https://github.com/anjupriya-v/sales-prediction-analysis
Developed using Angular.js, Flask and MongoDB
https://github.com/anjupriya-v/sales-prediction-analysis
angularjs emailjs flask mongodb
Last synced: over 1 year ago
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Developed using Angular.js, Flask and MongoDB
- Host: GitHub
- URL: https://github.com/anjupriya-v/sales-prediction-analysis
- Owner: anjupriya-v
- Created: 2022-11-20T12:44:20.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-03-30T08:50:20.000Z (over 3 years ago)
- Last Synced: 2025-03-29T22:23:39.929Z (over 1 year ago)
- Topics: angularjs, emailjs, flask, mongodb
- Language: TypeScript
- Homepage:
- Size: 3.14 MB
- Stars: 8
- Watchers: 3
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Sales Prediction Analysis
## Tech Stacks Used:
- Angular.js
- Python (Flask)
- MongoDB
- Machine learning model (SARIMAX Model)
- Email.js
## Demo Video 👇
https://user-images.githubusercontent.com/84177086/211177742-99782e71-a60e-4591-9aa6-98ce188b4723.mp4
## :point_down: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`
- 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`
- 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`
- A guide to Email.js
https://www.youtube.com/watch?v=dgcYOm8n8ME
### Email.js Content template screenshot 👇
### Email.js Auto reply template screenshot 👇
- 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](https://github.com/anjupriya-v/sales-prediction-analysis/files/10367624/sales_data_sample.csv)