Cases

In competitive markets, pricing is based on supply and demand economics. Sellers maximize profitability given certain market conditions. This case leverages the use of machine learning techniques to interpret the value of features such as brand, performance, age, and more to estimate the retail price of an automobile.

This case attempts to find solution to one of the most important questions in the ecommerce industry, whether the customer is casually browsing or actually purchasing? To answer this, it is required to identify the customers that are more likely to make a purchase or increase the chances of purchase by taking proactive actions. At the end of the day, this can help to target right customers, improve retention and increase profits.

Using the text data from the reviews given by the customers, predict the sentiment of the customers towards the retailer and classify the customers as positive/negative/neutral customers.

With the fluctuating demand influenced by holiday season, the manufacturing manager of a candy company needs to understand the seasonality to better predict the production outcomes.

The dataset contains daily avg revenue of telecom industry from each customer. We are using a dummy dataset which contains date and avg revenue per customer. The goal is to forecast the average revenue of the telecom industry per customer.

Customer churn, also known as customer retention, customer turnover, or customer defection, is the loss of clients or customers.

For a debt collections company identifying the right offering to each customer is a key component in an effective call planning strategy. Analyttica conducted a field experiment to fine tune this strategy.

To analyse a sample of machine data to build a predictive model and arrive at the predictive capability to classify the transformer as faulty or not faulty.

Using the customer data from online transactions, an e-commerce retailer leveraged the k-means algorithm for customer segmentation - using which the retailer improved their customer offerings and sales.

Dataset contains monthly counts, from 1971 to present, of initial claims for regular unemployment insurance benefits. In this case, we will tell you how to build ARIMA Forecast Model to forecast time series data of Unemployment insurance initial claims

Customer Sentiment Analysis in Insurance claims processing is a key step to identify the key attributes of customer satisfaction. This case study delves in to deducing customer sentiment for the claims process from their feedback.

A leading healthcare provider in the US wanted to understand the effect of several demographic, risk factors, laboratory and overall survival features on the survival of HCC patients and leveraged ML models to do so.

The case provides a step-by-step approach right from data cleaning and exploratory data analysis to compare different classification Machine Learning models to predict whether or not someone is making more or less than $50,000 a year using US census data. Fine tune the hyper-parameters to increase accuracy of the selected classification models and application of XAI to interpret model predictions at the candidate level, using LIME and SHAP.

Passionate about cricket & data? Snoop in to get a blend of both. Explore on how to apply some of the data science concepts on real-world cricket data. In this datacase we apply the linear programming optimization technique to create a fantasy cricket team.

This case provides a step-by-step approach right from data cleaning and exploratory data analysis to using hyperparameter optimisation techniques to compare different sets of hyperparameters of regression Machine Learning models to estimate price of diamonds using various attributes such as carat, color, clarity, cut etc

Identify risk class and eligibility of a customer in Insurance: Application of Machine Learning

Customers segmentation enables companies to identify the high-profit customers. Clustering algorithms are commonly used for customer segmentation. In this case we have leveraged K-means clustering to do customer segmentation.

Time series analysis and forecasting of pharmaceutical product sales of different products of a pharma manufacturer on a monthly level.

One of the largest retail companies in the world was looking to revamp its existing SCM operations. At the macro-level, the client wanted to improve the accuracy of its demand forecasting model which can consider several scenarios in the market.

The case provides a step-by-step approach to compare different text classification Machine Learning models which predicts the language of a Stackoverflow post based on its content. Four classification models are built, compared and application of XAI to interpret model predictions over text data, using LIME and SHAP.

How can a retailer, best layout the products for customer’s ease of access? How can the market baskets be discovered which are often bought together? These baskets can be used to design the layout and increase sales. Explore this applied case to know more.

What factors influence airline fares? There could be a lot of them. Travel agencies build predictive models for forecasting fares, though how does one choose the factors with most explaining power. Finish this case to experience how.

