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Hi šŸ‘‹, I'm Animesh Chakraborty

I have a strong interest in DATA SCIENCE and MACHINE LEARNING, and currently, I am working as a trainee in the field of Data Science. My interests include Python programming, Machine Learning and Statistics.

MasterHead

hiteshlko1

  • šŸŒ± Iā€™m currently Persuing Azure, GCP,Machine Learning , Deep Learning ,NLP ,Computer Vision

  • šŸ’¬ Ask me about Python, SQL,ML,DL,NLP

  • šŸ“« How to reach me [email protected]

  • šŸ“„ Know about my experiences

Connect with me:

Animesh Chakraborty

Languages and Tools:

Coding

azure docker flask gcp git kubernetes linux mssql mysql pandas postgresql python pytorch scikit_learn seaborn sqlite tensorflow

Animesh227

Ā Animesh227

Animesh227

animesh227's Projects

capstone-project-credit-card-default-prediction- icon capstone-project-credit-card-default-prediction-

Credit Card Default Prediction is a machine learning project that involves predicting the likelihood of a credit card user defaulting on their payments. The project aims to help credit card companies mitigate the risk of default by identifying potential default accounts in advance and taking preventive actions.

hotel-booking-analysis icon hotel-booking-analysis

Hotel booking analysis is the process of examining data related to hotel bookings to identify patterns, trends, and insights that can inform business decisions. This analysis may include factors such as booking frequency, booking channels, seasonal trends, customer demographics, and revenue generated.

netflix-movies-and-tv-shows-clustering icon netflix-movies-and-tv-shows-clustering

NETFLIX MOVIES AND TV SHOWS CLUSTERING is a project that aims to cluster the available movies and TV shows on Netflix based on their attributes such as genre, release year, and country of production.

seoul-bike-sharing-demand-prediction icon seoul-bike-sharing-demand-prediction

Seoul bike sharing demand prediction is a project that uses machine learning to predict the demand for rental bikes in Seoul, South Korea. The project aims to provide insights for the city's bike-sharing system to better manage the supply of bikes and ensure their availability to the public at the right time.

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