YOLOv8 image classifier model comparison
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Updated
Sep 4, 2023 - Jupyter Notebook
YOLOv8 image classifier model comparison
Create the Decision Tree classifier and visualize it graphically. The purpose is if we feed any new data to this classifier, it would be able to predict the right class accordingly.
Repository to store materials for our group project on predicting health insurance charges.
This project involves predicting customer churn in a telecommunications company using machine learning techniques, exploring various features' impact, optimizing models, and identifying key factors influencing churn.
These projects illustrate key machine learning concepts, model development, regression, predictions, model assessment, iterations, and parametrization in the context of operational forecasting
Tensorflow image classifier Keras Applications model comparison
A machine learning foundation project to predict defaulting credit card clients. Topics covered are EDA, feature engineering and selection, model evaluation.
This repository explores machine learning models applied to predict online shoppers' purchase intentions based on a comprehensive dataset, showcasing various classification algorithms and their performance in the e-commerce domain.
This is an end to end machine learning project using my personal shopping data collected over the past three years.
Predicting customer subscriptions for a bank's term deposit post marketing campaigns
Graduate Rotational Internship Program -TSF-The Spark Foundation(Data Science and Business Analysis Internship) #GRIPJULY21-Task#2:Prediction Using Unsupervised Machine Learning-In this task, we have to predict the optimum number of clusters from the iris dataset & represent it visually.
Using publicly available data for the national factors that impact supply and demand of homes in US, build a data science model to study the effect of these variables on home prices.
This project explores the predictive modeling workflow using the Kaggle competition "Titanic - Machine Learning from Disaster." It emphasizes key stages like data analysis and model evaluation, aiming to identify the optimal model. Through a real-world approach, we enhance our understanding of the workflow and emphasize rigorous model evaluation.
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