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ML project focused on predicting Titanic passenger survival using various algorithms and extensive data analysis techniques. This project includes detailed data visualization and interpretation to uncover key factors affecting survival. By leveraging various ML models the analysis aims to achieve high predictive accuracy.
Predict and prevent customer churn in the telecom industry with this project. Harness the power of advanced analytics and Machine Learning on a diverse dataset to develop a robust classification model. Gain deep insights into customer behavior and identify critical factors influencing churn using interactive Power BI visualizations.
Performed model evaluation using evaluation metrics such as accuracy, precision, recall, F1-score etc. Then model interpretation using feature importance, SHAP and LIME. Finally , evaluated model robustness and stability through techniques like bootstrapping or Monte Carlo simulations.
The folliwing ML project involves EDA analysis of Election Dataset, Data preparation for modelling, and prediction using ML models. Also Text Analysis on the inaugral corpora from nltk to analyse the most frequently used words in Presidents' Speeches.
a robust method of classification and recognition of coffee leaf diseases using both classical ma learning and deep learning methods, also a custom CNN. These methods were evaluated on the Arabica coffee leaf dataset known as JMuBEN.
Machine learning course project on computer science master degree. Prediction of diabetes based on many features related to health habits and previous medical events. EDA phase, followed by 3 ML supervised models (naive Bayes, Decision Tree and Neural Network)
Develop a heart disease prediction system that can assist medical professionals in predicting heart disease status based on the clinical data of patients.
The project involves using machine learning techniques, like RandomForestClassifier and MLP, to predict whether a song will be popular or not based on its acoustic features. The input consists of various acoustic and metadata features, while the output is a binary classification.