Zomato-Data-Analysis

Food Delivery App Data Analysis using Python and Integrating the data to the target database(Oracle)

Description

This project aims to perform data analysis on Zomato restaurant data and create a pipeline for storing the cleaned data in an Oracle Database. The pipeline is designed to update the target file in the database whenever there is a modification in the source file.

Files in the Repository

The repository contains the following files:

1. zomato.csv

   • This file contains the uncleaned data extracted from Zomato.

2. cleaning_script

   • This script is responsible for cleaning and preprocessing the Zomato data.

3. pipeline_Auto

   • The dynamic pipeline script that stores the cleaned data into the Oracle Database. Users need to provide necessary details through the command line for execution. Example:

   python pipeline_Auto.py <oracle_user> <oracle_password> <oracle_host> <oracle_port> <oracle_sid> <csv_file_path>

4. runQuery.py

   • This script reads a query written in a .txt file and outputs the result in a .csv file. Similar to the pipeline script, it is designed to be dynamic and accepts input through the command line. Example:

   python runQuery.py <oracle_user> <oracle_password> <oracle_host> <oracle_port> <oracle_sid> <query_file_path>

Installation

To use this project, you need the following dependencies:

• Python 3.x: If you don't have Python 3.x installed, you can download it from the Python official website.

• Oracle Database: Ensure you have access to an Oracle Database where you can store the cleaned data.

• Python packages: You'll need to install the following Python packages:

  • pandas: Used for data manipulation and analysis.
  • cx_Oracle: Required for database connectivity with Oracle Database.

To install the Python packages, you can use the following commands:

# Install pandas
pip install pandas

# Install cx_Oracle
pip install cx_Oracle

Usage

1. Cleaning Script:

• Execute the cleaning script on the zomato.csv file to prepare the data for further analysis.

2. Pipeline Script:

• Run the pipeline script, providing Oracle database credentials and the file paths as command-line arguments to store the cleaned data.

Example:

python pipeline_Auto.py user pass host port sid path/to/zomato.csv

3. Query Execution:

• Use runQuery.py to execute queries on the Oracle database. Provide the query file path and the desired output CSV file path.

Example:

python runQuery.py user pass host port sid path/to/query.txt

Contributing

If you would like to contribute to this project, please follow these steps:

• Fork the repository.
• Create a new branch for your feature or bug fix.
• Make changes and commit them.
• Push to your fork and submit a pull request.