food and beverage data analysis This project analyzes a dataset of food and beverage shops, performing data cleaning, exploratory data analysis (EDA), visualizations, and predictive modeling using machine learning.

---

🗂️ Dataset

• File: Dataset_for_Food_and_Beverages.csv
• Description: Contains records of various shops (e.g., cafes, bakeries, restaurants) with attributes like shop type, ratings, foot traffic, marketing efforts, and yearly sales.

---

🛠️ Tools & Libraries

• Python 3.x
• pandas
• matplotlib
• seaborn
• plotly
• scikit-learn

---

📌 Project Workflow

1️⃣ Data Cleaning

• Sorted data by Shop_Name.
• Dropped unnecessary columns (Shop_Id).
• Reset dataframe index.
• Standardized Shop_Type values.
• Mapped Shop_Website and Marketing to binary (0 = No, 1 = Yes).
• Categorized Rating into Low, Medium, High.

---

2️⃣ Exploratory Data Analysis & Visualizations

✅ Shop Type Distribution

• Pie chart of shop type counts.

✅ Foot Traffic by Shop Type

• Histogram showing average foot traffic for each shop type.

✅ Rating by Shop Type

• Line plot of mean ratings by shop type.

✅ Marketing vs Yearly Sales

• Scatter plot of marketing presence vs sales.

✅ Website vs Yearly Sales

• Scatter plot of website presence vs sales.

✅ Foot Traffic vs Yearly Sales

• Scatter plot analyzing correlation.

✅ Rating vs Yearly Sales

• Line plot of rating and sales relationship.

---

3️⃣ Predictive Modeling

• Target: Yearly_Sales
• Features: Shop type (encoded), website, marketing, foot traffic, rating.
• Model: Random Forest Regressor
• Validation: Train-test split (80-20), cross-validation
• Metrics: R² score, Mean Absolute Error

---

📝 Results Summary

• R² Score: Model performance metric indicating variance explained.
• MAE: Average prediction error in sales values.
• Insights: Shops with marketing, website presence, higher foot traffic, and better ratings generally have higher sales.

---

🚀 How to Run

1️⃣ Clone/download the notebook.
2️⃣ Ensure dependencies are installed:

pip install pandas matplotlib seaborn plotly scikit-learn

3️⃣ Run the notebook:

jupyter notebook ITS69304_SupremKhatri_IndividualAssignment.ipynb

4️⃣ Make sure Dataset_for_Food_and_Beverages.csv is in the same directory.