Repository For Codes And Concept Taught in Udemy Course
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Updated
Jul 2, 2021 - Python
Repository For Codes And Concept Taught in Udemy Course
Golang Dynamic Decision Tree
Bank card fraud detection using machine learning. Web application using Streamlit framework
This repository consists of machine Learning models, deep learning models and some NLP tasks such as Topic Modelling, Sequence generation, Sentiment analysis, Recommendation System
Classification of mushrooms using decision tree in ID3 implementation
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Random Forest Classification
Keywords—classification, supervised learning, Random Forest, K-Nearest Neighbor(K-NN), Naïve Bayes, Decision tree, Support Vector machine
Machine learning algorithms from scratch in python.
My implementations of Linear Regression, Logistic Regression
Machine Learning Projects Portfolio
URL Based Spam Classification Using Machine Learning
This project evaluates logistic regression, random forest, decision tree, and gradient boosting classifier models for fake news detection. Using labeled data, it analyzes accuracy, confusion matrices, and ROC curves to understand each model's effectiveness in discerning between real and fake news.
Using Machine learning algorithms on the famous Titanic Disaster Dataset for Predicting the survival of the passenger.
Create a decision tree, plot it, convert the rules into IF-THEN format, and utilize cost-complexity pruning for minimal tree and interpretable rules.
This repository consists of ML projects including data modeling, clustering, classification, and dimensionality reduction models
Decision Tree implementation without any library on Iris dataset
Email Spam detection using KNN and Decision Trees Models Trained using the Spambase database
This project emphasizes on predicting churn of an employee within an organization such that whether the employee will leave the company or continue with it. It uses the data of previous employees who have worked for the company and by finding a pattern it predicts the churn in the form of yes or no.
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