https://github.com/zeroxjackson/knn-from-csv
Build and save K Nearest Neighbors models from .csv files!
https://github.com/zeroxjackson/knn-from-csv
csv knn nearest-neighbors-models prediction-data
Last synced: 6 months ago
JSON representation
Build and save K Nearest Neighbors models from .csv files!
- Host: GitHub
- URL: https://github.com/zeroxjackson/knn-from-csv
- Owner: zeroxjackson
- Created: 2020-01-17T22:10:17.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2020-10-03T20:19:24.000Z (over 5 years ago)
- Last Synced: 2025-07-08T19:11:35.882Z (9 months ago)
- Topics: csv, knn, nearest-neighbors-models, prediction-data
- Language: Python
- Size: 69.3 KB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
[](https://repl.it/@jacksonet00/KNN-from-CSV)
# KNN from CSV
Build and save K Nearest Neighbors models from .csv files!
## Requirements.
* Sklearn
* Pandas
* Numpy
## Functionality.
KNN from CSV contains a main program (KNN.py) as well as two subfolders (data and models). When run, this program will allow the user to select a .csv file from the data folder and build a K Nearest Neighbors model of that data which will be stored in the models folder.
## Tutorial: Run the [demo version][1] in your browser.
* Drag any number of .csv files into the data folder. This file should be 7 columns or less of data and the 7th column should contain the data to be predicted by the model.
* Run the file knn.py
* The program will ask for the name of the file you would like to model.
* The program will ask for the portion of the data to be used for testing the model. Ex: A value of 0.1 would partitian 10% of the data for testing the model's accuracy and use the remaining 90% of the data for training the model.
* The program will ask for the number of neighbors to use for the KNN model.
* The program will train a model of the data and report the accuracy of the model's predictions (this may be useful for determining if the user should retrain the model with a different number of neighbors or a different test partitian).
* The user may choose to visualize any number of tests from the accuracy testing.
* Finally, the user is given the option to save this model to the models folder.
## Loading a saved model.
This program allows the user to save a trained model using Python 3's pickle function. Once the model has been saved, it can be opened in Python using the following code:
```python
import pickle
pickle.open('models\example-file-name.sav')
```
## Future updates.
This product is currently version 1.0. The following updates and changes will come in future version of KNN from CSV:
* KNN from CSV will be able to handle .csv files with more than 7 columns.
* The user will be able to select a column to predict (currently this must be the 7th column, although columns 1-6 may be left blank).
* The prediction visualizer will be able to represent the names given in the .csv file rather than the model's numerical representation fo those values.
* The user will be able to retrain a model for improved accuracy without rerunning the program.
[1]: