This repository contains code for performing data analysis using Elasticsearch. The code includes functions to query Elasticsearch for aggregated data and statistical metrics, particularly focusing on analyzing clicks and conversions data.
The code provided in this repository is designed to perform analysis on data stored in Elasticsearch. Specifically, it focuses on analyzing clicks and conversions data to derive insights such as correlation between clicks and conversions, as well as various statistical metrics related to clicks and conversions.
To use this code, you'll need the following prerequisites:
- Elasticsearch instance running locally or accessible via URL
- Python installed on your system
- Required Python libraries (
elasticsearch)
-
Clone this repository to your local machine:
git clone https://github.com/1varma/DE-Case-Study.git -
Install the required Python libraries:
pip install elasticsearch
-
Ensure that your Elasticsearch instance is running and accessible.
-
Modify the Elasticsearch configuration in the code (
es = Elasticsearch("http://localhost:9200")) if your Elasticsearch instance is running on a different URL. -
Run the Python script to perform data analysis:
python analysis_script.py -
The script will connect to Elasticsearch, perform the analysis, and print the results to the console.
This project is licensed under the MIT License.