#Decision-tree-classifier
Showing 10 of 10 repositories tagged #decision-tree-classifier, ranked by stars
A collection of research papers on decision, classification and regression trees with implementations.
Network Intrusion Detection based on various machine learning and deep learning algorithms using UNSW-NB15 Dataset
Projects I completed as a part of Great Learning's PGP - Artificial Intelligence and Machine Learning
AI & Machine Learning: Detection and Classification of Network Traffic Anomalies based on IoT23 Dataset
Feature Selection using Metaheuristics Made Easy: Open Source MAFESE Library in Python
Collection of various implementations and Codes in Machine Learning, Deep Learning and Computer Vision ✨💥
Modular full-stack ML project leveraging Groq API, Streamlit, Supabase, JSON, SciPy, SciKit-Learn, Plotly & EmailJS, alongside libraries - NumPy, Pandas, Utils, OS, Base64, Re, Pillow & DateTime.
Predict and prevent customer churn in the telecom industry with our advanced analytics and Machine Learning project. Uncover key factors driving churn and gain valuable insights into customer behavior with interactive Power BI visualizations. Empower your decision-making process with data-driven strategies and improve customer retention.
:snake: Data Science Boot-Camp : UC San DiegoX
In the banking industry, detecting credit card fraud using machine learning is not just a trend; it is a necessity for banks, as they need to put proactive monitoring and fraud prevention mechanisms in place. Machine learning helps these institutions reduce time-consuming manual reviews, costly chargebacks and fees, and denial of legitimate transactions. Suppose you are part of the analytics team working on a fraud detection model and its cost-benefit analysis. You need to develop a machine learning model to detect fraudulent transactions based on the historical transactional data of customers with a pool of merchants.