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Sales Prediction Analysis

Tech Stacks Used:

  • Angular.js
  • Python (Flask)
  • MongoDB
  • Machine learning model (SARIMAX Model)
  • Email.js

Demo Video 👇

Sales.Prediction.Analysis.mp4

👇Steps to initialize the project:

  • Clone the repository
$ git clone https://github.com/anjupriya-v/sales-prediction-analysis.git

  • Redirect to the cloned repo directory

  • Open up the terminal and redirect to client directory.

  • Install the dependencies

npm install

  • create the mongoDB account in the mongoDB atlas and create the cluster

  • Note: A guide to create the mongoDB account and mongoDB URL https://www.youtube.com/watch?v=oVHQXwkdS6w

  • click on connect and select connect your application.

  • select python as Driver and select version as per the version that you have installed in your PC and get the MONGO DB url from it

  • Then create the database user by clicking the database access from the mongoDB atlas menu and click on Add New Database User. Then provide the username and password and set the built-in role as read and write to any database and click on Add user.

  • Replace the DB user name and password in the MongoDB URL.

  • Paste the MongoDB URL in app.py file /server/app.py

image

  • To create the database, click the database from the mongoDB atlas menu. Then click Browse Collections and click Create Database

  • Note: The database should be named as SalesPrediction and the collection should be named as account .

  • Create the Secret key typing the following command in the terminal.

python -c 'import os; print(os.urandom(24))';
  • Secret key will be generated and paste it in app.py file /server/app.py

image

  • Use email.js for sending the contact form data to your email inbox

  • Create the email.js account in https://www.emailjs.com/ and paste the service id, template id and user id in /client/src/app/components/contact-us/contact-us.component.ts

image

Email.js Content template screenshot 👇

image

Email.js Auto reply template screenshot 👇

image

  • For starting the client, type the following in the command prompt
cd client 

ng serve -o
  • For starting the server, type the following in the new command prompt
cd server

flask --app app --debug run
  • Note: This Application will be worked only for the following single dataset.

sales_data_sample.csv