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https://github.com/jahnvisahni31/predict_bike_sharing_demand
This project leverages AutoGluon in AWS SageMaker Studio to predict bike sharing demand, automating model training and tuning for accurate forecasting.
https://github.com/jahnvisahni31/predict_bike_sharing_demand
autogluon automl aws bikesharing datascience jupyternotebook jupyternotebooks machinelearning predictiveanalytics sagemaker
Last synced: 18 days ago
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This project leverages AutoGluon in AWS SageMaker Studio to predict bike sharing demand, automating model training and tuning for accurate forecasting.
- Host: GitHub
- URL: https://github.com/jahnvisahni31/predict_bike_sharing_demand
- Owner: jahnvisahni31
- License: apache-2.0
- Created: 2024-04-27T21:49:20.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-05-18T17:30:05.000Z (9 months ago)
- Last Synced: 2024-11-14T17:08:03.348Z (3 months ago)
- Topics: autogluon, automl, aws, bikesharing, datascience, jupyternotebook, jupyternotebooks, machinelearning, predictiveanalytics, sagemaker
- Language: HTML
- Homepage: https://colab.research.google.com/github/jahnvisahni31/predict_bike_sharing_with_autogluon/blob/main/predict_bike_sharing_with_autogluon.ipynb
- Size: 781 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Predict Bike Sharing with AutoGluon in AWS SageMaker Studio
This repository contains a Jupyter Notebook (`.ipynb` file) that demonstrates how to use AutoGluon to predict bike sharing demand in AWS SageMaker Studio. AutoGluon is a powerful AutoML toolkit that automates the process of training and tuning machine learning models.
## Table of Contents
- [Prerequisites](#prerequisites)
- [links](#links)
- [Installation](#installation)
- [Dataset](#dataset)
- [Usage](#usage)
- [AutoGluon Workflow](#autogluon-workflow)
- [Evaluation](#evaluation)
- [Cleanup](#cleanup)
- [References](#references)
## Prerequisites
Before you begin, ensure you have the following:
- An AWS account with access to SageMaker Studio.
- SageMaker Studio set up in your AWS environment.
- Basic knowledge of Jupyter Notebooks and Python.
## Links
[Open in Google Colab](https://colab.research.google.com/github/jahnvisahni31/predict_bike_sharing_with_autogluon/blob/main/predict_bike_sharing_with_autogluon.ipynb)
## Installation
1. **Clone the repository:**
```bash
git clone https://github.com/jahnvisahni31/predict_bike_sharing_with_autogluon.git
cd predict_bike_sharing_with_autogluon
```
2. **Open SageMaker Studio:**
Launch SageMaker Studio from the AWS Management Console.
3. **Upload the Notebook:**
Upload the `predict_bike_sharing_with_autogluon.ipynb` file to your SageMaker Studio environment.
4. **Install Required Libraries:**
Open a terminal in SageMaker Studio and run the following command to install AutoGluon:
```bash
pip install autogluon
```
## Dataset
The dataset used in this example is the [Bike Sharing Demand dataset](https://www.kaggle.com/c/bike-sharing-demand) from Kaggle. You can download the dataset and upload it to your SageMaker Studio environment.
## Usage
1. **Open the Notebook:**
Open the `predict_bike_sharing_with_autogluon.ipynb` file in SageMaker Studio.
2. **Follow the Steps:**
Follow the steps in the notebook to:
- Load the dataset.
- Preprocess the data.
- Train the model using AutoGluon.
- Evaluate the model's performance.
## AutoGluon Workflow
The notebook demonstrates the following AutoGluon workflow:
1. **Import Libraries:**
```python
from autogluon.tabular import TabularPredictor
```
2. **Load Dataset:**
```python
import pandas as pd
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
```
3. **Train Model:**
```python
predictor = TabularPredictor(label='count').fit(train_data)
```
4. **Evaluate Model:**
```python
performance = predictor.evaluate(test_data)
print(performance)
```
## Evaluation
The notebook includes steps to evaluate the trained model on a test set, providing metrics such as RMSE (Root Mean Squared Error) to measure the model's performance.
## Cleanup
After completing the notebook, remember to clean up any resources to avoid unnecessary charges:
- Delete any endpoints or instances created during the process.
- Remove datasets and notebooks from your SageMaker Studio environment if no longer needed.
## References
- [AutoGluon Documentation](https://auto.gluon.ai/stable/index.html)
- [AWS SageMaker Studio Documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/studio.html)
- [Bike Sharing Demand Dataset on Kaggle](https://www.kaggle.com/c/bike-sharing-demand)
---
This README provides a high-level overview of using AutoGluon for bike sharing prediction in AWS SageMaker Studio. For detailed instructions and code, please refer to the included Jupyter Notebook.