https://github.com/nachiket273/ml_algo_implemented
Bare-bone and simple implementations of few Machine Learning Algorithms
https://github.com/nachiket273/ml_algo_implemented
dbscan decision-trees gradient-boosting gradient-descent kmeans knn linear-regression logistic-regression machine-learning machine-learning-from-scratch naive-bayes pca python random-forest svm tsne
Last synced: 7 months ago
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Bare-bone and simple implementations of few Machine Learning Algorithms
- Host: GitHub
- URL: https://github.com/nachiket273/ml_algo_implemented
- Owner: nachiket273
- License: mit
- Created: 2019-11-02T09:15:19.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2020-05-22T16:33:51.000Z (over 5 years ago)
- Last Synced: 2025-03-22T01:24:53.330Z (8 months ago)
- Topics: dbscan, decision-trees, gradient-boosting, gradient-descent, kmeans, knn, linear-regression, logistic-regression, machine-learning, machine-learning-from-scratch, naive-bayes, pca, python, random-forest, svm, tsne
- Language: Jupyter Notebook
- Homepage:
- Size: 2.5 MB
- Stars: 5
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ML_Algo_Implemented
Simple Implementations of some Machine Learning Algorithms.
Along with implementation, it contains simple notebook that compares results of these implemenations
with standard sklearn library implementations.
Note that these implementations are very simple and do not consider aspects like space complexity, time complexity,
scalability, correct error messaging etc.
Most of the implementations use NumPy ( scarcely use Scipy ) and mostly do not have any other requirement.
# Pre-Requisite
1) [NumPy](https://numpy.org/) ( pip install numpy)
2) [SciPy](https://www.scipy.org/) ( pip install scipy)
3) [Sklearn](https://scikit-learn.org/stable/) ( used for comparison and metrics like accuracy_score etc) ( pip install -U scikit-learn)
4) [seaborn](https://seaborn.pydata.org/) ( for plotting, similar plotting can be done using matplotlib too)
5) [cvxopt](https://cvxopt.org/) ( for quadratic optimization for SVM )
# Implementations
----------------------------------------------------------------------------------------
1) [Decision Tree](./DecisionTree)
2) [k-Nearest Neighbors](./KNN)
3) [Random Forest](./RandomForest)
4) [Gradient Boosting](./GradientBoosting)
5) [Linear Regression](./Linear_Regression)
6) [Logistic Regression (with Gradient Descent)](./Logistic_Regression)
7) [K-means](./KMeans)
8) [DBSCAN](./DBSCAN)
9) [SVM](./SVM)
10) [Naive_Bayes](./Naive_Bayes)
11) [PCA](./PCA)
12) [tSNE](./tSNE)
# Contact
Feel free to [email](mailto:nachiket.tanksale@gmail.com) or contact me on [LinkedIn](https://www.linkedin.com/in/nachikettanksale/)