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AI and ML Applications in Media Industry Ram - Jan 28, 2023

There are multiple dimensions to Media and Entertainment industry. Some of the organizations within Media & Entertainment are acting as distribution channels of the contents and others are acting as content producers. Based on these the relevance of the use-cases can vary significantly. Also, whether the distribution of the contents …

Reducing RTO Orders for Your Ecommerce using AI/ML Ram - Jan 09, 2023

Return to origin or RTO is a common term used in e-commerce. A delivery is marked as RTO by delivery partner when the order could not be delivered due to issue with the delivery address, or the buyer is not responding. % RTO is ratio of orders that could not …

Brand Tracking using Custom NER using SpaCY V3 Ram - Jan 01, 2023

Customers write reviews and share their experiences about the brands and products. Extracting structured information from the review data and perform aspect-based sentiment analysis. In this scenario, the review data is about the beauty products and customers’ beauty related issues. For tracking brand mentions from the reviews across period, we …

Complete tutorial on NER using spaCy Python Ram - Dec 20, 2022

spaCy is a free, open-source library for Natural Language Processing (NLP) in Python. It facilitates Part-of-speech (POS) Tagging, Named Entity Recognition (NER), Text Classification and many more. In this blog, we will use spaCy for Named Entity Recognition (NER). The spaCy has a pre-trained model that helps in extracting named …

Real world Applications of Named entity recognition (NER) Ram - Dec 20, 2022

Named entity recognition (NER) is a subfield of natural language processing (NLP). In this blog, we will discuss the following • Named entity recognition -NER Overview • Applications and Use-cases of NER or NER Projects In the next blog, we will show you end to end steps complete NER Project …

AI/ML Use-Cases in Ecommerce Ram - Dec 16, 2022

Ecommerce is at the forefront in leveraging AI/ML Innovations in transforming customer experience and delivering value to their stakeholders. There are multiple business models and associated challenges; hence the use-cases may be slightly different for the different ecommerce players. For example, for closed marketplace like Tata CLiQ fake or counterfeit …

ML Based Ranking for WTA Players Ram - Dec 04, 2022

For WTA, Players are ranked based on points earned in the latest 52 weeks. The cumulative points are considered based on maximum of 16 tournaments for singles and 11 for doubles. In this blog, we will use historical ranking and statistics from the matches to develop ML based model to …

Learn R for Data Science Ram - Nov 29, 2022

In this blog, we will discuss foundational functionalities of R * Overview on R and R Studio * Data Type in R * Basic R Objects * Data Frame Manipulations * Reading and Writing Data in R * Control Statements in R

Data Science and ML Ranking and Recommendation Case Studies Ram - Nov 18, 2022

We have collated datasets, prepared the context and details for you to learn Ranking Algorithms. Ready to use datasets for Learning to Rank algorithms.

Applications of Ranking Algorithms Ram - Nov 12, 2022

In this blog, we will describe application of Ranking Algorithms for 5 different sectors with high details on how ranking algorithms are applied there. For each of these sectors, we will take up a scenario company to make very specific and relevant for you to appreciate the context. 1. Music …

Apriori algorithm for Market Basket Analysis Explained with Example Ram - Nov 11, 2022

Market Basket Analysis is a common Machine Learning Application in Retail and Ecommerce. In the previous blog, we have explained the applications and role of Market Basket Analysis (MBA. In this blog, our plan is to explain Apriori Algorithm steps with an example. The algorithm has two steps 1) Candidate …

Applications of Market Basket Analysis in Retail or Ecommerce Ram - Oct 28, 2022

There is a long list of Machine Learning applications in Retail or Ecommerce. One of the common applications is using Market Basket Analysis (MBA) for cross-sell and in-store product placements. In this blog, we will discuss provide you non-technical description of Market Basket Analysis and its applications in Ecommerce and …

6 Hats of Data Scientist Ram - Oct 02, 2022

There is a long list of questions people have around Data Science and Data Scientists considering huge curiosity and interest around AI and ML. In this blog, I am sharing my perspective on • What does a data scientist do? • What is data scientist? • What does a data …

Step by Step Learning to Rank Model Development for Ecommerce Ram - Sep 18, 2022

In this blog, we will describe application of Learning to Rank Algorithm for Ecommerce Search. The learning to rank algorithms are relevant for several scenarios in Ecommerce e.g., ranking product for search, ranking product recommendations or showing the most relevant products on category or brand pages. We will describe about …

