Random Forest Algorithm written in Python using NumPy and Pandas. Based on the Decision Tree Algorithm.
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Updated
Feb 13, 2021 - Python
Random Forest Algorithm written in Python using NumPy and Pandas. Based on the Decision Tree Algorithm.
Implementation of decision tree classifier from scratch.
A decision tree is a predictive model useful for different purposes and often used as a tool for decision support.
Train a "model function" with the "decision tree algorithm" to farther use in test in online app like browser extensions
Projects based on Machine Leaning
Implementation of various machine learning algorithms from scratch.
Implementation of various machine learning algorithms
SPSS to build decision tree, KNN and classification models
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.
Recommendation System support Farmer to prevent The Blast Disease ( Type of Rice Disease )
Implementation of some decision tree algorithms in Python.
Decision tree algorithm falls under the category of supervised learning. They can be used to solve both regression and classification problems
Various Machine Learning algorithms implemented from scratch
Decision Trees (ID3)
Iris flower dataset classification using Decision tree and KNN Algorithms.
Problem to solve: Predict if a candidate would be hired based on specific characteristics; what are the most important features a candidate must have to have higher possibilities of getting the job?
This checks out data provided by the Kepler space telescope to study exoplanets.
Machinelearning_algorithms_scratch
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