Sales Analysis Project

Overview

This project involves analyzing sales data to gain insights into sales trends, performance metrics, and product categories. The analysis includes data cleaning, exploratory data analysis (EDA), sales trend analysis, profit dependency analysis, and ABC analysis. The goal is to provide actionable insights that can inform business decisions and strategies.

Project Structure

• data/: Contains raw and processed data files.
  • raw/: Raw datasets used for analysis.
    • events.csv: Contains information about sales transactions.
    • products.csv: Contains information about product types.
    • countries.csv: Contains information about countries and territories.
  • processed/: Processed datasets ready for analysis.
    • sales_data.csv: Combined and cleaned sales data.
• reports/: Contains Jupyter Notebooks for analysis.
  • sales_data_exploration_insights.ipynb: Main notebook containing data cleaning, EDA, and analysis.
• config.json: Configuration file for file paths.
• README.md: This file.

Installation

1. Clone the repository:

   git clone https://github.com/shliakhovai/sales-analysis-project.git cd sales-analysis-project

2. Install dependencies:

   Ensure you have Python installed. Install the required libraries using pip:

   pip install pandas matplotlib seaborn

Usage

1. Data Preparation:

   Ensure your raw data files are placed in the data/raw/ directory. The notebook will automatically read from these files.

2. Running the Analysis:

   Open the Jupyter Notebook:

   jupyter notebook

   Navigate to reports/sales_data_exploration_insights.ipynb and execute the cells to perform the analysis.

Notebook Contents

• Data Understanding: Load and explore the datasets. Perform initial data inspection and understand the structure.
• Data Cleaning: Clean the data, handle missing values, and prepare the dataset for analysis.
• Exploratory Data Analysis (EDA): Analyze sales performance metrics, trends, and visualize data.
• Sales Trend Analysis: Analyze sales trends over time and visualize the results.
• Profit Dependency Analysis: Explore the relationship between profit and shipping time.
• Sales Analysis by Day of the Week: Analyze sales performance by day of the week.
• ABC Analysis: Perform ABC analysis to categorize products based on sales.