---
license: other
license_name: joke
license_link: LICENSE
datasets:
- ConquestAce/spotify-songs
pipeline_tag: audio-classification
tags:
- music
---

This model is extremely weak. I am not good at data science

# Iterations
**null**: 

<details>
<summary><b>Trained on 500 Epoch with 2.1 million song data from Spotify Database</b></summary>
  
```
import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import pandas as pd


# Split the data into features and target variable
X = df[numerical_features[:-1]].values  # all except popularity
y = df['popularity'].values

# Split into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Standardize the features
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Convert to PyTorch tensors
X_train_tensor = torch.FloatTensor(X_train)
y_train_tensor = torch.FloatTensor(y_train).view(-1, 1)  # shape to (N, 1)
X_test_tensor = torch.FloatTensor(X_test)
y_test_tensor = torch.FloatTensor(y_test).view(-1, 1)

# Define the neural network model
class PopularityPredictor(nn.Module):
    def __init__(self):
        super(PopularityPredictor, self).__init__()
        self.fc1 = nn.Linear(X_train.shape[1], 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, 32)
        self.fc4 = nn.Linear(32, 1)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# Create an instance of the model
model = PopularityPredictor()

# Define the loss function and optimizer
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Train the model
num_epochs = 100
for epoch in range(num_epochs):
    model.train()
    optimizer.zero_grad()
    
    # Forward pass
    outputs = model(X_train_tensor)
    loss = criterion(outputs, y_train_tensor)
    
    # Backward pass and optimization
    loss.backward()
    optimizer.step()
    
    if (epoch+1) % 10 == 0:
        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')

# Evaluate the model
model.eval()
with torch.no_grad():
    predicted = model(X_test_tensor)
    
```

</details>