This project aims to train 5 different classifiers on the RUHA dataset using the scikit-learn library. The dataset is an audio dataset, and I will explore different machine learning algorithms and techniques to train the classifiers. After training the classifiers, I will evaluate their performance by displaying the confusion matrix, accuracy, recall, precision, and F1-measure. The most important part of the project is to create a web application using Flask, a Python web framework. I will deploy our project on the web application, where users can interact with it by providing input and getting the output. For example, a user can input "Ruha lights band kardo" on the website, and the 5 trained classifiers will predict the result, which will be creatively displayed on the webpage. After displaying the result, I will provide a result analytics button that will redirect the user to another webpage, where the complete result will be displayed, including how the 5 classifiers performed on the dataset.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
You will need to have Python 3.x installed on your machine. You can download the latest version of Python here.
You will also need to have Anaconda on your machine. You can download the latest version of Anaconda here.
Clone this repository onto your local machine.
git clone https://github.com/MuhammadAhmedSuhail/RUHA-Voice-Assistant.git
Install the required packages.
pip install -r requirements.txt
Run the web app.
python app.py
The RUHA dataset contains audio samples in Urdu language.
I used the scikit-learn library to train 5 different classifiers on the RUHA dataset such as Decision Tree, Random Forest, SVM, Naive Bayes, etc. After training the classifiers, I evaluated their performance using the confusion matrix, accuracy, recall, precision, and F1-measure.
I will created a web application using Flask, a Python web framework. The web application will allow users to record a statement in Urdu language, and the 5 trained classifiers will predict the result.
In this project, I explored different machine learning algorithms to train 5 different classifiers on the RUHA dataset. I also created a web application using Flask, which allows users to interact with the classifiers by providing input and getting the output.
- Muhammad Ahmed Suhail
- This project was completed as a project for Introduction to Data Science at FAST - NUCES Islamabad.