Sambuddha Goswami

Proactive-Attrition-Management-Telecom-Churning_Logistic-Regression_SAS

February 25, 2019 – February 25, 2019

Customer Churn Analysis (Telecom domain): Developed a model for predicting customer churn at a telecom company with 71047 customer records and 75 potential predictors in the database, and used the insights from the model to develop an incentive plan for enticing would-be customers to remain with the company, using logistic regression analysis.

Customer-Segmentation-for-Credit-Card-Users_SAS

February 25, 2019 – February 25, 2019

Customer Segmentation for Credit Card Users (Banking domain): Defined a marketing strategy by developing a customer segmentation profile using K-means cluster and factor analysis. Used the derived KPI's to gain insights on the behavioral segments of credit card customers. The sample dataset summarizes the usage behavior of about 9000 active credit card holders during the last 6 months. The file is at a customer level with 18 behavioral variables.

Bank-Transaction_Credit-Card-Spend-Drivers_Linear-Regression_SAS

February 25, 2019 – February 25, 2019

KPI’s for Credit Card Spends (Banking domain): Analyzed and understood the driving factors for total spend of both primary and secondary credit cards by customers using multivariate linear regression analysis. There were 71047 customer records in the database and 75 potential predictors.

Data-analysis-on-Airline-dataset_NYC-Flight

February 25, 2019 – February 25, 2019

This dataset contains information about all flights that departed from NYC (e.g. EWR, JFK and LGA) in 2013: 336,776 flights in total. Analyzed the data to get the information on departure delay, arrival delay, busiest routes, busiest time of the day, understanding weather conditions related with delays, understanding relationship with years of operation and fuel consumption cost and variation of delays over the course of the day

Retail-Chain_Exploratory-Data-Analysis

February 25, 2019 – February 25, 2019

Product Strategy, Pricing Policies and Maximizing Revenues (Retail domain): Defined product strategy and pricing policies using linear regression to maximize projected revenues for a leading retail chain having more than 500 stores, which sells laptops and accessories.

UK-Transport_Time-Series-Forecasting

February 25, 2019 – February 25, 2019

Forecasting (Transport domain): Using time series forecasting, predicted the total number of passenger movement from UK for the next four quarters.

Proactive-Attrition-Management-Telecom-Churning_Logistic-Regression_R

February 25, 2019 – February 25, 2019

Customer Churn Analysis (Telecom domain): Developed a model for predicting customer churn at a telecom company with 71047 customer records and 75 potential predictors in the database, and used the insights from the model to develop an incentive plan for enticing would-be customers to remain with the company, using logistic regression analysis.

Bank-Transaction_Credit-Card-Spend-Drivers_Linear-Regression_R

February 25, 2019 – February 25, 2019

KPI’s for Credit Card Spends (Banking domain): Analyzed and understood the driving factors for total spend of both primary and secondary credit cards by customers using multivariate linear regression analysis. There were 71047 customer records in the database and 75 potential predictors.

Customer-Segmentation-for-Credit-Card-Users_R

February 25, 2019 – February 25, 2019

Customer Segmentation for Credit Card Users (Banking domain): Defined a marketing strategy by developing a customer segmentation profile using K-means cluster and factor analysis. Used the derived KPI's to gain insights on the behavioral segments of credit card customers. The sample dataset summarizes the usage behavior of about 9000 active credit card holders during the last 6 months. The file is at a customer level with 18 behavioral variables.