Update README.md

README.md

@@ -7,11 +7,165 @@ datasets:
 pipeline_tag: audio-classification
 tags:
 - music
+- spotify
+- machine-learning
+- music-prediction
+- data-science
+- regression
+- classification
+- popularity-analysis`
 ---
 
-This model is extremely weak. I am not good at data science
+# 🎵 Spotify Song Popularity Prediction
 
-# Iterations
+Predict the popularity of a song based on its audio features and estimate potential Spotify royalties.
+
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
+
+---
+
+## 📖 Project Overview
+
+This project explores machine learning models to predict the popularity of songs using publicly available features such as danceability, energy, tempo, and valence. It also demonstrates a prototype pricing tool that estimates potential Spotify revenue based on predicted popularity.
+
+Despite the challenges in accurately forecasting popularity due to time-evolving factors, our models show that **minimum popularity and expected revenue can be estimated** using machine learning techniques.
+
+---
+
+## 📊 Dataset
+
+- **Source**:  
+  - Spotify Web API
+  - Original Dataset (~114,000 songs) expanded to **~2 million songs**
+- **Features**:  
+  - Acoustic features (energy, danceability, valence, etc.)
+  - Target variable: `popularity` (integer from 0–100)
+
+---
+ 
+## 🔬 Methods
+
+- **Data Cleaning and Preparation**:
+  - Removed zero-popularity entries, duplicates (~8% of rows), and outliers
+  - Standardized genres using clustering
+- **Exploratory Data Analysis (EDA)**:
+  - Analyzed distributions, correlations, and cumulative trends
+- **Modeling**:
+  - Linear Regression, Ridge Regression
+  - Decision Tree, Random Forest, AdaBoost (best recall: 86% on popular songs)
+  - XGBoost (binning) and Neural Networks
+- **Revenue Estimation**:
+  - Quadratic regression fit between predicted popularity and play counts
+  - Prototype pricing tool predicting Spotify revenue for songs
+
+---
+
+## 🏆 Results
+
+| Model                  | Highlights                                    |
+|-------------------------|----------------------------------------------|
+| Linear/Ridge Regression | Poor fit due to complex, noisy data          |
+| Random Forest           | Best overall stability (recall on populars)  |
+| AdaBoost (weighted)     | **Best performance**: 86% recall for popular songs |
+| Neural Networks         | Showed challenges due to "popularity" instability |
+
+- Predicted revenue for a song with **popularity 55** ≈ **\$357,000 CAD**.
+- Pricing tool demonstrated practical viability despite prediction limitations.
+
+---
+
+## 📈 Example
+
+Predicting a song’s revenue based on its feature vector:
+
+```python
+# Example (simplified)
+predicted_popularity = model.predict(features)
+predicted_revenue = pricing_function(predicted_popularity)
+```
+
+---
+
+## 🚀 How to Run
+
+```bash
+# Clone this repo
+git clone https://huggingface.co/username/spotify-popularity-prediction
+
+# Install dependencies
+pip install -r requirements.txt
+
+# Train or evaluate models
+python train_models.py
+python evaluate_models.py
+
+# Predict song revenue
+python pricing_tool.py
+```
+
+(Adaptable scripts for different model types: AdaBoost, Random Forest, Neural Net.)
+
+---
+
+## 🤔 Limitations
+
+- Song features alone are **not sufficient** for high-accuracy predictions.
+- "Popularity" is a **time-dependent** and **dynamic** metric.
+- Genre diversity (>5000 unique genres) complicated modeling.
+
+---
+
+## 🧠 Future Work
+
+- Predict **play count** directly instead of popularity.
+- Fine-tune **XGBoost** and **deep neural networks** on larger datasets.
+- Integrate **time-evolution models** for dynamic popularity changes.
+- Improve genre classification with unsupervised learning (e.g., genre embeddings).
+
+---
+
+## 📚 Citation
+
+If you use this project, please cite:
+
+```bibtex
+@misc{bhuiyan2024spotify,
+  title={Spotify Song Popularity Prediction},
+  author={Ashiful Bhuiyan, Blanca Fernández Méndez, Nazanin Ghelichi, Pavle Curcin},
+  year={2024},
+  institution={York University},
+}
+```
+
+---
+
+## 🧑‍💻 Authors
+
+- Ashiful Bhuiyan
+- Blanca Elvira Fernández Méndez
+- Nazanin Ghelichi
+- Pavle Curcin
+
+---
+## 📄 License
+
+This project is licensed under the [MIT License](LICENSE).
+
+---
+
+---
+**Would you also like me to create**:
+- A **`README.md` file** version you can upload directly? 
+- A **short Hugging Face model card** (if you plan to deploy it as a model too)?  
+
+(They have slightly different requirements!) 🎯  
+Would you like it? 🚀
+
+# `popularity_predictor.pth`
+
+This neural network model is extremely weak. I was not good at data science when I made this
+
+## Iterations
 **null**: 
 
 <details>
@@ -90,4 +244,7 @@ with torch.no_grad():
     
 ```
 
-</details>
+</details>
+
+# 🏷 Tags
+`#spotify` `#machine-learning` `#music-prediction` `#data-science` `#regression` `#classification` `#popularity-analysis`