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.

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

🔥 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! 🚀

🔥 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! 🚀

Posture classification system that uses an IMU sensor.

Android app for vehicle tracking & prediction with Jetpack Compose. Features data collection, visualization, & ML integration for training & predictions.

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;

Built various machine learning models for banks to develop effective credit rating

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.

The project tries to develop & compare 3 different Machine Learning methods that could better predict in employee attrition.

Mask and Type of Mask Detection

This project creates a binary classifier that is capable of predicting whether applicants will be successful if funded by the Alphabet Soup Charity Program

Solve complex real-life problems with the simplicity of Keras