🍕 Pizza Price Predictor using Machine Learning.

This is an interactive application developed with Streamlit, Scikit-learn and Matplotlib libraries and .csv files. The objective is predict the price of a pizza based on its diameter and calculate the cost of the required mozzarella cheese using linear regression.

📝 Features

• Pizza price prediction: Based on the diameter (in centimeters).
• Mozzarella cost calculation: Based on the weight of mozzarella (in grams).
• Total order value calculation: Sum of the pizza price and the mozzarella cost.

Requirements

Make sure you have the following installed on your machine:

• Python 3.8 or higher
• Libraries listed in requirements.txt

🚀 Installation

1. Clone the repository or download the files.

   git clone <repository-url>
   cd <repository-name>

2. Install the dependencies.

   pip install -r requirements.txt

3. Ensure that the pizzas.csv and mozzarella.csv files are in the same directory as the code.

📊 Data Structure

pizzas.csv

The pizzas.csv file should contain the following columns:

• diameter: Pizza diameter in centimeters.
• price: Corresponding price in currency.

mozzarella.csv

The mozzarella.csv file should contain the following columns:

• quantity: Weight of mozzarella in grams.
• value: Corresponding price in currency.

🛞 Usage

1. Run the Streamlit application.

   streamlit run <file-name>.py

2. Enter the following data into the application:

   • Pizza diameter (in cm): To calculate the pizza price.
   • Mozzarella weight (in grams): To calculate the cheese cost.

3. View the estimated pizza price, cheese cost, and total order value.

Main Code

import streamlit as st
import pandas as pd
from sklearn.linear_model import LinearRegression

df = pd.read_csv("pizzas.csv")
model = LinearRegression()
x = df[["diameter"]]
y = df[["price"]]
model.fit(x, y)

df_1 = pd.read_csv("mozzarella.csv")
model_1 = LinearRegression()
x = df_1[["quantity"]]
y = df_1[["value"]]
model_1.fit(x, y)

def calculate_pizza_price(diameter, model):

    if diameter:
        return model.predict([[diameter]])[0][0]
    else:
        return 0.0

#see the full code in this repository! 

⚠️ Notes

• Ensure that the CSV files contain enough data to train the linear regression models.
• The application uses the scikit-learn library to build and utilize the predictive models.

🗒️ License

This project is licensed under the MIT License. See the LICENSE file for more information.