Project description
This Python package is designed to classify fish species, distinguishing between Bream and Smelt. We use the KNN algorithm to implement this prediction system
what is special about
- This preidict system start with empty dataframe
- This machine learning rate will increase as much as the data you put in
- This machine will show you the ratio of correct answers to incorrect answers.Therefore, you can gauge the machine's level of growth from the accuracy rate.
- This machine will show you the location of your data which you typed
System Envrioment and Dependencies for Use
How to install
$ pip install SWEvolML
How to use
Experience the machine learning program that grows through the repeated execution of commands
$ gml
길이와 무게를 입력하세요:18.5 17 # Input length and weight data separated by spaces to get the prediction.
빙어 # ML will tell you prediction
정답을 입력하세요(y or n):y # Input yes or no depending on whether the answer is correct or incorrect
역시 그렇군요 # ML will answer as your respond
[정답율] # print accuracy ratio
[##########################################--------] 85.00%
[입력 데이터 위치]
location of input data # print location of your recent input data on scatter plot
┌─────────────────────────────────┐
340.0┤ B│
284.6┤ B B│
173.8┤ │
118.3┤ │
7.5┤SS S * │
└┬───────┬───────┬───────┬───────┬┘
10.5 14.5 18.5 22.5 26.5
weight length
Metadata
Release files for SWEvolML 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| swevolml-0.1.0.tar.gz | 3.7 kB | Without provenance | Details for swevolml-0.1.0.tar.gz |
Built distributions (wheels)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| swevolml-0.1.0-py3-none-any.whl | Python 3 | none | any | Details for swevolml-0.1.0-py3-none-any.whl |
| SWEvolML-0.1.0-py3-none-any.whl | Python 3 | none | any | Details for SWEvolML-0.1.0-py3-none-any.whl |
Total release size: 12.2 kB
Release files / swevolml-0.1.0.tar.gz
| Download URL | swevolml-0.1.0.tar.gz |
|---|---|
| Size | 3.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
a79d10eee4aff3cf2298631bc638fc3668e99090decf259fafdbfe8a321213d7
|
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
pdm/2.18.1 CPython/3.12.4 Linux/5.15.153.1-microsoft-standard-WSL2
|
Release files / swevolml-0.1.0-py3-none-any.whl
| Download URL | swevolml-0.1.0-py3-none-any.whl |
|---|---|
| Size | 4.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
3cecbe1dc3a9be505a886696055a796edd1ef7039bf66538b4bd57eb0195a12e
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BLAKE2b-256 checksum How to use checksums |
259ec2ef6ad2e97e0f4c42b3d8b03b99f705791148240fa3e01761854d10d92c
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
pdm/2.18.1 CPython/3.12.4 Linux/5.15.153.1-microsoft-standard-WSL2
|
Release files / SWEvolML-0.1.0-py3-none-any.whl
| Download URL | SWEvolML-0.1.0-py3-none-any.whl |
|---|---|
| Size | 4.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
1148921597d20840457e3a5db9d795d54152e35e064a280005f3f6403464a548
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BLAKE2b-256 checksum How to use checksums |
4adb9e02708f7b5064c88f28de1cce827b3f4dce333f4c85e40986dd762c1cea
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
pdm/2.18.1 CPython/3.12.4 Linux/5.15.153.1-microsoft-standard-WSL2
|
Security for SWEvolML 0.1.0
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