New Beverage Product Analysis

Intro

This project analyzes consumer preferences and market trends for launching a new beverage product. All data used is simulated and does not reflect anything from the companies I have worked for.

Tools Used

• Python (pandas, matplotlib)
• Jupyter Notebook

Steps

1. Data Cleaning
2. Exploratory Data Analysis (EDA)
3. Visualization of Key Insights

Results

• Identified key demographic segments.
• Highlighted trends in purchasing behavior.

Dataset

The raw dataset is located in the data/ folder as raw_data.csv.
It contains 1,000 rows and 11 columns with the following features:

• Region: Geographic location (e.g., North, South).
• Channel: Sales channels like General Trade or Modern Trade.
• Product Category: Beverage category (Water, Tea, etc.).
• Product Name: Specific product names.
• PC (Product Cases): Number of product cases sold.
• Units per Case: Units per product case.
• Product Volume (L): Volume per product.
• Sales Value (VND): Total sales in VND.
• Time Period: Sales timeframe.
• UC (Unit Cases): Calculated unit cases.
• Price per UC (VND): Price per unit case.

Notes:

• There are some missing values in the dataset, which will be addressed during the analysis phase.
• This dataset serves as the starting point for cleaning, exploration, and analysis.

Setup

To run this project:

pip install -r requirements.txt
jupyter notebook