https://github.com/mikeleo03/supervised-learning-algorithm
Implementation of KNN and Naive-Bayes Supervised Learning Algorithm from Scratch to Cluster Phone Dataset
https://github.com/mikeleo03/supervised-learning-algorithm
artificial-intelligence data-clustering jupyter-notebook kaggle knn-algorithm naive-bayes-algorithm python3
Last synced: 6 days ago
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Implementation of KNN and Naive-Bayes Supervised Learning Algorithm from Scratch to Cluster Phone Dataset
- Host: GitHub
- URL: https://github.com/mikeleo03/supervised-learning-algorithm
- Owner: mikeleo03
- Created: 2023-11-09T04:05:35.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-11-29T15:51:24.000Z (over 1 year ago)
- Last Synced: 2025-01-23T08:43:04.831Z (5 months ago)
- Topics: artificial-intelligence, data-clustering, jupyter-notebook, kaggle, knn-algorithm, naive-bayes-algorithm, python3
- Language: Jupyter Notebook
- Homepage:
- Size: 1.91 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Tugas Besar 2 IF3170 - Intelegensi Buatan
Supervised-Learning Algorithm
> Disusun untuk memenuhi Tugas Besar 2 - Supervised learning Algorithm | IF3170 Intelegensia Buatan tahun 2023/2024
## Table of Contents
1. [General Info](#general-information)
2. [Creator Info](#creator-information)
3. [Features](#features)
4. [Technologies Used](#technologies-used)
5. [Structure](#structure)
## General Information
Pada tugas besar ini, Kami melakukan implementasi algoritma pembelajaran mesin KNN dan Naive-Bayes (sesuai dengan cakupan materi kuliah IF3170 - Intelegensia Buatan). Data yang digunakan pada implementasi ini sama seperti data [tugas kecil 2](https://drive.google.com/file/d/14kZUHH39P9-U2W8KDJt1i2X1wVJ_45bf/view?usp=drive_link). Kami melakukan proses pelatihan model menggunakan data latih yang terdapat pada pranala tersebut, kemudian dilakukan validasi hasil dengan menggunakan data validasi untuk mendapatkan _insight_ seberapa baik model melakukan generalisasi.Tahap selanjutnya adalah melakukan perbandingan hasil implementasi algoritma KNN dan Naive-Bayes kelompok Kami dengan algoritma milik pustaka eksternal _scikit-learn_. Parameter perbandingan yang digunakan, antara lain: _precision_, _recall_, _F1-score_, _support_, _accuracy_, _macro avg_, dan _weighted avg_.
## Creator Information
| Nama | NIM | E-Mail |
| --------------------------- | -------- | --------------------------- |
| Michael Leon Putra Widhi | 13521108 | [email protected] |
| Muhammad Zaki Amanullah | 13521146 | [email protected] |
| Mohammad Rifqi Farhansyah | 13521166 | [email protected] |
| Nathan Tenka | 13521172 | [email protected] |
## Features
1. Implementasi algoritma KNN dan Naive-Bayes
2. Perbandingan hasil implementasi algoritma KNN dan Naive-Bayes dengan pustaka eksternal _scikit-learn_
3. Penyimpanan dan load _model_
4. Submisi kaggle
## Technologies Used
- python
- numpy
- pandas
- matplotlib
- scikit-learn
## Structure
```bash
│ README.md
│
├───data
│ data_train.csv
│ data_validation.csv
│ full_data.csv
│ test.csv
│
├───result
│ predictions-knn.csv
│ predictions-naive-bayes.csv
│
└───src
│ knn.ipynb
│ naive.ipynb
│
├───algorithm
│ knn.py
│ naiveBayes.py
│ weightedKnn.py
│
├───models
│ knn_model.pkl
│ naive_bayes_model.pkl
│
└───utils
scaler.py
```