Hands-on Data Science Project: Voice Gender Recognition 🎙️ | EDA | Machine Learning Model | ML | AI
🎯 Learn how to build a Machine Learning model that predicts gender from voice recordings — step-by-step and hands-on!
In this real-world ML project, we explore how to classify voices as *male or female* using classical machine learning algorithms like *Logistic Regression, SVM, and Random Forest* — all based on extracted audio features.
🔍 What You’ll Learn:
✔️ Loading and exploring voice-based datasets
✔️ Cleaning data and removing outliers using the IQR method
✔️ Visualizing data with EDA techniques
✔️ Applying feature scaling and train-test splitting
✔️ Building and evaluating ML models (SVC, Random Forest, Logistic Regression)
✔️ Hyperparameter tuning using GridSearchCV
✔️ Understanding model performance and feature importance
📁 GitHub Repo: https://github.com/udaybhan10/VoiceGe...
📊 Dataset Source: https://www.kaggle.com/datasets/prima...
🛠️ Tools Used: Python, Pandas, Seaborn, Matplotlib, Scikit-learn
🕒 Timestamps:
0:15 - Project Overview
1:54 - Data Loading and Exploration
4:43 - Data Preprocessing (Outlier Removal with IQR)
8:21 - Exploratory Data Analysis (EDA)
10:39 - Feature Scaling and Train-Test Split
11:02 - Model Training (SVC, Random Forest, Logistic Regression)
12:38 - Hyperparameter Tuning with GridSearchCV
17:40 - Feature Importance
18:31 - Final Accuracy & Model Comparison
✅ Perfect For:
Data science and ML beginners
Resume & portfolio projects
Students working on audio/speech classification
Anyone looking to apply ML to real-world problems
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