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
Bank card fraud detection using machine learning. Web application using Streamlit framework
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)
Classification of mushrooms using decision tree in ID3 implementation
Sklearn, logistic regression, Naive Bayes classifier, K-Nearest Neighbors, decision trees
My implementations of Linear Regression, Logistic Regression
Implemented Traditional ML models for Regression and Classification using sklearn.
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.
In This Repository you can find The Explanation and The Implementation of the Most Famous Machine Learning Algorithms
Complete Tutorial Guide with Code for learning ML
A new method of supervised feature scaling using decision tree
Machine learning algorithms from scratch in python.
Implementation of decision trees for classification and regression as objects similar to sklearn's.
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