Pedro

README_Zina.md

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+# Project overview
+This project analyzes **global Michelin-starred restaurants** (1, 2, and 3 stars) to understand patterns in:
+- Cuisine type vs Michelin star level
+- Geographic distribution across regions and major cities
+- Price trends by star rating
+- Public reviews and their alignment with Michelin ratings
+
+The datasets were concatenated, cleaned, and enriched with:
+- Average price (`price_mean`)
+- Review rating and count metrics
+
+# Installation
+
+1. **Clone the repository**:
+
+```bash
+git clone https://github.com/ArrimachNasser/first_project.git
+
+```
+
+2. **Install UV**
+
+If you're a MacOS/Linux user type:
+
+```bash
+curl -LsSf https://astral.sh/uv/install.sh | sh
+```
+
+If you're a Windows user open an Anaconda Powershell Prompt and type :
+
+```bash
+powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
+```
+
+3. **Create an environment**
+
+```bash
+uv venv 
+```
+
+3. **Activate the environment**
+
+If you're a MacOS/Linux user type (if you're using a bash shell):
+
+```bash
+source ./venv/bin/activate
+```
+
+If you're a MacOS/Linux user type (if you're using a csh/tcsh shell):
+
+```bash
+source ./venv/bin/activate.csh
+```
+
+If you're a Windows user type:
+
+```bash
+.\venv\Scripts\activate
+```
+
+4. **Install dependencies**:
+
+```bash
+uv pip install -r requirements.txt
+```
+
+# Questions 
+1. Do certain cuisines have a higher probability of receiving 3 stars?
+2. Which regions host the most starred restaurants?
+3. How does the average price vary between 1, 2, and 3 stars?
+4. Do public reviews align with Michelin star ratings?
+
+# Dataset 
+We used three primary datasets:
+- `one-star-michelin-restaurants.csv`
+- `two-stars-michelin-restaurants.csv`
+- `three-stars-michelin-restaurants.csv`
+
+**Additional sources / enrichment**:
+- MICHELIN Guide for The restaurants' price ranges index
+- Online Review information using API 
+
+## Main dataset issues
+- Missing values in `price` column for some restaurants
+- Inconsistent cuisine naming (e.g., "French cuisine" vs "French")
+- Some restaurants incorrectly had five dollar symbols ($$$
+- Special characters in restaurant names
+
+## Solutions for the dataset issues
+- Imputed missing prices with the **median price** per star level
+- Normalized cuisine names by lowercase, stripping spaces, and mapping variants to a single category
+- Removed duplicates based on `name + city`
+- Created a `major_city` flag using top city populations
+- Special Characters: Used 'unidecode' to clean restaurant names of accents and special characters
+- Inconsistent cuisine naming: Reduced over 100 cuisine types into ~10 coherent groups
+- Geographic Organization: Grouped and ordered suburbs by city and region for clarity
+- Symbol Inconsistencies: Fixed entries with too many $ symbols, removed extra spaces and hidden characters
+- Handeling Missing Prices: Filled missing values using per star category.
+
+# Conclusions
+- New York, Hong Kong, and San Francisco are major culinary and cultural hubs that attract Michelin-star.
+- New York leads in 1-star and 2-star Michelin restaurants, offering a diverse array of high-quality dining.
+- Hong Kong, excels in 3-star Michelin establishments, indicating a focus on elite dining experiences.
+- Among 3-star Michelin restaurants, the dominant cuisines are contemporary, French, and Chinese.
+- Average price increases with star level, most 3-star restaurants tend to be the most expensive
+- Among all cuisines, Austrian cuisine is the most expensive on average.
+
+# Next steps
+- Predict Michelin star level using machine learning
+- Include chef profiles for deeper insights
+- Analyze yearly trends using the `year` column

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