https://github.com/engrzulqarnain/basics_nlp_and_classical_ml_models_part_2
https://github.com/engrzulqarnain/basics_nlp_and_classical_ml_models_part_2
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/engrzulqarnain/basics_nlp_and_classical_ml_models_part_2
- Owner: ENGRZULQARNAIN
- Created: 2024-04-24T12:07:36.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-24T12:10:11.000Z (about 1 year ago)
- Last Synced: 2024-04-24T16:16:43.699Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 1.33 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Different Text Preprocessing Techniques and ML Model Performance
## Overview
This notebook explores various text preprocessing techniques and evaluates the performance of different machine learning models. This work is essential for anyone interested in understanding the impact of text preprocessing on model accuracy and performance in natural language processing tasks.
## Table of Contents
- [Loading Dataset](#loading-dataset)
- [Basic Exploratory Data Analysis (EDA)](#basic-eda)
- [Text Preprocessing and Model Training](#text-preprocessing-and-model-training)
- [Stop Words Removal](#stop-words-removal)
- [KNN & Performance Metrics](#knn)
- [Naive Bayes & Performance Metrics](#naive-bayes)
- [Logistic Regression & Performance Metrics](#logistic-regression)
- [Decision Trees & Performance Metrics](#decision-trees)
- [Random Forest & Performance Metrics](#random-forest)
- [SVM & Performance Metrics](#svm)
- [Accuracy Chart After Stop Words Removal](#accuracy-chart)
## Installation
```bash
pip install -r requirements.txt