  • Customer Voice Data Clusters

  • Customer Segmentation Summary

Application

  • Customers Segmentation App

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CPG Demand Forecasting
Workflows

  • 01- Conducting Store Product Data EDA

  • 02- Forecasting Store Product Demand

  • 03- Analyzing Store Product Demand Forecasting

  • Predicting Demad Using AutoML Regression Models

Reports

  • Report Sales Store Forecasting

Application

  • Demand Forecasting

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CPG Sales Quantity Forecasting
Workflows

  • Forecasting Quantity Using Arima

  • Forecasting Quantity Using Prophet

  • Forecasting Quantity Using Arima Trial

  • Forecasting Quantity for Sub-Groups Using Arima

Reports

  • Monthly Forecast Per Segment Per Category

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Quality Control
Workflows

  • 01- Performing EDA on Human Error Data

  • 02- Performing EDA on Raw Material Degradation

  • 03- Performing EDA on Sensor Failure Data

  • 04- Training Model to Identify Human Error

  • 05- Training Model to Identify Material Degradation

  • 06- Training Model for Sensor Failure Detection

  • 07- Predicting Next Day Product Price

  • 08- Product Demand Prediction on Input Dataset

  • 09-Training Model for Product Demand Forecasting

  • 10- Predicting Final Quality

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