Ice Cream Truck Revenue Prediction with Seasonality Integration

This project is an enhanced version of a machine learning model designed to predict the daily revenue of an ice cream truck business. By integrating seasonal trends such as monthly cycles this model aims to improve prediction accuracy and provide more actionable insights.

Overview

Ice cream sales are highly influenced by factors such as temperature and seasonality. This project builds upon an initial revenue prediction model by incorporating these seasonal patterns, allowing for more precise forecasts. The project utilizes Python and its data science libraries, including Pandas, NumPy, Scikit-learn, and Matplotlib, for data processing, modeling, and visualization.

Features

• Seasonality Integration: The model includes features that capture seasonal effects, such as monthly and weekly cycles, to account for variations in sales throughout the year.
• Advanced Data Visualization: The project includes visualizations that illustrate both the raw data and the model's predictions, highlighting the impact of seasonal trends.
• Model Comparison: The performance of the enhanced model is compared against the original, non-seasonal model to demonstrate improvements.

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 ExpandedCapstone.ipynb

Usage

1. Data Preparation:

   • Load and preprocess the historical sales and temperature data.
   • Extract date-related features.

2. Model Training:

   • Train the machine learning model using the processed data.
   • Compare performance with the original model to assess the impact of seasonal integration.

3. Visualization:

   • Generate visualizations to explore sales trends, seasonal effects, and model predictions.

Results

The enhanced model with seasonality integration provides more accurate predictions, particularly in periods where seasonal effects are strong, such as peak summer months or holidays.

Contact

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