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This project focuses on the development of a hate speech and offensive language detection system using machine learning techniques, implemented within Jupyter Notebooks. Leveraging a dataset of tweets obtained from Kaggle's train.csv, the goal is to train models capable of accurately classifying tweets into categories.

Jupyter Notebook 100.00%

hate-speech-detection's Introduction

User Guide

Step 1: Download the train.csv data file and the hate_speech_detection.ipynb

Step 2: Download the necessary tools to access the data.

  • Anaconda Navigator
  • Install the Jupyter Notebook

Step 3: Click launch under the Jupyter Notebook and a terminal/command prompt should appear. Stand by until a browser opens with a list of files and folders. Ensure the train.csv file and the hate_speech_detection.ipynb file are present. If the files are not there, return to step 1.

Step 4: Once all files are present, proceed by clicking on the hate_speech_detection.ipynb file. The notebook should open in a new tab.

Step 5: Once inside the notebook, selected the run tab and select Run all cells.

Step 6: The notebook will run each cell and provide details and visuals.

Step 7: Scroll to the very last cell where an input should appear. Enter a text input and a classification along with the accuracy score should return. ** To rerun a new input, simply select the cell and rerun.

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