nishanthbhat07 / machinelearning_python

This repository is about machine learning algorithms

Machine Learning Algorithms

Aim

To practice machine learning algorithms.

Algorithms

The following algorithms have been implemented by me.

1. Simple Linear Regression.
2. Multi-variate Linear Regression.
3. L1 Regularisation Regression.
4. L2 Regularisation Regression.
5. Gradient Descent for linear and logistic regression.
6. Logistic Regression
7. Naive Bayes Algorithm
8. K-Nearest Neighbors Algorithm.
9. Decision Trees
10. Perceptrons

Installation

1. Git clone this repository or download as zip or download selected files.
2. 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.
3. Python ^3.6 is expected to be installed and the corresponding valid versions for numpy, scikit-learn and matplotlib libraries needs to be installed.

Data

The data is easily available on kaggle and UCI repository. For reference:

Linear Regression

1. Moore's Law Dataset -- Already present in the repository (Moore's.txt)
2. Predicting Systole BP -- Present in the repository (mlr02.xls)

Logistic Regression

1. Ecommerce Dataset -- ecommerce_data.xlsx present in the folder where Logistic regression folder.

KNN, Naive Bayes, Decision Trees and Perceptron

1. train.csv --- shorturl.at/arKY0
2. mnist dataset --- https://www.kaggle.com/oddrationale/mnist-in-csv