/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.

Primary LanguageJupyter NotebookMIT LicenseMIT

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 : miteshgupta2711@gmail.com

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

Twitter : https://twitter.com/mg_mitesh