1. Abstract

Bike rental services have become increasingly popular in modern cities as a convenient and eco-friendly mode of transportation. These services allow users to rent bikes on a daily or hourly basis for traveling within a city. Predicting the demand for bike rentals is important for service providers to efficiently manage their resources and improve customer satisfaction.

This project focuses on predicting the number of bikes rented per day based on different environmental and seasonal factors such as temperature, humidity, weather conditions, season, and holiday information.

The dataset used in this project contains daily bike rental data for 731 days, including various attributes that influence the number of bikes rented.

Machine learning algorithms such as Linear Regression, Random Forest, and XGBoost are used to build predictive models for estimating the bike rental count.

Additionally, the project also uses H2O AutoML, an automated machine learning library that automatically trains multiple models, compares their performance, and selects the best model for prediction.

The project includes several stages such as data analysis, data preprocessing, feature engineering, model building, model evaluation, and comparison with AutoML models.

This project demonstrates how machine learning and automated machine learning can help businesses forecast demand and improve decision-making in rental services.

2. Objectives

The main objectives of this project are:

1. To understand the concept of bike rental demand prediction using machine learning.
2. To analyze the dataset and identify relationships between environmental factors and bike rental count.
3. To perform data preprocessing and feature engineering to prepare the dataset for machine learning models.
4. To build predictive models using machine learning regression algorithms.
5. To apply machine learning techniques such as Linear Regression, Random Forest, and XGBoost.
6. To implement Automated Machine Learning using H2O AutoML.
7. To compare traditional machine learning models with AutoML models.
8. To develop a system capable of predicting bike rental demand based on environmental conditions.

3. Existing System

In traditional systems, bike rental companies track rental counts manually or use simple statistical analysis to estimate demand.

However, these methods have several limitations:

1. Traditional systems cannot accurately predict future bike rental demand.
2. Manual data analysis is time-consuming.
3. Environmental and seasonal factors are difficult to analyze manually.
4. Traditional systems cannot automatically forecast rental demand.
5. Decision making becomes difficult without predictive analytics.

Therefore, machine learning based systems are required to predict bike rental demand more efficiently.

4. Proposed System

The proposed system uses Machine Learning and Automated Machine Learning techniques to predict the number of bikes rented daily.

In this system:

1. A dataset containing bike rental information and environmental factors is used.
2. Data preprocessing techniques are applied to clean and prepare the dataset.
3. Machine learning algorithms such as Linear Regression, Random Forest, and XGBoost are trained to build predictive models.
4. H2O AutoML is used to automatically train multiple models and select the best performing model.
5. The system predicts the expected number of bikes rented per day based on environmental and seasonal conditions.

This system helps bike rental companies estimate demand and manage their resources effectively.

5. Implementation Procedure

The implementation of this project is carried out in the following steps:

Step 1: Data Collection

1. Collect the Bike Sharing Dataset.
2. The dataset contains 731 records of daily bike rental counts.
3. It includes environmental and seasonal attributes affecting bike rentals.

Step 2: Data Analysis

1. Analyze the dataset to understand relationships between features.
2. Visualize patterns between environmental conditions and rental counts.

Step 3: Data Preprocessing

1. Handle missing values if present.
2. Encode categorical variables using label encoding or one-hot encoding.
3. Handle outliers and normalize the dataset if necessary.

Step 4: Feature Engineering

1. Select important features affecting bike rentals.
2. Prepare the dataset for training machine learning models.

Step 5: Model Building

1. Train machine learning models such as:
2. Linear Regression
3. Random Forest
4. XGBoost

Step 6: Automated Machine Learning

1. Apply H2O AutoML to automatically train and compare multiple models.
2. H2O AutoML finds the best performing machine learning pipeline.

Step 7: Model Evaluation

1. Evaluate models using metrics such as:
2. Mean Squared Error (MSE)
3. Root Mean Squared Error (RMSE)
4. R² Score

Step 8: Model Comparison

1. Compare traditional machine learning models with AutoML models.
2. Select the best model for bike rental count prediction.

6. Software Requirements

The software used in this project includes:

Operating System

1. Windows / Linux / macOS

Programming Language

1. Python 3.x

Development Environment

1. Jupyter Notebook
2. Google Colab
3. VS Code

Libraries and Frameworks

1. H2O AutoML
2. Scikit-learn
3. NumPy
4. Pandas
5. Matplotlib
6. Seaborn

Web Browser

1. Chrome / Firefox

7. Hardware Requirements

The hardware required for this project includes:

1. Processor: Intel i3 / i5 or higher
2. RAM: Minimum 4 GB (8 GB recommended)
3. Storage: Minimum 128 GB free space
4. System: Laptop / Desktop Computer
5. Internet Connection

8. Advantages of the Project

1. Helps predict bike rental demand in advance.
2. Supports better business planning for rental companies.
3. Uses machine learning techniques for accurate prediction.
4. Automates model development using H2O AutoML.
5. Reduces manual effort in analyzing rental data.
6. Helps optimize bike availability and resource management.
7. Useful for understanding real-world demand forecasting problems.
8. Can be extended to real-time smart transportation systems.