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🍦 When the Sun Rises, Sales Soar: A Predictive Analysis

We all know it by intuition: hot days and ice cream are a perfect match. But what if we could go beyond intuition? What if we could use data to create a true "crystal ball" capable of predicting exactly how much an ice cream shop's revenue could increase with every degree the temperature rises?

This project does just that. We dove into a sales dataset to turn common sense into a precise mathematical model. Using Linear Regression, we deciphered the relationship between temperature and revenue, creating a tool to forecast sales success before the sun even hits its peak.

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🔮 The Interactive Oracle: Explore the Data

To make this analysis accessible to everyone, we built a visual oracle in Looker Studio. On this interactive dashboard, you can adjust the temperature with a slider and watch the revenue forecast respond in real-time. It's not just a chart; it's your chance to predict the future of sales.

>> Consult the Interactive Oracle Here <<

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📈 The Revelation: The Magic Formula for Sales

Our analysis confirmed what we suspected: there is a strong, clear linear connection between temperature and revenue. Using the Scikit-learn library, we taught a model to "learn" this relationship and translate it into a powerful predictive formula.

The Prophecy's Equation

The model revealed the following equation to predict revenue:

$$ \text{Revenue} = 21.44 \times \text{Temperature} + 44.27 $$

• Coefficient (m): 21.44
• Intercept (c): 44.27

What does this mean? The revelation is simple and powerful: the model predicts that for every 1°C increase in temperature, the ice cream shop's revenue is expected to increase by approximately $21.44.

Testing the Prophecy

To validate our model, we made a prediction:

• For a day with a temperature of 25°C, the predicted revenue is $580.31.

Visualizing the Connection

The scatter plot below leaves no doubt. The red line, representing our model's predictions, fits the real-world data perfectly, visually confirming the strong positive correlation.

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📜 The Ancient Scrolls: The Dataset

Our journey wouldn't have been possible without the manuscripts containing the secrets of past sales. We used the "Ice Cream Sales Dataset," originally sourced from Kaggle.

• Original Source: Ice Cream Sales Dataset on Kaggle
• Author: Sakshi Satre

The df_final_ice_cream.csv file in this repository is the cleaned and prepared version, containing the two columns essential for our magic: Temperature (°C) and Revenue ($).

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🛠️ The Alchemist's Cauldron: Tools & Libraries

To turn raw data into predictive gold, we used the following tools:

• Language: Python
• Magic Libraries:
  • Pandas (for organizing and manipulating data)
  • Matplotlib & Seaborn (for visualizations and charts)
  • Scikit-learn (for conjuring the Linear Regression model)
• Spellcasting Environment: Jupyter Notebook
• BI Tool: Looker Studio (for our interactive oracle)

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🚀 Recreate the Magic: How to Run the Project

Follow these steps to run the analysis and make your own predictions:

1. Clone the repository:

   git clone [https://github.com/OYanEnrique/ice-cream-revenue-prediction.git](https://github.com/OYanEnrique/ice-cream-revenue-prediction.git)
   cd ice-cream-revenue-prediction

2. Install the necessary ingredients:

   pip install pandas matplotlib seaborn scikit-learn jupyterlab

3. Start the lab:

   jupyter lab

4. Open and run the ice_cream_data.ipynb notebook to witness the full analysis.

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👨‍💻 O Arquiteto da Análise

• Yan Enrique
• LinkedIn: https://www.linkedin.com/in/yanenrique/
• GitHub: https://github.com/OYanEnrique
• Landing page: https://yanenrique.carrd.co

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