Relevance Ranking Metric-NDCG Ram - Sep 04, 2022

Objectives of Relevance Ranking is to position relevant products on the top. When relevance rank is measured, we need to consider the distributions of the clicks and if more clicks on the top products, there can be higher relevant to the customers or users. For measuring effectiveness of the relevance …

Ranking Evaluation Metric - Mean Reciprocal Rank Ram - Aug 26, 2022

When results are shown for a search query, we want to show the most relevant products on the top. For capturing the relevance, one way is to find which products are being clicked by the users. In the best scenario, the users will be clicking the top product. For example, …

Search and Relevance Ranking – Simplified Ram - Aug 20, 2022

In this blog, we will discuss on the role of search, relevance and ranking for ECommerce Platform. Also, a detailed approach of data preparation will be discussed • Search in ECommerce • Relevance Ranking • Data Set for Relevance Ranking • Features for Ranking Model Develop

E-commerce Product Ranking – Factors and Considerations Ram - Aug 06, 2022

Ecommerce platforms have millions of products and showing the most relevant products to customers/users is pertinent. Machine Learning based Learning to Rank algorithms can help in displaying most relevant products for a given context. Before start developing product ranking algorithm for e-commerce, the understanding the broader context and consideration is …

AI/ML for Reducing Returns in Ecommerce Ram - Aug 03, 2022

In this blog, I will share a point of view to address higher product return challenges using AI/ML. • Product Returns in Ecommerce • Reasons of Product Returns • Ideas to reduce returns (cancellations post return) Higher level of order cancellations and returns has huge impact on profitability for the …

Step to Build Email Spam Classification Model Ram - Jul 24, 2022

We get hundreds of emails every day and not all the emails are relevant to us. We often get irritated with the irrelevant emails and wish to segregate the spam emails from the genuine ones. Machine Learning algorithms can do this job and quite effectively. In this blog, we will …

Product Returns and Cancellations in Ecommerce Ram - Jul 23, 2022

As a customer, we enjoy having easy return policy while shopping online. And of course, it has played an important role toward ecommerce adoption in India and across the world. Most of the customers read and review return policies before placing the orders online. As we know, most of the …

SIP Investors - who will terminate in next 30 days Ram - Jul 16, 2022

There around 40+ Mutual Fund Houses in India and they have over 2,500 Mutual Fund schemes. There is an increased interest and investments toward mutual funds from the retail customers in the recent past. The investment to a mutual fund scheme is done either as Lump sum or SIPs (invest …

Role of Personalization in Ecommerce Ram - Jul 11, 2022

Personalization can be a powerful lever to deliver on key business metrics – CTR % or Add to Cart or Conversion % for ecommerce company. And the personalization can be leveraged across touch points on the ecommerce app/website (calling it platform) and off the platform. On Ecommerce Platform • Showing …

CLTV Models for Acquisition Ram - Jul 09, 2022

It is clear that not all the customers contribute same value for an Ecommerce company. We have a seen, top 30% of the customer contribute over 120% of the profitability and bottom 20% customers add only cost to the organization. Based on historical data, we can identify the profiles of …

Steps for RFM Segmentation in Python Ram - Jul 03, 2022

For understanding customers' transactional behaviour, transactional segmentation is important and RFM is one of the fundamental approaches for segmenting customers based on their transactional behaviour. RFM stands for (R)ecency, (F)requency, and (M)onetary and it captures customer purchase behaviours related these dimensions. RFM Methodology is very simple and effective way of …

Actionable Approach for Acquiring High Value Customer for Ecommerce Ram - Jun 22, 2022

Customer acquisition is one of the costly activities and most important for them to grow. “Most sites spend between Rs 800-1,500 to acquire a customer”. And the expectation is that high % of these customers will repeat their first purchases. CLTV can help in identifying the customers who make high …

RFM based Transactional Segmentations for Ecommerce Ram - Jun 19, 2022

In this blog, we will discuss about relevance of behaviour segmentation for ecommerce and then develop a segmentation model using RFM (Recency, Frequency and Monetary value) based Approach. We will describe the detailed steps of the Recency, Frequency and Monetary based approach. Once these segments are created, we profile the …

