https://github.com/miteshgupta07/sales-forecast-app

A sales forecast application built with Flask that predicts future sales trends using historical data, providing actionable insights and visualizations for better business planning.
https://github.com/miteshgupta07/sales-forecast-app

flask machine-learning python

Last synced: 3 months ago
JSON representation

A sales forecast application built with Flask that predicts future sales trends using historical data, providing actionable insights and visualizations for better business planning.

• Host: GitHub
• URL: https://github.com/miteshgupta07/sales-forecast-app
• Owner: miteshgupta07
• License: mit
• Created: 2024-01-01T06:37:03.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-05-28T14:47:44.000Z (12 months ago)
• Last Synced: 2024-12-28T03:27:45.836Z (4 months ago)
• Topics: flask, machine-learning, python
• Language: Jupyter Notebook
• Homepage:
• Size: 3.3 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

Awesome Lists containing this project

README

        

# Sales Forecast App

The Sales Forecast App is a web application designed to assist businesses in predicting future sales based on historical data. Leveraging machine learning models and data analytics, this app provides insights that can aid decision-making and strategic planning.

## Features

- **Machine Learning Forecasting:** Utilizes advanced machine learning models to predict future sales trends.

- **Interactive Dashboard:** Presents sales forecasts through an interactive and user-friendly dashboard.

- **Customizable Parameters:** Allows users to adjust parameters and scenarios for forecasting.

## Prerequisites

- Python 3.8

- Dependencies List -

  - **Scikit Learn:** A machine learning library in Python.

    - Install: `pip install scikit-learn`

    - Purpose: Utilized for implementing machine learning models and data preprocessing in the project.

  - **Pandas:** A powerful data manipulation and analysis library.

    - Install: `pip install pandas`

    - Purpose: Used for handling and processing structured data.

  - **NumPy:** A fundamental package for scientific computing with Python.

    - Install: `pip install numpy`

    - Purpose: Provides support for large, multi-dimensional arrays and matrices, along with mathematical functions to operate on these arrays.

  - **Seaborn:** A data visualization library based on Matplotlib.

    - Install: `pip install seaborn`

    - Purpose: Enhances the visual appeal of statistical graphics created with Matplotlib.

  - **Matplotlib:** A comprehensive library for creating static, interactive, and animated plots.

    - Install: `pip install matplotlib`

    - Purpose: Essential for generating various types of plots and charts.

  - **SciPy:** A library for mathematics, science, and engineering.

    - Install: `pip install scipy`

    - Purpose: Offers functionality for optimization, signal and image processing, and more.

  - **XGBoost:** An optimized and efficient gradient boosting library.

    - Install: `pip install xgboost`

    - Purpose: Popular for building machine learning models, especially in predictive data analysis.

  - **Pickle:** A module for serializing and deserializing Python objects.

    - Comes with Python standard library, no separate installation required.

    - Purpose: Used for saving and loading machine learning models or other Python objects.

## Acknowledgements

- **Kaggle:** Thanks to Kaggle for providing historical IPL match data.

- **Scikit-learn:** Gratitude to the Scikit-learn community for creating a powerful machine learning library.

- **NumPy:** Heartfelt thanks to the NumPy community for developing a fundamental library that forms the backbone of numerical computing in Python.

- **Pandas:** Special appreciation to the Pandas development team for creating an indispensable tool for data manipulation and analysis, making our project more efficient and effective.

- **Matplotlib:** A big shout-out to the Matplotlib developers for providing an extensive and flexible plotting library, adding a visual dimension to our data exploration and presentation.

- **Seaborn:** We express our gratitude to the Seaborn community for enhancing our data visualization capabilities with a high-level interface to Matplotlib, making our plots more aesthetically pleasing and informative.

- **SciPy:** Thanks to the SciPy project for delivering a powerful library for scientific and technical computing in Python, contributing significantly to the success of our project.

- **XGBoost:** A special thank you to the XGBoost community for creating an efficient and scalable gradient boosting library, boosting the performance of our machine learning models.

  

## Contact

Email : [email protected]

Linkedin : https://www.linkedin.com/in/mitesh-gupta/

Twitter : https://twitter.com/mg_mitesh