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model-accuracy

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Splitting the advertising data (advertising.csv) into training and testing data sets, then choosing and training a classification machine learning algorithm; Getting the accuracy of the ML model; Using feature engineering skills to create new features and improve my ML model;

  • Updated Oct 24, 2023
  • Jupyter Notebook

🔥 Sentiment Classifier App: Instantly predict whether a review is positive or negative using machine learning! 🧠💬 Built with Logistic Regression, trained on 84K+ reviews, with 91.22% accuracy! 🚀

  • Updated Oct 5, 2024
  • Python

🔥 Sentiment Classifier App: Instantly predict whether a review is positive or negative using machine learning! 🧠💬 Built with Logistic Regression, trained on 84K+ reviews, with 91.51% accuracy! 🚀

  • Updated Oct 5, 2024
  • Python

This project is a sentiment analysis model built to classify IMDB movie reviews as either positive or negative using the **IMDB dataset**. It uses various machine learning models and deep learning techniques to handle the text data.

  • Updated Nov 11, 2024
  • Jupyter Notebook

Designing strategies to pull back potential churn customers of a telecom operator by building a model which can generalize well and can explain the variance in the behavior of different churn customer. Analysis being done on large dataset which has lot of scope for cleaning and choosing the right model for prediction.

  • Updated Apr 2, 2023
  • Jupyter Notebook

This repos contains my all the basic to some advance Python Projects which i have created for the beginners so that they can learn the whole development processs by just implementing the projects

  • Updated Oct 19, 2024
  • Jupyter Notebook

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