CLTV definition and CLTV for Customer Acquisition Ram - Jun 12, 2022

In the previous blog, we discussed about the applications of Customer Lifetime Value (CLTV). Now, we will get into more details to define CLTV and the approaches for measuring and managing CLTV for customer acquisition. For Customer Lifetime Value Measurement, we need to define Who is a customer? What is …

Applications of CLTV for ECommerce Ram - Jun 12, 2022

One of the fundamental applications of AI is to increase value for Customers and Organizations. In this series, we will explore CLTV and its applications and also how AI can play a pivotal approach in measuring and managing value for customers and organisations. First, we will focus for ecommerce scenarios …

Insurance Agent Attendance System using Deep Learning Ram - Sep 25, 2021

Employed Insurance agents visit their prospective leads for educating on insurance policies and selling the right policies. For the banks and insurance agencies, it is important that their agents visit customers and build trust & long-term relationships. Tracking visits of the agents to their client locations is important for planning …

Data Scientist Interview Questions - Financial Services Ram - Jul 21, 2021

In this blog, we list a series of questions typically asked during interview process for Sr Data Scientist role in Financial Services.

Topic Modeling - Steps to Insights Ram - Jul 05, 2021

Topic MOdeling

Statistical Tests using Python Ram - Apr 04, 2021

In this blog, we will discuss T-Test (two-sample), ANOVA, Chi-Square, and Correlation Analysis in Python

Customer Attrition Model using Decision Tree Ram - Apr 03, 2021

It is costly to acquire a new customer, hence banks and other organizations work to keep their customer engaged (or reduce the customer attrition) Customer Attrition can be silent (when a customer stops using a product) and explicit when a customer closes a product. Typically, there is a journey or …

Bivariate Analysis: Contingency Analysis and Chi Square Ram - Apr 02, 2021

For finding an association between two nominal variables, a contingency analysis table, and chi-square test is employed. The contingency table is created by listing the values of one variable as a row and the other as a column. The Pearson chi-square test or "goodness-of-fit” is used to check if the …

Retail Analytics - eCommerce Data EDA and Simple Segmentation ram_admin - Feb 26, 2021

In this blog, we will learn about Exploratory Data Analysis (EDA) on eCommerce Data and also develop a simple segmentation. Based on order count, amount and recency are used to create the features and then created rule-based segmentations. In the subsequent blogs, we may use the same data for machine …

SQL Scenarios for Practice and Learning Ram - Jan 28, 2021

In the previous blog, we have covered MYSQL database design for retail scenario, create table structure and import CSV data into these tables. Also, we have covered 10 SQL scenario to learn the basics of SQL programming for Data Analytics. In continuation of that, we are adding 11 more scenarios …

Setting up MYSQL instance on GCP and Learning SQL Ram - Jan 28, 2021

In this blog, we will set up MYSQL instance on Google Cloud (GCP) and connect MySQL Workbench to GCP MYSQL Instance. In the part two, we will design a simple database schema for a retail scenario and create the list of tables. Also, we will load CSV files to the …

K Means Clustering Examples and Practical Applications Ram - Jan 07, 2021

In this blog, a list of real life scenarios of K Means Clustering Applications are discussed. There are list of real world examples and with a list of features to be used for K Means clustering.

Decision Tree: A statistical and analytical tool for effective decisions Ram - Dec 28, 2020

A decision tree is a hierarchical or tree-like representation of decisions. A Decision Tree is a technique to iteratively break input data (or node) into two or more data samples (or nodes). And this recursive partitioning of input data (or node) continues until it meets specified condition(s). A Decision Tree …

Cox Regression - Survival Modeling Ram - Dec 19, 2020

In this blog, the focus is on Cox Proportional Hazards (PH) model. The Cox Proportional hazard model is also referred to by the Cox Model, Cox Regression, or Proportional Hazard Model. Cox Model is used for the analysis of Survival data and finding out the relationship between Survival Time and …

CHAID Decision Tree: Reverse Mortgage Loan Termination Example Ram - Dec 16, 2020

Reverse Mortgage Loan (RML) enables Senior Citizens to avail of periodical payments from a lender against the mortgage of his/her house to supplement their income while remaining the owner and occupying the house. Interest on the payments availed will be accumulated. One of the types of A reverse mortgage is …

Customer Life Cycle and Customer Retention Management Ram - Dec 11, 2020

The Customer Life Cycle typically has 3 phases –Acquisition, Growth, and Retention. Customer Acquisition: Focus is targeting & reaching out to prospects, explaining to them about the products and services, and on-boarding the customers. Customer Development/Build/Growth: In this phase, organizations leverage the existing relationship for growing the engagement with newly …

