Email Spam detection using KNN and Decision Trees Models Trained using the Spambase database

An implementation of Random Forests in the Python programming language (accuracy tested with 100, 300, and 500 trees)

Final project for IEE 520 Stat learning for data mining. Highly imbalanced data set. Sampling methods used.

The primary objective of this study is to explore the feasibility of using machine learning algorithms to classify health insurance plans based on their coverage for routine dental services. To achieve this, I used six different classification algorithms: LR, DT, RF, GBT, SVM, FM(Tech: PySpark, SQL, Databricks, Zeppelin books, Hadoop, Spark-Submit)

In This Repository you can find The Explanation and The Implementation of the Most Famous Machine Learning Algorithms

Sklearn, logistic regression, Naive Bayes classifier, K-Nearest Neighbors, decision trees

Implemented Traditional ML models for Regression and Classification using sklearn.

Implementation of decision tree

Complete Tutorial Guide with Code for learning ML

A new method of supervised feature scaling using decision tree

Implementation of decision trees for classification and regression as objects similar to sklearn's.

Classification of mushrooms using decision tree in ID3 implementation

Machine learning algorithms from scratch in python.

My implementations of Linear Regression, Logistic Regression

Bank card fraud detection using machine learning. Web application using Streamlit framework

Repository For Codes And Concept Taught in Udemy Course