Implementations of popular machine learning models
This repo contains implementations of popular machine learning models. This implementations are part of our course work in Machine Learning.
To get a local copy up and running follow these simple steps.
Python version >= 3.6 is required to run this algorithms without any problems.
This is an example list of dependencies present/used in the project
- numpy
- scipy
- pandas
- matplotlib
- nltk (used in naives bayes classifier for stemming and tokenizing words in database)
- tqdm (used for progressbars in neural net)
- Clone the Machine-Learning
git clone https://github.com/RikilG/Machine-Learning.git
cd Machine-Learning- Install python modules If you have anaconda installed, run this command to fetch all packages
conda install scikit-learn numpy pandas tqdm matplotlib nltkElse, install all the required packages using pip
pip -r requirements.txt- Run the files in src folder with
python <filename.py>command
repo root directory
├── AssignmentPDFs
│ ├── Assignment 1 - Machine Learning.pdf
│ └── Assignment 2 - Machine Learning.pdf
├── datasets # Datasets used by all algorithms
│ ├── a1_d1.csv
│ ├── ...
│ └── xorgate.csv
├── Images # Neural net images
│ ├── 2Layer0.2Alpha.png
│ ├── ...
│ └── Screenshot_20200426_100220.png
├── Reports
│ ├── BITS F464 - Report 1.pdf
│ └── BITS F464 - Report 2.pdf
├── src
│ ├── Fischers_Discriminant
│ │ └── fisher_discr.py
│ ├── Logistic_Regression
│ │ └── LogisticRegression.py
│ ├── Naive_Bayes
│ │ ├── naive_bayes.py
│ │ └── preprocess.py
│ └── Neural_Network
│ ├── neural_network.py
│ └── nn_core.py
├── LICENSE
├── README.md
├── requirements.txt
└── tree.txt
Distributed under the MIT License. See LICENSE for more information.
- Rikil Gajarla - 2017A7PS0202H
- L Srihari - 2017A7PS1670H
- Raj Kashyap Mallala - 2017A7PS0025H
Project Link: https://github.com/RikilG/Machine-Learning