Skip to content

This BMW Vehicle Sales Analysis project leverages Python and Power BI to clean data, conduct exploratory analysis, calculate KPIs, and create interactive dashboards. We’ll uncover trends in sales, customer demographics, and market performance, empowering stakeholders with actionable insights to enhance strategic decision-making.

License

Notifications You must be signed in to change notification settings

B0m0/BMW-Vehicle-Sales-Analysis

Repository files navigation

BMW-Vehicle-Sales-Analysis

Project Overview:

This project provides a comprehensive analysis of BMW vehicle sales using Python and Power BI. The objective is to clean, analyze, and visualize sales data to generate actionable insights that enhance strategic decision-making.

Project Goals:

End-to-End Analysis: Conduct a thorough analysis of BMW vehicle sales data. Data Cleaning: Clean and preprocess raw data for accurate analysis. Exploratory Data Analysis (EDA): Uncover patterns and trends in the dataset. KPI Development: Calculate key performance indicators relevant to sales performance. Interactive Dashboards: Create visualizations in Power BI to present findings. Actionable Insights: Generate recommendations based on the analysis.

Tools:

Python Libraries: Pandas: For data manipulation and cleaning. NumPy: For numerical operations. Matplotlib: For static visualizations. Seaborn: For advanced visualizations. Power BI: For creating interactive dashboards. CSV Datasets: Used for analysis.

Business Objectives:

Identify Top-Selling Models and Regions: Determine which BMW models and regions drive sales. Analyze Sales Trends and Pricing Patterns: Understand seasonal trends and pricing strategies. Provide Insights for Strategic Decision-Making: Facilitate data-driven decisions for sales strategies.

Dataset Preparation and Cleaning:

Handle missing values and outliers. Standardize data formats (dates, currency). Remove duplicates to enhance data quality.

Exploratory Data Analysis (EDA):

Visualize relationships between variables (e.g., sales vs. price, sales by model). Identify sales trends and customer demographics using various plots.

Key Performance Indicators (KPIs):

Calculate essential KPIs, including: Total sales revenue Average transaction value Year-over-year growth rates Market share by model and region Data Visualization and Dashboard in Power BI Develop interactive dashboards featuring: Filter options for dynamic analysis. Visual storytelling to highlight key findings. Clear metrics to monitor and evaluate performance.

Conclusion: This project delivers a detailed analysis of BMW vehicle sales, equipping stakeholders with insight-driven recommendations to enhance their strategies.

Author Younes Boumouh – Data Analyst

About

This BMW Vehicle Sales Analysis project leverages Python and Power BI to clean data, conduct exploratory analysis, calculate KPIs, and create interactive dashboards. We’ll uncover trends in sales, customer demographics, and market performance, empowering stakeholders with actionable insights to enhance strategic decision-making.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published