RobuRishabh/Text_Classification_CodingNinjas

Performed Text Classification using Multinomial Naive Bayes, Also implemented naive bayes then compared results of the implementations.

Text Classification Project

This project implements a text classification pipeline using machine learning techniques. It walks through data preprocessing, feature extraction, model training, evaluation, and prediction. The goal is to classify text data into various categories using different machine learning models.

Table of Contents

1. Overview
2. Prerequisites
3. Project Structure
4. Installation
5. Usage
6. Example Output
7. Customization
8. License

Overview

The notebook implements the following steps for text classification:

1. Data Preprocessing: Cleaning and preparing text data, including stopwords removal and tokenization.
2. Feature Extraction: Converting text into numerical features using methods like Term Frequency-Inverse Document Frequency (TF-IDF).
3. Model Training: Training models such as Naive Bayes, Logistic Regression, or Random Forest.
4. Evaluation: Assessing the performance of the trained model using metrics like accuracy, precision, recall, and F1-score.
5. Prediction: Classifying unseen data with the trained model.

Prerequisites

Ensure the following Python libraries are installed before running the notebook:

• numpy
• pandas
• scikit-learn
• nltk
• matplotlib

To install the required libraries, use the following command:

pip install numpy pandas scikit-learn nltk matplotlib

Project Structure

The project contains the following key files:

• text_classification.ipynb: The Jupyter notebook containing the full code and implementation of the text classification pipeline.
• README.md: This file provides an overview of the project, instructions on how to set up, and usage details.

Installation

To get started with the project, clone the repository and install the required dependencies.

1. Clone the repository:

   git clone <repository-url>
   cd <repository-folder>

2. Install the required libraries:

   pip install -r requirements.txt

Usage

1. Open the text_classification.ipynb file using Jupyter Notebook or JupyterLab.

   jupyter notebook text_classification.ipynb

2. Run the cells in sequence to preprocess data, train models, and evaluate performance.

3. Modify the data or models as needed to experiment with different configurations.

Example Output

Here is an example of the output from the classification model:

• Accuracy: 0.85
• Precision: 0.84
• Recall: 0.83
• F1-score: 0.84

You can visualize the model performance using confusion matrices, classification reports, and more.

Customization

Feel free to customize the notebook as per your needs:

• Data Preprocessing: Modify the stopwords, tokenization method, or text cleaning process.
• Models: Experiment with different machine learning models such as Support Vector Machines (SVM), Random Forest, or XGBoost.
• Feature Extraction: Use other feature extraction methods like Word2Vec, BERT embeddings, or custom feature engineering.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

This `README.md` provides a comprehensive guide on how to use and understand the project, making it easier for users to navigate. Let me know if you want to add or change anything!