Ramgopal Prajapat:

Learnings and Views

Blogs
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 - Jul 05, 2020
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? …