A software engineer and data analyst skilled in Artificial Intelligence,Machine Learning, Android App Development, IOS app development, Python, Javascript frameworks, Responsive web design
@ayshwaryaJ
Ayshwaryajagadeesan
and ayshwarya-jagadeesan-2880a0b.
Image classification using CNNs in KerasData Description:The dataset consists of images of plant seedlings at various stages of growth. Each image has a filename that is its unique id. The dataset comprises 12 plant species. The goal of the project is to create a classifier capable of determining a plant's species from a photo.
Twitter posses 330 million monthly active users, which allows businesses to reach a broad population and connect with customers without intermediaries. On the other side, there’s so much information that it’s difficult for brands to quickly detect negative social mentions that could harm their business. That's why sentiment analysis/classification, which involves monitoring emotions in conversations on social media platforms, has become a key strategy in social media marketing.
Given a Bank customer, build a neural network-based classifier that can determine whether they will leave or not in the next 6 months.Businesses like banks that provide service have to worry about the problem of 'Churn' i.e. customers leaving and joining another service provider. It is important to understand which aspects of the service influence a customer's decision in this regard. Management can concentrate efforts on the improvement of service, keeping in mind these priorities.
All Life Bank wants to focus on its credit card customer base in the next financial year. They have been advised by their marketing research team, that the penetration in the market can be improved. Based on this input, the Marketing team proposes to run personalised campaigns to target new customers as well as upsell to existing customers. Another insight from the market research was that the customers perceive the support services of the back poorly. Based on this, the Operations team wants to upgrade the service delivery model, to ensure that customers queries are resolved faster. Head of Marketing and Head of Delivery both decide to reach out to the Data Science team for help.
To predict the concrete strength using the data available in file "concrete.csv". Apply feature engineering and model tuning to obtain a score above 85%.
We have data from a Portuguese bank on details of customers related to selling a term deposit. The objective of the project is to help the marketing team identify potential customers who are relatively more likely to subscribe to the term deposit and this increase the hit ratio.
This case is about a bank (Thera Bank) whose management wants to explore ways of converting its liability customers to personal loan customers (while retaining them as depositors). A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over 9% success. This has encouraged the retail marketing department to devise campaigns with better target marketing to increase the success ratio with a minimal budget.
The GroupLens Research Project is a research group in the Department of Computer Science and Engineering at the University of Minnesota. The data is widely used for collaborative filtering and other filtering solutions. However, we will be using this data to act as a means to demonstrate our skill in using Python to “play” with data.