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Ready to unlock the potential of your data? Let's collaborate and turn challenges into triumphs!

  • šŸ‘‹ Hi, Iā€™m Debaditya Samanta
  • šŸ‘€ Iā€™m interested in leveraging the power of data to unravel insights and solve complex challenges.
  • šŸŒ± Iā€™m currently learning cutting-edge techniques in machine learning and enhancing my skills in data analysis.
  • šŸ’žļø Iā€™m looking to collaborate on innovative data science projects that push boundaries and deliver tangible impact.
  • āš” Fun fact: I enjoy blending creativity with data ā€“ transforming raw numbers into compelling narratives!

Debaditya Samanta's Projects

halfdeb icon halfdeb

Config files for my GitHub profile.

income-prediction icon income-prediction

Explore the Income Prediction Project! This repository hosts a machine learning model designed to predict if an individual earns above or below a specific income threshold. Leveraging a dataset rich in demographic, education, and employment variables, the model seeks to shed light on income inequality.

text-classifier-using-self-implemented-naive-bayes- icon text-classifier-using-self-implemented-naive-bayes-

Explore the world of text classification with this project that showcases a text classifier built from the ground up using a self-implemented Naive Bayes algorithm. Leveraging the 20 Newsgroups dataset from scikit-learn, this project guides you through the process of data exploration, preprocessing, and model training.

titanic-survival-predictor-eda- icon titanic-survival-predictor-eda-

Welcome to the Titanic Machine Learning from Disaster project! This repository hosts a comprehensive solution for the Kaggle Titanic competition, where the challenge is to predict the survival or demise of passengers aboard the iconic RMS Titanic.

twitter-sentiment-analysis icon twitter-sentiment-analysis

Dive into the realm of sentiment analysis on Twitter with this project. Using the Twitter US Airline Sentiment Dataset, the project aims to predict tweet sentiments as positive, negative, or neutral. The journey involves exploring over 14,000 tweets, training a model, and making predictions.

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