Internship_project(Gender predection using machine learning algorithms)

Problem statement:

Create a classification model to predict the gender (male or female) based on different acoustic parameters

Context:

This database was created to identify a voice as male or female, based upon acoustic properties of the voice and speech. The dataset consists of 3,168 recorded voice samples, collected from male and female speakers. The voice samples are preprocessed by acoustic analysis in R using the seewave and tuneR packages, with an analyzed frequency range of 0hz-280hz (human vocal range).

Column Description:

Classifier Algorithms used:

Dataset:

https://drive.google.com/file/d/1OqjMamx5LuejWLbQXJOrvwCXu5nAZr96/view?usp=sharing

Conclusion :

From the above bar graph which shows the accuracy of different classifiers , it is pretty evident that Random Forest Classifier reported with best accuracy compared to other algorithms with 98% Accuracy.