/DSCI-310-Group-7

Group Project Repo for DSCI 310 (Group 7)

Primary LanguageJupyter NotebookOtherNOASSERTION

Prediction on the Animal Species

Group Project Repository for DSCI 310 (Group 7)


Contributors / Authors:

  • Elaine Zhou

  • Jossie Jiang

  • Swakhar Poddar

  • Weihao Sun


Abstract

The data set we used is the Zoo (1990) provided by UCL Machine Learning Repository. It stores data about 7 classes of animals and their related factors including animal name, hair, feathers and so on. In this project, we picked classification as our method to classify a given animal to its most likely type. All of 16 factors including hair, feathers, eggs, milk, airborne, aquatic, predator, toothed, backbone, breathes, venomous, fins, legs, tail, domestic, and catsize were selected as our predictors. To best predict the class of a new observation, we implemented and evaluated models based on a list of algorithms including k-Nearest Neighbor(k-NN), Decision Tree, Support Vector Machine and Logistic Regression. After a comparison among accuracies of different models, we finally found that algorithm k-NN produced the most accurate result of predicting animal type. There are several ways to repeat/reproduce our analysis, please kindly find details in Usage section.

Documents about Project Manners:

code of conduct

contributing


Analysis Results

The final report can be viewed from the following:

Jbook Html

Jbook Pdf

Rmd Html

Rmd PDF


Dependencies

A list of the dependencies packaged in the image:

Package Name Version
python 3.9.7
pandas 1.4.1
scikit-learn 1.0.2
matplotlib 3.5.1
numpy 1.22.2
pytest 7.0.1
R 4.1.2
knitr 1.38
reticulate 1.24
tidyverse 1.3.1
jupyter-book 0.12.1.

see dockerfile and docker image


Usage

0. Preparation

  • You should sign up/on a Docker account.

  • Install Docker in your computer.

  • Clone this repo

    You can find the detailed instructions of repository clone from here

    git clone https://github.com/DSCI-310/DSCI-310-Group-7.git

Then, choose one of following two suggested ways to repeat/reproduce this analysis:

1. Via Docker

a. Pull down the docker image

docker pull sasiburi/dsci-310-group-7:latest

Up to the present, the latest version is v3.2.0. You can replace latest by another specific version.

b. Run the docker image

cd to the root of the cloned repo, then run the command:

docker run --rm -p 8888:8888 -v ${PWD}:/home/jovyan/work sasiburi/dsci-310-group-7:latest

Open the link provided on your console. Now you should be able to see the repo under work/.

Possible Errors & Solutions

  • If you see the following error message: docker: Error response from daemon: Mounts denied: The path /YOURPATH is not shared from the host and is not known to Docker. Try following steps:
  1. Open Preferences
  2. Click Resources -> FILE SHARING
  3. Add your /path/to/exported/directory
  4. Restart Docker and try the command above again
  • If a token is required, it can be obtained on your console. For example, token=3912f59232fe3b260fda201da4e822e69bfed02e649dc56b, then 3912f59232fe3b260fda201da4e822e69bfed02e649dc56b is the token.
  • If the port 8888 has been occupied, you can replace the first 8888 by another four-digit number.

2. Via Makefile

a. Install all the dependencies

b. reproduce the analysis

cd to the root of the cloned repo, then run the command:

make all

c. reset the repo

make clean

History of Releases

Releases relate to each milestone are listed as follows:

  1. Milestone 1: v0.5.0
  2. Milestone 2: v0.10.0
  3. Milestone 3: v2.9.0
  4. Final Project: v3.2.0

more details could be found on the right-hand-side panel, Releases or our docker web.


licenses:

a. an MIT license for the project code

b. a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International(CC BY-NC-ND 4.0) license for the project report