To practice machine learning algorithms.
The following algorithms have been implemented by me.
- Simple Linear Regression.
- Multi-variate Linear Regression.
- L1 Regularisation Regression.
- L2 Regularisation Regression.
- Gradient Descent for linear and logistic regression.
- Logistic Regression
- Naive Bayes Algorithm
- K-Nearest Neighbors Algorithm.
- Decision Trees
- Perceptrons
- Git clone this repository or download as zip or download selected files.
- Note : you should have jupyter notebook installed for this or you can also use google colab.
Google Colab - https://colab.research.google.com/
Jupyter Notebook Download - https://www.anaconda.com/products/individual#:~:text=Anaconda%20Navigator%20is%20a%20desktop,without%20using%20command%2Dline%20commands. - Python ^3.6 is expected to be installed and the corresponding valid versions for numpy, scikit-learn and matplotlib libraries needs to be installed.
The data is easily available on kaggle and UCI repository. For reference:
- Moore's Law Dataset -- Already present in the repository (Moore's.txt)
- Predicting Systole BP -- Present in the repository (mlr02.xls)
- Ecommerce Dataset -- ecommerce_data.xlsx present in the folder where Logistic regression folder.
- train.csv --- shorturl.at/arKY0
- mnist dataset --- https://www.kaggle.com/oddrationale/mnist-in-csv