This project focuses on predicting trip durations for the "Mi Bici" public bike-sharing system in Guadalajara, Mexico. We leverage machine learning models to analyze ride patterns and provide real-time trip duration predictions.
🔗 Data Source: Mi Bici Open Data
📅 Training Data: 2024 trip records
📅 Testing Data: 2025 trip records
This project integrates:
- MLflow for experiment tracking & model versioning.
- FastAPI for real-time trip duration predictions.
- Automated retraining when model performance degrades.
.
├───api
│ ├───logs # Stores logs from API requests
│ ├───saved_inputs # Stores API input data for retraining
│ │
│ │ app.py # FastAPI application
│ │ README.md # API documentation
│ │ ss_api_test.JPG # Screenshot of Postman test
│
├───configs
│ │ model1.yaml # Configuration file for model settings
│
├───data
│ ├───external # External data sources (if applicable)
│ ├───interim # Intermediate processed data
│ ├───processed
│ │ ├───2024 # Processed dataset for training
│ │ │ combined_2024.csv # Combined processed data
│ │ │ test.csv # Test dataset
│ │ │ train.csv # Training dataset
│ │
│ └───raw
│ ├───2024 # Raw dataset from Mi Bici 2024
│ │ datos_abiertos_2024_01.csv
│ │ datos_abiertos_2024_02.csv
│ │ datos_abiertos_2024_03.csv
│ │ ...
│ │ datos_abiertos_2024_12.csv
│ │
│ │ nomenclatura_2024_12.csv # Data dictionary
│
├───docs # Project documentation
│
├───MLFlow
│ ├───mlartifacts # Stores MLflow artifacts
│ ├───mlruns # MLflow experiment tracking
│ │
│ │ infer.py # Model inference script
│ │ README.md # MLflow documentation
│ │ requirements.txt # Dependencies for MLflow
│ │ retrain_model.py # Automated retraining script
│ │ ss_mlfow_dashboard.JPG # Screenshot of MLflow dashboard
│ │ train.py # Model training script
│
├───models
│ │ lr_mae-286.9239_2025-01-29.pkl # Latest linear regression model
│ │ random_forest_mae-273.4929_2025-01-29.pkl # Latest random forest model
│
├───notebooks
│ │ 1.IRA_data_preprocessing.ipynb # Data cleaning notebook
│ │ 2.IRA_data_vizualization.ipynb # Exploratory Data Analysis (EDA)
│ │ 3.IRA_modeling.ipynb # Model training and evaluation
│ │ 4.IRA_retraining.ipynb # Model retraining analysis
│
├───references # References and additional documentation
│
├───reports
│ ├───figures
│ │ umap_2d_K3.png # Visualization of UMAP embeddings
│
└───src
├───data
│ │ build_features.py # Feature engineering
│ │ cleaning.py # Data cleaning functions
│ │ ingestion.py # Data loading
│ │ labeling.py # Labeling for supervised learning
│ │ splitting.py # Train-test split
│ │ validation.py # Data validation
│
├───models
│ ├───model1
│ │ │ dataloader.py # Loads data for training
│ │ │ hyperparameters_tuning.py # Hyperparameter tuning
│ │ │ model.py # Model definition
│ │ │ predict.py # Inference script
│ │ │ preprocessing.py # Preprocessing functions
│ │ │ train.py # Training script
│
├───visualization
│ │ evaluation.py # Model evaluation scripts
│ │ exploration.py # Data exploration and visualization
│
│ __init__.py
.env
.gitignore
LICENSE
Makefile
README.md
requirements.txt
- Uses Linear Regression to predict trip durations.
- Trained on 2024 Mi Bici data.
- Logs:
- Hyperparameters
- Performance metrics (MAE, RMSE)
- Model artifacts & schema in MLflow.
- Deploys the trained model as a REST API.
- Logs every request & prediction for monitoring.
- Saves input data (
saved_inputs/prediction_inputs.csv) for future retraining.
- Monitors model performance on 2025 Mi Bici test data.
- Retrains the model if MAE increases beyond a threshold.
- Uses both stored API inputs & new Mi Bici data for retraining.
- Registers new models in MLflow if performance improves.
- Python (ML model + API)
- scikit-learn (Linear Regression + preprocessing)
- FastAPI (Real-time model inference)
- MLflow (Model tracking & versioning)
- pandas & NumPy (Data processing)
- joblib (Model serialization)
- Logging & CSV storage (For monitoring & retraining)
1️⃣ Train the Model (train.py):
- Loads Mi Bici 2024 dataset → Preprocesses → Trains model → Logs in MLflow.
2️⃣ Deploy API (api.py):
- Loads MLflow model → Serves predictions via FastAPI.
3️⃣ Log Predictions for Retraining:
- API logs input requests → Saves them for future training.
4️⃣ Monitor & Retrain (retrain_model.py):
- Uses Mi Bici 2025 test data to check performance.
- If MAE degrades, merges new inputs + original data.
- Retrains & registers new model in MLflow.
| Column Name | Description |
|---|---|
| Trip_Id | Unique trip identifier |
| User_Id | Unique user identifier |
| Gender | Gender of the user |
| Year_of_Birth | Year of birth of the user |
| Trip_Start | Start timestamp of the trip |
| Trip_End | End timestamp of the trip |
| Origin_Id | Origin bike station ID |
| Destination_Id | Destination bike station ID |
| Trip_Duration | Total trip duration in seconds |
| Start_Hour | Hour of day when the trip started |
| Start_DayOfWeek | Day of the week (Monday=0, Sunday=6) |
pip install -r requirements.txtpython scripts/train.pyThis will train the model using Mi Bici 2024 data and log it in MLflow.
uvicorn scripts.api:app --host 0.0.0.0 --port 8000 --reloadThe API will be available at http://127.0.0.1:8000.
$headers = @{
"Content-Type" = "application/json"
}
$body = @{
"Year_of_Birth" = 1995
"Gender" = 1
"Origin_Id" = 10
"Destination_Id" = 50
"Start_Hour" = 14
"Start_DayOfWeek" = 2
} | ConvertTo-Json -Depth 10
$response = Invoke-RestMethod -Uri "http://127.0.0.1:8000/predict" -Method Post -Headers $headers -Body $body
Write-Output $responsecurl -X POST "http://127.0.0.1:8000/predict" \
-H "Content-Type: application/json" \
-d '{"Year_of_Birth": 1995, "Gender": 1, "Origin_Id": 10, "Destination_Id": 50, "Start_Hour": 14, "Start_DayOfWeek": 2}'mlflow uiCheck experiment logs & model versions at http://127.0.0.1:5000.
Schedule retrain_model.py to run daily (or as needed) using:
- Linux (Cron Job)
Add:
crontab -e
0 */12 * * * /usr/bin/python3 /path/to/retrain_model.py
- Windows (Task Scheduler)
- Set Trigger: Run every 12 hours.
- Set Action: Run:
python C:\path\to\retrain_model.py