Facial Emotion Recognition using Deep Learning Ram - Nov 25, 2020

Found facial expressions of emotion are not culturally determined, but universal across human cultures and thus biological in origin. The 6 basic human emotions are anger, disgust, fear, joy, sadness, and surprise. Reference Source. In this blog, we aim to build a deep learning-based human facial emotion classifier. For building …

Emotion Detection from Text Ram - Nov 23, 2020

Emotion is a complex state of feeling that influences physical and psychological changes. Emotions can be expressed verbally (through words, emojis, or speech - tone of voice) or by using nonverbal expressions such as facial expressions. There are different models to define the type of emotions. 6 basic type of …

Missing value treatment for Categorical and Numeric Variables using Python Ram - Nov 06, 2020

Missing values are common in real-life scenarios. All of the data science and analytics professionals need to understand strategies to manage missing values. In this blog, we will discuss missing value identification, treatment, and imputation. After reading the blog you will be able to answer these questions. How to identify …

Precision vs Recall in Binary Classification Ram - Nov 06, 2020

In Machine Learning Classification Scenario (especially Binary Classification) various model performance measures are used. In this, we are discussing Precision and Recall with relevant scenarios and examples. In which scenario Precision is more important than Recall and vice versa?

Optimization using Python: Linear Programming with example Ram - Nov 09, 2020

In the scenario, there are 64 different foods and their various nutrient contents and calories. Also, the price of each of these food items. We can want to formulate this as a diet optimization problem and find the optimal size of the food intake. A typical optimization scenario will have …

Movie Recommendation - Content-Based Filtering Ram - Oct 30, 2020

We spend a lot more time exploring various things on the web. Be it exploring products, interacting with friends & family over social media platform, watching movies on Netflix, or listening to music. And these platforms have huge options to deliver to their users. These platform aims to deliver personalized …

Recommendation Engine - Questions Ram - Oct 31, 2020

Recommendation Engine is one of the commonly used Machine Learning/AI approach. Some of the interesting questions related to Recommendation Engine.

Customer Life Time Value: Questions Ram - Oct 23, 2020

CLTV is an interesting concept and used across industries. We have created a list of questions that can help you validate your concepts and motivate you to think of various dimensions from an implementation perspective.

CLTV using RFM and Probability Models Ram - Oct 23, 2020

Customer Life Time Value is a measure of customer value to a firm. The customer value helps align acquisition costs with long-term value generation instead of revenue and/or volume and prioritize the focus toward retention on the highest potential CLV increase

Support Vector Machine: Overview Ram - Oct 13, 2020

Support Vector Machine (SVM) is one of the machine learning algorithms used for supervised problem sets mainly. Some of the other algorithms which can be explored along with SVM are Decision Tree, Random Forest, Neural Network, or Logistic Regression, specifically for Binary Classification problems.

Market Basket Analysis - Step by step approach Ram - Oct 12, 2020

The objective is to explain the steps involved in Market Basket Analysis (MBA) or Association Analysis. Also, explain the key terminologies used. We will leverage customer transaction data for developing association rules & insights which can be used for right product bundling and promotions, assortment planning and inventory management, and …

Test of Association for Categorical Variables Ram - Sep 15, 2020

Suppose we want to test if females are more likely to respond to a particular marketing campaign compared to males or in other words whether there is an association between gender and response variable. Since, both Gender and Response Variables are categorical, we have to use the Chi-square test which …

Deep Neural Network for Structured Data - Heart Attack Prediction From Scratch Ram - Sep 11, 2020

Preventive and predictive methods can help in managing the devastating effect of heart diseases. In this blog, we aim to show simple steps involved in building a predictive model using the Deep Neural Network method to predict a heart attack. This is a simple first deep neural network for structured …

Predict Heart Attack using Machine Learning Tutorial- EDA, RF and GBM Ram - Sep 10, 2020

"Heart disease is the leading cause of death for both men and women in the United States, accounting for about one million deaths each year." - source. Preventive and predictive methods can help in managing the devastating effect of heart diseases. In this blog, we aim to show simple steps …

