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Ice Cream Truck Revenue Prediction

This project is a machine learning model designed to predict the daily revenue of an ice cream truck business based solely on outside temperature. The model employs linear regression to establish a relationship between temperature and sales, enabling the business to forecast revenue and optimize operations.

Overview

Ice cream sales are strongly influenced by weather conditions, particularly temperature. This project focuses on using historical temperature and revenue data to build a predictive model. By utilizing Python and its data science libraries, including Pandas, NumPy, Scikit-learn, and Matplotlib, the project demonstrates the power of linear regression in forecasting daily revenue based on temperature.

Features

  • Temperature-Based Prediction: The model uses historical temperature data to predict daily ice cream sales, allowing for simple yet effective revenue forecasting.
  • Linear Regression Model: A straightforward linear regression approach is applied to model the relationship between temperature and revenue.
  • Basic Data Visualization: The project includes visualizations to explore the relationship between temperature and revenue, as well as to present the model's predictions.

Installation

To run this project locally, follow these steps:

  1. Clone the Repository:

    git clone [repository-url]
cd [repository-folder]

  2. Install Dependencies: Download and install Anaconda

  3. Run the Jupyter Notebook: Launch the Jupyter Notebook to explore the code and results:

    jupyter notebook IceCreamRevenuePrediction.ipynb

Usage

  1. Data Preparation:

    • Load and preprocess the historical sales and temperature data.
    • Explore the data to understand the relationship between temperature and revenue.

  2. Model Training:

    • Train the linear regression model using the processed data.
    • Evaluate the model's performance to ensure accurate revenue predictions.

  3. Visualization:

    • Generate visualizations to examine the temperature-revenue relationship and the model's predictions.

Results

The linear regression model successfully predicts daily ice cream truck revenue based on temperature, providing the business with a reliable tool for revenue forecasting.

Contact

For any questions or suggestions, please feel free to reach out via [email protected] or connect with me on LinkedIn.