EV Toolbox Regression

Overview

The EV Toolbox Regression project provides a streamlined solution for performing regression analysis on datasets stored in Box. The workflow involves downloading datasets, combining them, running regression analysis, and uploading results back to Box, ensuring an end-to-end solution for data handling and analysis.

This tool is built using Python, leveraging libraries such as pandas and scikit-learn for data manipulation and machine learning, and the Box SDK for seamless integration with Box.

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Features

• Box Integration: Securely connect to Box to download and upload files.
• Dataset Combination: Automatically combine datasets using a specified key column.
• Regression Analysis: Perform linear regression and output:
  • Regression coefficients in a single-row CSV format.
  • R² score, Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) in the terminal.
• File Overwriting: Automatically replaces existing files in Box when uploading new results.

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Requirements

• Python: Version 3.8 or later.
• Libraries:
  • pandas
  • scikit-learn
  • boxsdk
  • dotenv
  • requests
• Box Account: Required for file storage and authentication.

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Setup Instructions

1. Clone the Repository

git clone https://github.com/your-repo/ev-toolbox-regression.git
cd ev-toolbox-regression

2. Install Dependencies

Create a virtual environment and install the required Python packages:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

3. Set Up .env File

Create a .env file in the project root with the following content:

# Box Credentials
BOX_CLIENT_ID=your_client_id
BOX_CLIENT_SECRET=your_client_secret
BOX_ACCESS_TOKEN=your_access_token
BOX_REFRESH_TOKEN=your_refresh_token
BOX_REDIRECT_URI=http://localhost

# File IDs
BOX_FILE_IDS=your_file_id_1,your_file_id_2

# Output Folder ID
BOX_OUTPUT_FOLDER_ID=your_output_folder_id

# Regression Parameters
TARGET_COLUMN=Y_house_price_of_unit_area
FEATURE_COLUMNS=X1_transaction_date,X2_house_age,X3_distance_to_MRT,X4_number_of_convenience_stores,X5_latitude,X6_longitude

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Usage

1. Run the Workflow

Run the main.py script to execute the end-to-end process:

python main.py

The workflow performs the following steps: 1. Authenticate with Box.

2.	Download datasets specified in BOX_FILE_IDS.

3.	Combine the datasets using the No column as the key.

4.	Perform regression analysis:

    • Outputs regression coefficients as a CSV (**'reg_coefficients.csv'**).

    • Displays model statistics (R², MSE, RMSE) in the terminal.

5.	Upload the results back to the Box folder specified in **'BOX_OUTPUT_FOLDER_ID'**.

2. Reauthorize the Application

If tokens expire, the script will prompt you to reauthorize the application:

1.	Follow the provided authorization URL.

2.  Log in your Box account, click "Grant Access".

3.	Paste the received authorization code (at the end of link of the redicted page, after "**'code='**") into the terminal.

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File Descriptions

• main.py: Orchestrates the entire workflow, including authentication, file handling, dataset combination, regression, and result upload.
• data_utils.py: Contains functions for combining datasets and handling file downloads/uploads with Box.
• regression_utils.py: Handles regression analysis, calculates statistics, and saves coefficients to a CSV file.
• token_manager.py: Manages Box API authentication, token refresh, and reauthorization.
• .env: Configuration file for Box credentials, file IDs, output folder ID, and regression parameters.

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Example Output

Terminal Output

Authenticated User: John Doe (ID: 12345678901)
Datasets combined successfully.
Regression Statistics:
R² Score: 0.5824
Mean Squared Error: 77.1317
Root Mean Squared Error: 8.7825
Regression coefficients saved to data/reg_coefficients.csv.
File uploaded successfully: reg_coefficients.csv

reg_coefficients.csv

X1_transaction_date	X2_house_age	X3_distance_to_MRT	X4_number_of_convenience_stores	X5_latitude	X6_longitude	Intercept
5.146227462979936	-0.269695448	-0.004487461	1.133276905	225.472976	-12.423601	-14437.101

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Troubleshooting

• ValueError: One or more specified columns are missing in the combined dataset.

  • Ensure all specified columns in the .env file exist in the combined dataset.
  • Verify the input datasets contain the required columns and are properly combined.

• BoxAPIException: item_name_in_use

  • This error occurs when attempting to upload a file that already exists in the Box folder.
  • The script is configured to overwrite files in Box. If the issue persists, verify that the upload_file_to_box function is correctly replacing files.

• Authentication Error: Refresh token has expired

  • Reauthorize the application by following the URL provided in the terminal and entering the new authorization code.

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License

This project is licensed under the MIT License.