Machine-Learning-Algorithms-from-Scratch

Implementing machine learning algorithms from scratch.

Algorithms implemented so far:

Simple Linear Regression.
Dataset: Stock data from Quandl
Logistic Regression.
Dataset: Stanford ML course dataset
Naive Bayes Classifier.
Dataset: Email spam/non-span
Decision Trees
K Nearest Neighbours.
K Nearest Neighbours in Parallel.
Dataset: Chronic Kidney disease data from UCI
A-Star Algorithm
K Means Clustering.
K Means Clustering in Parallel.
Dataset: IPL player stats norm data
Support Vector Machine

Name		Name	Last commit message	Last commit date
Latest commit History 108 Commits
data		data
.gitattributes		.gitattributes
.gitignore		.gitignore
Collaborative Filtering.py		Collaborative Filtering.py
DBSCAN.py		DBSCAN.py
Decision Trees.py		Decision Trees.py
FP Growth.py		FP Growth.py
Genetic Algorithm.py		Genetic Algorithm.py
K Means Clustering.py		K Means Clustering.py
K Nearest Neighbours - In Parallel.py		K Nearest Neighbours - In Parallel.py
K Nearest Neighbours.py		K Nearest Neighbours.py
Linear Regression.py		Linear Regression.py
Logistic Regression.py		Logistic Regression.py
Mean Shift.py		Mean Shift.py
Naive Bayes.py		Naive Bayes.py
README.md		README.md
Support Vector Machine.py		Support Vector Machine.py
a-star.py		a-star.py
random forest classifier.py		random forest classifier.py