BalanceDataProject

Task 1 - Build a decision Tree model

BalanceDataDT

Decision Tree Model on balance data from UCI -> Dataset

Project Submission Guidelines:

Don't use Github GUI at all, do all work in CLI mode (except for making Pull Request)

1. First Fork the Repo.
2. Then go to issue section of my repo.
3. Search the Issue with your name.
4. Ask me in Comments to assign the issue to you.
5. After that you can clone the repo of yours in your local system git clone https://Your/Repo/Name/ and
6. then make a dev branch using -> git checkout -b dev
7. upload the project in folder according to name.
8. Your project files should contain a .gitignore file (So that you don't have to upload redundant files like '.ipynb_checkpoints',etc).
9. Update the the readme files of your project folder
10. then add git add . and commit git commit -m "add project files" all your work
11. to your origin/dev branch using -> git push -u origin dev
12. And then finally make A Pull Request (from your dev branch to my main branch) by going to your github forked repo.
13. Write a suitable title and comment in your Pull Request(PR).
14. If Everything will be fine It'll get merge.

Dataset Description

1. Title: Balance Scale Weight & Distance Database

2. Source Information: (a) Source: Generated to model psychological experiments reported by Siegler, R. S. (1976). Three Aspects of Cognitive Development. Cognitive Psychology, 8, 481-520. (b) Donor: Tim Hume (hume@ics.uci.edu) (c) Date: 22 April 1994

3. Past Usage: (possibly different formats of this data)

   • Publications
   1. Klahr, D., & Siegler, R.S. (1978). The Representation of Children's Knowledge. In H. W. Reese & L. P. Lipsitt (Eds.), Advances in Child Development and Behavior, pp. 61-116. New York: Academic Press
   2. Langley,P. (1987). A General Theory of Discrimination Learning. In D. Klahr, P. Langley, & R. Neches (Eds.), Production System Models of Learning and Development, pp. 99-161. Cambridge, MA: MIT Press
   3. Newell, A. (1990). Unified Theories of Cognition. Cambridge, MA: Harvard University Press
   4. McClelland, J.L. (1988). Parallel Distibuted Processing: Implications for Cognition and Development. Technical Report AIP-47, Department of Psychology, Carnegie-Mellon University
   5. Shultz, T., Mareschal, D., & Schmidt, W. (1994). Modeling Cognitive Development on Balance Scale Phenomena. Machine Learning, Vol. 16, pp. 59-88.

4. Relevant Information: This data set was generated to model psychological experimental results. Each example is classified as having the balance scale tip to the right, tip to the left, or be balanced. The attributes are the left weight, the left distance, the right weight, and the right distance. The correct way to find the class is the greater of (left-distance * left-weight) and (right-distance * right-weight). If they are equal, it is balanced.

5. Number of Instances: 625 (49 balanced, 288 left, 288 right)

6. Number of Attributes: 4 (numeric) + class name = 5

7. Attribute Information:

   1. Class Name: 3 (L, B, R)
   2. Left-Weight: 5 (1, 2, 3, 4, 5)
   3. Left-Distance: 5 (1, 2, 3, 4, 5)
   4. Right-Weight: 5 (1, 2, 3, 4, 5)
   5. Right-Distance: 5 (1, 2, 3, 4, 5)

8. Missing Attribute Values: none

9. Class Distribution:

   1. 46.08 percent are L
   2. 07.84 percent are B
   3. 46.08 percent are R