Upload README.md

README.md

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+# 🎵 Music Preference Classification using Spotify Audio Features
+
+## 📄 Project Overview
+
+This project aims to classify whether a song is **liked** or **disliked** based on its audio features from Spotify. The dataset includes a curated collection of tracks categorized by personal music taste, and machine learning models are used to identify patterns that predict preference.
+
 ---
-license: mit
+
+## 📚 Table of Contents
+
+1. [Playlist Creation](#1-🎼-playlist-creation)
+2. [Data Collection](#2-📊-data-collection)
+3. [Features Description](#3-🎵-features-description)
+4. [Target Variable](#4-📈-target-variable)
+5. [Project Structure](#5-📂-project-structure)
+6. [Usage](#6-🚀-how-to-use)
+
 ---
+
+## 1. 🎼 Playlist Creation
+
+### Liked Songs (100 tracks):
+
+* Mainly French Rap
+* Some American Rap, Rock, Electro
+
+### Disliked Songs (95 tracks):
+
+* 25 Metal (e.g. Cannibal Corpse)
+* 20 Disliked Rap (e.g. PNL)
+* 25 Classical
+* 25 Disco
+
+> Pop songs are excluded as the user is neutral about them.
+
+---
+
+## 2. 📊 Data Collection
+
+### Step-by-step:
+
+#### 2.1 Get Playlist Track IDs
+
+* Spotify API: `Get a Playlist's Items`
+* Fields: `items(track(id,name))`
+* Output saved to: `ids/yes.py`, `ids/no.py`
+
+#### 2.2 Convert JSON to Track IDs
+
+* Script: `ids/ids_to_data.py`
+* Output: Comma-separated string of track IDs
+
+#### 2.3 Fetch Audio Features
+
+* Spotify API: `Get Audio Features for Several Tracks`
+* Output Files:
+
+  * `data/good.json`
+  * `data/dislike.json`
+
+---
+
+## 3. 🎵 Features Description
+
+Each track is represented by 13 Spotify audio features:
+
+| Feature            | Type        | Description                          |
+| ------------------ | ----------- | ------------------------------------ |
+| `acousticness`     | float (0–1) | Likelihood track is acoustic         |
+| `danceability`     | float (0–1) | Suitability for dancing              |
+| `duration_ms`      | int         | Track length in ms                   |
+| `energy`           | float (0–1) | Intensity/activity level             |
+| `instrumentalness` | float (0–1) | Likelihood of no vocals              |
+| `key`              | int (0–11)  | Musical key (0 = C, 1 = C#/Db, etc.) |
+| `liveness`         | float (0–1) | Probability of live performance      |
+| `loudness`         | float (dB)  | Average loudness                     |
+| `mode`             | int (0/1)   | 1 = major, 0 = minor                 |
+| `speechiness`      | float (0–1) | Presence of spoken words             |
+| `tempo`            | float       | Tempo in BPM                         |
+| `time_signature`   | int         | Beats per measure                    |
+| `valence`          | float (0–1) | Musical positiveness                 |
+
+---
+
+## 4. 📈 Target Variable
+
+| Column  | Type | Description             |
+| ------- | ---- | ----------------------- |
+| `liked` | int  | 1 = Liked, 0 = Disliked |
+
+---
+
+
+---
+
+## 5. 🚀 How to Use
+
+1. **Install dependencies:**
+
+```bash
+pip install -r requirements.txt
+```
+
+2. **Convert playlist JSON to track IDs** using `ids_to_data.py`
+
+3. **Fetch features from Spotify API** using the ID strings
+
+4. **Run EDA and train models** in Jupyter notebooks
+
+5. **Save and evaluate models**
+
+---
+
+## 🔹 Notes
+
+* Requires a valid **Spotify Developer token**.
+* Fully customizable to user preferences.
+* Great example of personalized ML using real-world API data.
+
+---
+
+Feel free to fork, modify, or contribute to this music recommendation project!