Internship Project for B2E

Description/process:

This project uses a Dataset of Expenses for advertising and sales. In this i have created a webpage to upload the dataset where we are fetching it and after preprocessing, the clean data is uploaded(POST) to the API created by the company. Then i fetched the uploaded data to perform Linear regression on Sales and TV (expense for advertising). The reason to select TV is because it is more correlated to the sales. A scatter plot is shown to show the difference between the Actual Sales Vs Pedicted Sales using my regression model. Finally a webpage to showcase the plot and table of Actual vs Predicted.

Note: This is only for the advertising csv used in this project as the API creation and prediction process is done according to the specific dataset

Steps to run it Locally:

Open the folder in an code editor for better experience

Step 1 - Clone the repository:

https://github.com/MKisKrazy/internship-project-B2E-

Step 2 - Install the required modules/libraries

pip install -r requirements.txt

Step 3 - Run the server:

Navigate to the myproject folder using

cd myproject

Run the server using the command:

python manage.py runserver

Step 4- To view the webpage:

http://127.0.0.1:8000/

Go to this url after starting the server.

Click the To upload the advertsing expenses dataset button to nagivate to next page

Choose the 'advertising.csv' file (which i have provided in the repository) and click upload buutton to upload.

It will take sometime to upload and you will navigated to next page and you will see response code: 200 and the preview of the dataset which indicated successful upload to API

To start the prediction process click on Start Prediction button

It will take some time to do the prediction process and you will be nagivated to new page where u will see the model's evaluation metrics,preprocessing analysis,correlation plots and final prediction plot

Click on View prediction to see a comparison on Actual Vs Predicted values of the sales data

To view those different pages directly:

Go to this url to see prediction output with plot directly with the data from API

http://127.0.0.1:8000/start_prediction

Go to this url to see Actual vs Predicted data

http://127.0.0.1:8000/predicted_data

Dataset in provided in the repository