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A well-known bank has been observing a lot of customers closing their accounts or switching to competitor banks over the past couple of quarters. This has caused a huge dent in their quarterly revenues and might drastically affect annual revenues for the ongoing financial year, causing stocks to plunge and market cap to reduce significantly. The idea is to be able to predict which customers are going to churn so that necessary actions/interventions can be taken by the bank to retain such customers.
In this machine learning churn prediction project, we are provided with customer data pertaining to his past transactions with the bank and some demographic information. We use this to establish relations/associations between data features and customer's propensity to churn and build a classification model to predict whether the customer will leave the bank or not. We also go about explaining model predictions through multiple visualizations and give insight into which factor(s) are responsible for the churn of the customers.
This project walks you through a complete end-to-end cycle of a data science project in the banking industry, right from the deliberations during formation of the problem statement to making the model deployment-ready.
Use the RACE dataset to extract a dominant topic from each document and perform LDA topic modeling in python.
In this Databricks Azure tutorial project, you will use Spark Sql to analyse the movielens dataset to provide movie recommendations. As part of this you will deploy Azure data factory, data pipelines and visualise the analysis.
In this machine learning resume parser example we use the popular Spacy NLP python library for OCR and text classification.
In this time series project, you will forecast Walmart sales over time using the powerful, fast, and flexible time series forecasting library Greykite that helps automate time series problems.
Use the Zillow dataset to follow a test-driven approach and build a regression machine learning model to predict the price of the house based on other variables.
Given big data at taxi service (ride-hailing) i.e. OLA, you will learn multi-step time series forecasting and clustering with Mini-Batch K-means Algorithm on geospatial data to predict future ride requests for a particular region at a given time.
In this NLP AI application, we build the core conversational engine for a chatbot. We use the popular NLTK text classification library to achieve this.
Estimating churners before they discontinue using a product or service is extremely important. In this ML project, you will develop a churn prediction model in telecom to predict customers who are most likely subject to churn.
Deep Learning Project to implement an Abstractive Text Summarizer using Google's Transformers-BART Model to generate news article headlines.
In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.