krunal-nagda Goto Github PK
Name: Krunal Nagda
Type: User
Bio: Actuarial Science Professional and a Certified Data Science Professional from IIIT-Bangalore
Location: Mumbai, IN
Name: Krunal Nagda
Type: User
Bio: Actuarial Science Professional and a Certified Data Science Professional from IIIT-Bangalore
Location: Mumbai, IN
For the past few months, Airbnb has seen a major decline in revenue. Now that the restrictions have started lifting and people have started to travel more, Airbnb wants to make sure that it is fully prepared for this change. The different leaders at Airbnb want to understand some important insights based on various attributes in the dataset so as to increase the revenue. Our responsibility is to provide valuable insights to aid in decision making.
Modeling the demand for shared bikes with the available independent variables in the given dataset 'day'. It will be used by the management to understand how exactly the demands vary with different features. They can accordingly manipulate the business strategy to meet the demand levels and meet the customer's expectations. Further, the model will be a good way for management to understand the demand dynamics of a new market.
Course Files for Complete Python 3 Bootcamp Course on Udemy
In the banking industry, detecting credit card fraud using machine learning is not just a trend; it is a necessity for banks, as they need to put proactive monitoring and fraud prevention mechanisms in place. Machine learning helps these institutions reduce time-consuming manual reviews, costly chargebacks and fees, and denial of legitimate transactions. Suppose you are part of the analytics team working on a fraud detection model and its cost-benefit analysis. You need to develop a machine learning model to detect fraudulent transactions based on the historical transactional data of customers with a pool of merchants.
This case study aims to identify patterns which indicate if a client has difficulty paying their instalments which may be used for taking actions such as denying the loan, reducing the amount of loan, lending (to risky applicants) at a higher interest rate, etc. This will ensure that the consumers capable of repaying the loan are not rejected. Identification of such applicants using EDA is the aim of this case study.
Using K-means and Hierarchical Clustering to categorise the countries using some socio-economic and health factors that determine the overall development of the country. Then suggesting the countries which the CEO needs to focus on the most.
X Education has appointed to help them select the most promising leads, i.e. the leads that are most likely to convert into paying customers. The company requires to build a model wherein you need to assign a lead score to each of the leads such that the customers with higher lead score have a higher conversion chance and the customers with lower lead score have a lower conversion chance. The CEO, in particular, has given a ballpark of the target lead conversion rate to be around 80%.
Transcribing a video and performing Sentiment Analysis
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