Shubha23

amazon-sagemaker-examples

Example notebooks that show how to apply machine learning, deep learning and reinforcement learning in Amazon SageMaker

Automation-basic-applications-Py-scripts

Convergence-prediction-based-on-user-behavior

Prediction of user convergence rate based on past user behaviours

Expert-system-for-cancer-patient-survival-prediction

Rule-based expert system for prediction of a patient's survival within 5 years of undergoing breast cancer surgery & its comparison with ML classification techniques.

Exploratory-Data-Analysis-Customer-Churn-Prediction

Application of K-means clustering. Prediction of customer churn using Multi-layer Perceptron ANN, Logistic Regression, SVM-RBF and Random Forest Classifier.

Fake-News-Detection-Text-Preprocessing-and-Classification

Fake new detection using text classification as real or fake news segments. Required installations - Python 3.8, NLTK, Scikit-Learn, Jupyter. Text cleaning, tokenization, vectorization, classification model generation and evaluation.

Insurance-Forecast-A-Regression-Problem

Application of Linear, AdaBoost, RandomForest Regressors and Ordinary Least Square method for projecting customer insurance charges.

Laplace-and-Exponential-mechanisms-for-privacy

Applying Laplace and exponential mechanisms to add random noise to data for differential privacy. Plotting MSE vs. epsilon.

Text-processing-NLP

This notebook contains entire text preprocessing pipeline for NLP problems. The ready-to-use functions require NLTK and SKlearn package installations. It also contains some prominent text classification models.

Shubha23/Laplace-and-Exponential-mechanisms-for-privacy

Applying Laplace and exponential mechanisms to add random noise to data for differential privacy. Plotting MSE vs. epsilon.

Shubha23/Text-processing-NLP

This notebook contains entire text preprocessing pipeline for NLP problems. The ready-to-use functions require NLTK and SKlearn package installations. It also contains some prominent text classification models.

Shubha23/Exploratory-Data-Analysis-Customer-Churn-Prediction

Application of K-means clustering. Prediction of customer churn using Multi-layer Perceptron ANN, Logistic Regression, SVM-RBF and Random Forest Classifier.

Shubha23/Expert-system-for-cancer-patient-survival-prediction

Rule-based expert system for prediction of a patient's survival within 5 years of undergoing breast cancer surgery & its comparison with ML classification techniques.

Shubha23/Fake-News-Detection-Text-Preprocessing-and-Classification

Fake new detection using text classification as real or fake news segments. Required installations - Python 3.8, NLTK, Scikit-Learn, Jupyter. Text cleaning, tokenization, vectorization, classification model generation and evaluation.

Shubha23/amazon-sagemaker-examples

Example notebooks that show how to apply machine learning, deep learning and reinforcement learning in Amazon SageMaker

Shubha23/Automation-basic-applications-Py-scripts

Shubha23/Convergence-prediction-based-on-user-behavior

Prediction of user convergence rate based on past user behaviours

Shubha23/Insurance-Forecast-A-Regression-Problem

Application of Linear, AdaBoost, RandomForest Regressors and Ordinary Least Square method for projecting customer insurance charges.

Shubha23/hummingbird

Hummingbird compiles trained ML models into tensor computation for faster inference.

Shubha23/Identification-of-Phishing-Websites

Application of Machine learning & Feature Selection techniques for Classification of Phishing Websites

Shubha23/Papers-Literature-ML-DL-RL-AI

Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning

Shubha23/Yelp-dataset-sample-codes

Samples for users of the Yelp Academic Dataset