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In this project, our goal is to identify the likelihood that a customer will quit the company, the key churn indicators, and potential retention strategies. Churn is one of the major problems affecting the telecom industry.
The project aims to analyze and forecast the number of products sold per stores per weeks for a neighborhood grocery store, the goal is to develop a model that can accurately anticipate future sales using data from 54 different stores and 33 different products collected from the same country.
Our goal is to build models use those models to make observations, and use them to guide future strategic decision-making. We would like to assist management (business managers) at the grocery shop in gathering some insights from their data in order to improve operations and eventually revenue.
A natural language processing technology called fake news detection is used to find and categorize erroneous or misleading material in news articles and social media posts. The prevalence of fake news has heightened the demand for automated systems that can scan information for possible deception and flag it. In this project, I looked into how to use Hugging Face to polish a pre-trained fake news detection model and distribute it on the Hugging Face model hub.
The major goal of this project is to develop a classification model that can accurately differentiate between sepsis and non-sepsis cases by using a dataset that contains essential information, such as blood test results, blood pressure, BMI, and patient age. A Docker container will be used to deploy the created model as a FastAPI-based API.
This project involves time series forecasting. Using information from Corporation Favorita, a big grocery chain with headquarters in Ecuador, we will forecast store sales. Six csv files (train, test, store, oil, holidays_events, transactions) are sent to us; they contain information about goods, stores, specials, sales, oil prices, holidays and more.
This project aims to inform significant players who are thinking about joining the Indian startup ecosystem. To do this, we will look at significant indicators of startup investment in India from 2018 to 2021. Utilizing this knowledge, management will make smart business decisions.
I fine-tuned pre-trained Deep Learning models from HuggingFace on a fresh dataset for this project in order to tweak the models to predict the feelings expressed in a Tweet (for example, neutral, positive, or negative).