🔷 Project Overview
The Smart Song Prediction System is an intelligent application that suggests songs to users based on their mood, listening history, and preferences. Instead of manually searching for music, the system automatically predicts and recommends songs that match the user’s taste.
This project uses Machine Learning algorithms to analyze song features such as tempo, genre, energy, and lyrics to provide accurate and personalized recommendations.
🔷 Problem Statement
Users often waste time searching for songs that suit their current mood or situation. Existing music platforms provide random or less accurate suggestions, leading to poor user experience.
🔷 Proposed Solution
Our system predicts suitable songs by:
Analyzing user behaviour and previous listening data
Understanding song attributes (genre, tempo, mood)
Matching user mood with similar song patterns
This makes music discovery faster, smarter, and more enjoyable.
🔷 How It Works (Flow)
User Input (mood / favorite song)
Data Collection (song database)
Feature Extraction (tempo, genre, energy)
Machine Learning Model Training
Song Prediction
Recommendation Display
🔷 Technologies Used
Python
Machine Learning (KNN / Decision Tree)
Spotify API / Dataset
Flask / Web Interface
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