Can statistical testing tell us if elected MPs are fulfilling their duty? Such tests could help ensure that our elected MPs attend parliament proceedings. Using MP attendance data, find answers to some hypothetical scenarios in this case.

Creating customer segments to develop marketing strategy is often the first step of marketing. How can a customer’s recency of purchase, frequency of purchase and value of purchase be used to develop effective segments? Finish this case to know.

The case provides a step-by-step approach to the user to learn and understand the different stages and techniques used in data exploration, data treatment and feature selection, as part of a model building exercise, as part of a risk framework. The final data set is leveraged to be trained on different models to predict the Probability of Default.

The case provides a step-by-step approach to compare different classification Machine Learning models to calculate the Probability to Default (PD). Fine tune the hyper-parameters to increase accuracy of the selected classification model and application of XAI to interpret model predictions at a customer level, using LIME and SHAP.

Did you know that majority of the data driven solutions are backed upon by descriptive analysis. Here, in this case, we will help you understand how descriptive analytics is utilized for driving business decisions with the help of an example from banking domain.

Through this datacase, we shall explore the COVID-19 data and get a deeper understanding of the distribution of cases, the growth rate across countries and the behaviour in different states across India

Probabilistic information about the Future Electricity Prices is very valuable for energy producers as well as buyers. In this case, we will tell you how to build ARIMA Forecast Model to forecast time series data of prices.

Can reviews of a movie be summarized to gauge the public sentiment about it? Understanding the dominant sentiment among the masses would help you finding an answer. Sentiment analysis is the key here, explore the case to know how.

Medical diagnosis is a serious affair. More accurate detection of a life threatening disease like cancer goes a long way in saving lives. Explore this case to know more about how data analytics can play a vital role in improvement of diagnosis!

Did you know that majority of the data driven solutions are backed on descriptive analysis. Here, in this case, we will tell you how to use descriptive statistics for the same with the help of an example from banking domain.

Clustering is often used for deriving insights from various kinds of data. For text data, clustering could be successfully used to find out what are the dominant sentiments prevailing among customers. Explore this case to find out more!

Master one of the most widely used segmentation technique's, with the help of a solution for Insurance industry. This case will help you to understand, learn, apply and interpret the results of segmentation in the Insurance domain

Customer is an asset to any organisation and hence every organisation strives to retain its customers. Learn how Data Science plays a role in helping organisations take proactive steps to reduce the risk of customer attrition.

From smart-watches to fitness trackers to smart home devices, IOT pervades our life today. In this This case, focuses on how critical sectors like healthcare benefit by applying “Supervised Learning” to make sense of data collected from such devices.

Are you facing trouble in keeping track of all the launched campaigns, to attract customers, and monitoring them? This case focuses on how to build a campaign dashboard and evaluate KPI's of your campaigns, across product lines .

Have you ever wondered, how the retail giants forecast weekly sales? Why are some offers visible only to select customers? This case, focuses on the application of 'Linear Regression', to predict holiday sales at a customer level.

Do you receive calls from a bank for selling Credit Cards or Loans. Did you know that Analytics plays a large role in identifying who gets the call? Explore how Credit Card companies utilize analytics to identify their worthy potential customers.

In FMCG retail space the customers' consumption patterns tend to vary. Do you know how giant retail players manage to plan their inventory as per regional demand. Data Science is the key here! Want to know how? Explore in this Data case.

Become an expert hands-on problem solver and gain proficiency in applying Non-Hierarchical Clustering methods with the help of a real-world business case from HR domain. Clone the case easily and take a step forward to apply what you learned.

Do you know what are the key assumptions for applying a linear regression technique to any data? Are you aware of the different methods by which these assumptions can be validated? Explore this case to understand more!

Have you ever wondered how analytics can make a difference to the world of sports. Analytics has a wide ranging application to the field of sports. Explore this data case focused on application of descriptive statistics in Cricket.

Making good business decisions depends on assessing the complex interplay of multiple factors. This case highlights the application of ‘Decision Trees’, a Machine Learning technique to make business decisions in the banking domain.