Music Store Database Analysis

Overview

This project delves into the Music_store_database to uncover valuable patterns and insights related to customer preferences, sales trends, and the music industry as a whole.

• Customers
• Invoices
• Albums
• Tracks

The project aims to:

• Inform business strategies: Provide actionable data to enhance marketing campaigns, inventory decisions, and pricing models.
• Customer understanding: Reveal customer preferences and behavior patterns for tailored outreach and improved customer experiences.

Features

Database Schema

A clear visual representation for understanding the database structure and relationships between tables:

Description

This diagram illustrates the relationships between the tables in the music store database:

• Customers: Stores information about customers, such as name, address, and purchase history.
• Employees: (Optional) Stores information about employees.
• Genres: Lists the various music genres for categorization.
• Media Types: Lists the different media formats (CD, vinyl, digital, etc.).
• Albums: Stores information about albums, such as artist, title, genre, price, release date, and media type.
• Tracks: Stores information about individual tracks on albums, such as title, duration, composer, and album association.
• Invoices: Stores transaction summaries, including total amount, date, and customer information.
• Invoice Lines: Links invoices to the specific tracks purchased in each transaction.
• Playlists: (Optional) Stores user-created playlists.
• Playlist Tracks: (Optional) Links playlists to the songs included in them.

Table of Contents

• About the Dataset
• Project Objectives
• Installation and Usage
• Analysis Approach
  • Data Wrangling
  • Feature Engineering
  • Exploratory Data Analysis (EDA)

About the Dataset

This section will provide a detailed summary of the included CSV files:

• albums.csv: Attributes like artist, album title, genre, price, release date, etc.
• customers.csv: Customer demographics, purchase history, and other relevant data points.
• employees.csv: Employee details (may or may not be directly relevant to this analysis).
• genre.csv: List of music genres for categorization.
• invoice.csv: Transaction summary, including total amount, date, and customer information.
• invoice_line.csv: Itemized transaction details.
• media_type.csv: Media formats of music (CD, vinyl, digital, etc.).
• playlist.csv: Potentially user-created playlists.
• playlist_track.csv: Songs included in playlists.
• track.csv: Individual track listing, duration, album association.

Project Objectives

Clearly state the primary goals of your analysis, such as:

• Understanding customer purchase patterns.
• Optimizing marketing initiatives based on customer demographics and music preferences.
• Identifying growth opportunities for different musical genres.
• Evaluating sales performance trends.

Analysis Approach

Outline the chosen methodology:

• Data Wrangling: Detection and treatment of missing values, data cleaning, and transformation for analysis.
• Feature Engineering: Crafting relevant features from existing data (e.g., time of day, day of the week, month).
• Exploratory Data Analysis (EDA): Employ statistical summaries and visualizations to reveal patterns and trends.