Advertisement Response Analysis Project

Introduction

This project analyzes advertisement responses using a Django backend and a Vite+React frontend. It includes scripts to load, clean, and transform data, which are executed within Docker containers. Data is stored in a MongoDB database, and the project can be run with or without Docker by adjusting the MongoDB connection strings. There are three pages on the frontend which are home, analyse and predict. Home page displays most impactful plots and analytics. Analyse page allows users to select a combination of attributes to plot and get analytics. Predict page allows users to preidct CTR and Conversion Rates for an advertisement given its attributes using Machine Learning Models.

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Screenshots of Frontend

Home Page

Analysis Page

Predict Page

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Directory Structure

project-root/
├── backend/
├── frontend/
├── data/
├── credentials/
├─ ext_and_load.py
├─ ld_gform_responses.py
├─ clean_and_transform.py 
├─ dockerfile 
├─ .env
├─ .dockerignore
├─ .gitignore
├─ package.json
├─ package-lock.json
├─ requirements.txt
└── docker-compose.yml

How It Works

• Backend (Django): Serves API endpoints for data handling.
• Frontend (Vite+React): Provides a user interface to interact with the project.
• Data Loading and Transformation Scripts:
  • ext_and_load.py: Loads data from an Excel file into MongoDB.
  • ld_gform_responses.py: Loads new entries from Google Forms into MongoDB.
  • clean_and_transform.py: Cleans and transforms data, storing results in MongoDB.

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Project Architecture Overview

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Initialization

Prerequisites

• Python 3.9 or higher installed on your machine.
• Docker and Docker Compose installed on your machine.
• A MongoDB instance, either locally or in a Docker container.
• A google sheets api credentials and google form to collect data from google forms and fetch it using google sheets api

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Steps to Initialize the Project

1. Clone the Repository:

git clone https://github.com/viveksapkal2793/Advertisement-Response-Analysis.git
cd Advertisement-Response-Analysis

2. Set Up Environment Variables:
   Create a .env file in the root directory with the following content:

MONGO_URI=<your_mongodb_uri>
SPREADSHEET_ID=Your google spreadheets id which is collecting the google form responses

3. Run the following command to install python modules for running data loading and cleaning scripts

pip install -r requirements.txt

4. Similarly run the same command in Backend directory

cd Backend
pip install -r requirements.txt

5. Run the following command in Frontend directory to install node modules and dependencies

cd Frontend
npm install

3. Modify MongoDB Connection Strings

Adjust the MongoDB connection strings in the scripts (ext_and_load.py, ld_gform_responses.py, clean_and_transform.py) depending on your environment:

• For Scripts and Backend running in Docker: Use the following connection string:

mongodb://172.17.0.1:27017 # for linux
mongodb://host.docker.internal:27017 # for windows

• For Without Docker: Use your MongoDB instance's URI, typically:

mongodb://127.0.0.1:27017

4. Run Project without docker

• To run project without docker run the following commands in different terminals in Frontend and Backend directories repsectively

cd Frontend
npm run dev

cd Backend
python3 manage.py runserver

5. Build and Run Docker Containers

• To build and run the Docker containers, execute the following command:

docker-compose up --build

• To stop the running containers and delete them, execute the following command:

docker-compose down

6. Access the Frontend when running on localhost at port 5173

Open your browser and navigate to http://localhost:5173.

7. Access the Backend when running on localhost at port 8000

The Django backend will be running at http://localhost:8000.

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Important Notes

• Data Persistence: The tmp directory is mounted as a volume in the Docker containers to persist temporary files.
• Running Scripts: The scripts (ext_and_load.py, ld_gform_responses.py, and clean_and_transform.py) are executed within Docker containers as defined in the docker-compose.yml file.
• Environment Configuration: Ensure that environment variables and MongoDB connection strings are correctly set up before running the project.

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Docker Compose Services

• runner: Runs the run_all.py script once.
• scheduler: Runs the ld_gform_responses.py script every 2 minutes.
• transformer: Runs the clean_and_transform.py script every 5 minutes.
• frontend: Runs the React frontend.
• backend: Runs the Django backend.

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Contributors

• Vivek Sapkal
• Kapil Yadav
• Bhagwan Arsewad