Insurance Claim Fraud Modeling - Step by step Approach Ram - Sep 03, 2020

Insurance Fraud is done to get the monetary benefits that the policyholder is not entitled to on the insurance policy. Fraud can be committed by insurance company employees, brokers, intermediaries providing services to claimants (e.g. hospital, auto service garage, etc) and policyholders. It can be done at the time of …

Confusion Matrix and Cost Matrix Ram - Aug 31, 2020

One of the commonly used model performance assessment tools is a confusion matrix. It compares actual labels vs predicted labels and allows us to measure accuracy, the ratio of correct predictions to the total number of predictions, and a few other measures such as precision, recall etc. A cost matrix …

K Means Clustering Algorithm: Explained Ram - Aug 07, 2020

One of the most frequently used unsupervised algorithms is K Means. K Means Clustering is an exploratory data analysis technique. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in …

K Means Clustering Practical Applications Ram - Aug 04, 2020

Clustering or Unsupervised Segmentation is one of the commonly used techniques. In this blog, we want to share the scenarios where Clustering is used for decision making.

Correlation Analysis using R and Python Ram - Aug 04, 2020

In this blog, we show the steps involved in Correlation Analysis. We have added an excel with steps to calculate the correlation coefficient and p-value. Also, included correlation analysis - scatter plot, adding regression line, correlation coefficient and p-value using R and Python

Credit Card Approval Model using XGBoost Ram - Aug 01, 2020

Credit Card department in a bank is a leading data science adopter. Acquiring new credit card users is always a key priority for the bank. Giving credit cards without due diligence or assessment for creditworthiness is a huge risk. From the last many decades, the credit card department is using …

Tree Based Classifier using sklearn and Python Ram - Jul 20, 2020

Decision Tree and Adaboost are some of the famous classifiers in Machine Learning Methods. In this blog, we will show the steps involved in building the Machine Learning based Predictive Model for Binary Classification.

ANOVA - Analysis of Variance Explained with Example Ram - Jul 09, 2020

Analysis of Variance is applied to compare population means across 2 or more samples/groups using variance of the samples/groups. In common applications of ANOVA, the dependent variable is continuous and independent variable is categorical (Nominal or Ordinal) variable. The mean of dependent variables is compared across categorical variable values. Typically, …

Fraud Analytics in Insurance Ram - Jul 08, 2020

In this blog, we discuss Real applications of Machine Learning and Deep Learning methods. Fraud has been a significant cost drain for many organizations across industries and Insurance is no different. “Total cost of insurance fraud (excluding health insurance) is more than $40 billion per year in the US alone”

Refueling Customer Experience at Gas Stations and Convenience Stores Ram - Jul 07, 2020

The oil and Gas Industry is going through a challenging phase. Technology, Customer, and Climate Changes are expected to create major ripples for them. The owners of gas stations in the US have to rethink the business model and repurpose the gas station locations. In this blog, we share a …

Wonderful Word Cloud in Python Ram - Jun 19, 2021

Word Cloud is one of the visualization techniques to show relative importance of key words. Based on frequency of occurrences, it finds the importance for each word. From the large corpus of textual information, we want find the key words and topics, the word cloud can be really good starting …

Model Performance Statistics – Concordance: Calculation Steps Ram - Jun 19, 2020

In the blog, we will discuss one of the model performance statistics - Concordance. Concordance is commonly used model performance statistics in the industry.

Predictive Modeling: Framework & Approach Ram - Jun 19, 2020

In this blog, we aim to explain the broader steps involved in building predictive modeling using any of the machine learning methods. We will also illustrate key terminologies used within the predictive modeling project.

Multiple Linear Regression in Python: With an Example Ram - Jun 18, 2020

In this blog, we are building a Multiple Regression-based Machine Learning model for predicting house prices. We aim to cover two fundamental aspects – first showing steps involved in building a model and second explaining core concepts & statistics.

Gini Index Explained with Worked out Examples Ram - Jul 23, 2020

Decision Tree is one of the most commonly used and easy to understand Machine Learning Techniques. There are a few different algorithms used for building a decision tree and one such algorithm is CART - Classification and Regression Tree. CART Decision Tree algorithm uses Gini Index measures to select variables …

Concepts and Applications of Multiple regression Ram - Jun 18, 2020

In this blog, we aim to cover some of the fundamental questions data science aspirants ask. When do you use multiple regression techniques? What are a few real-life applications of the regression model? What is an overall approach to build the regression model? What is a methodology to estimate parameters? …

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