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---
language: en
license: apache-2.0
tags:
- keras
- tensorflow
- time-series
- menstrual-cycle-prediction
- healthcare
pipeline_tag: time-series-forecasting
model-index:
- name: lstm_combined_model
results:
- task:
type: time-series-forecasting
name: Menstrual Cycle Prediction
metrics:
- type: mae
value: 1.2
name: Mean Absolute Error (MAE)
- type: mse
value: 2.5
name: Mean Squared Error (MSE)
---
# 🩸 **Cycle Sync: Menstrual Cycle Prediction using LSTM**
## 🚀 **Model Overview**
The `cycle-sync` model is built using a Long Short-Term Memory (LSTM) architecture trained to predict menstrual cycle lengths and period durations based on a user’s past period history.
## 🔥 **Model Highlights**
- 🧠 **Architecture:** LSTM (Long Short-Term Memory) with time-series inputs.
- 📊 **Purpose:** Predict the next period start date and duration based on previous cycle data.
- 🎯 **Task Type:** `time-series-forecasting`
- 📚 **Framework:** Keras with TensorFlow backend.
- 📈 **Scalers:** `MinMaxScaler` used for feature and label scaling.
## 📡 **Usage**
### 🎨 **Load Model**
To load the model from Hugging Face, use the following code:
```python
import keras
from datetime import timedelta
import numpy as np
import pickle
# Load the model from Hugging Face
model = keras.saving.load_model("hf://VishSinh/cycle-sync")
# Load the scalers (if needed)
with open("feature_scaler.pkl", "rb") as f:
feature_scaler = pickle.load(f)
with open("label_scaler.pkl", "rb") as f:
label_scaler = pickle.load(f)
``` |