https://github.com/akarshankapoor7/comprehensive-ml-project-on-sales-forcasting-using-facebook-prophet-

A comprehensive machine learning project using Facebook's Prophet to forecast future sales. The model utilized historical data and effectively accounted for various factors, including seasonality effects, demand fluctuations, holiday impacts, promotional activities, and competitive influences.
https://github.com/akarshankapoor7/comprehensive-ml-project-on-sales-forcasting-using-facebook-prophet-

data-science prophet-facebook-model salesanalysis statistics timeseries-forecasting

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A comprehensive machine learning project using Facebook's Prophet to forecast future sales. The model utilized historical data and effectively accounted for various factors, including seasonality effects, demand fluctuations, holiday impacts, promotional activities, and competitive influences.

• Host: GitHub
• URL: https://github.com/akarshankapoor7/comprehensive-ml-project-on-sales-forcasting-using-facebook-prophet-
• Owner: akarshankapoor7
• Created: 2024-05-27T09:58:10.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-05-27T10:05:37.000Z (8 months ago)
• Last Synced: 2024-06-06T15:29:42.799Z (7 months ago)
• Topics: data-science, prophet-facebook-model, salesanalysis, statistics, timeseries-forecasting
• Language: Jupyter Notebook
• Homepage:
• Size: 7.24 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Comprehensive-ML-Project-on-Sales-Forcasting-using-Facebook-Prophet-

A comprehensive machine learning project using Facebook's Prophet to forecast future sales. The model utilized historical data and effectively accounted for various factors, including seasonality effects, demand fluctuations, holiday impacts, promotional activities, and competitive influences.

Follow the steps in the notebook to understand the requirements:

Why to use Facebook Prophet?? Not ARIMA and any other ML algorithm!!

Answer: Its uses less input parameters but still gives good results.

Advantages of Facebook Prophet

User-Friendly: Easy to use for various expertise levels.

Seasonality Detection: Automatically adjusts for yearly, weekly, and daily patterns.

Handles Missing Data: Robust against missing data and outliers.

Holiday Effects: Accounts for holidays and special events.

Flexible: Allows custom seasonalities and additional regressors.

Scalable: Efficiently handles large datasets.

Fast: Quickly generates forecasts.

Interpretable: Provides clear, understandable parameters.

Open Source: Freely available with community support.

Integrates Easily: Works well with Python and R.