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Dimensionality_Reduction
Dimensionality_Reduction
 
 
Discrete_Cosine_Transform_(DCT)
Discrete_Cosine_Transform_(DCT)
 
 
K-Means_Implementation
K-Means_Implementation
 
 
MSCRED
MSCRED
 
 
data
data
 
 
A_Star.py
A_Star.py
 
 
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.py
K_Means.py
 
 
K_Nearest_Neighbours.py
K_Nearest_Neighbours.py
 
 
K_Nearest_Neighbours_In_Parallel.py
K_Nearest_Neighbours_In_Parallel.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
 
 
Random_Forest_Classifier.py
Random_Forest_Classifier.py
 
 
Support_Vector_Machine.py
Support_Vector_Machine.py
 
 

README.md

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Clustering Algorithms with Python

Implementing machine learning algorithms from scratch.

Algorithms implemented so far:

  1. Simple Linear Regression.
    Dataset: Stock data from Quandl

  2. Logistic Regression.
    Dataset: Stanford ML course dataset

  3. Naive Bayes Classifier.
    Dataset: Email spam/non-span

  4. Decision Trees

  5. K Nearest Neighbours.
    K Nearest Neighbours in Parallel.
    Dataset: Chronic Kidney disease data from UCI

  6. A-Star Algorithm

  7. K Means Clustering.
    K Means Clustering in Parallel.
    Dataset: IPL player stats norm data

  8. Support Vector Machine