Welcome! This repository houses a comprehensive data analysis project based on the RevoU data analytics mini-course. In this case study, I delve into the dynamic world of vehicle sales, aiming to extract insights and answers from a dataset containing valuable information about various vehicles.
In this project, I tackle several intriguing questions related to vehicle sales using a dataset provided in both raw and cleaned formats. The key questions we seek to answer include:
- Which product lines have the highest and lowest sales? Create a chart that is representable?
- Show sales performance over time, is there any pattern?
- How does deal size (small, medium, large) correlate with total sales? and What is the percentage of contribution for each type of deal? (💡 Optional: Create with PandasAI)
These are the following resources:
- Dataset: Google Sheets - Dataset
- Python Code Notebook: Google Colab - Python Code
- Presentation Slides: Google Slides - Presentation
- Interactive Dashboard: Tableau Public - Dashboard
- Data Cleaning and Preparation: Detailed steps for data preprocessing, including handling missing values, outlier detection, and data normalization.
- Exploratory Data Analysis (EDA): In-depth analysis of vehicle sales data to identify key trends, correlations, and patterns.
- Visualization: Interactive and static visualizations to represent sales data effectively, using tools like Matplotlib, Seaborn, and Plotly.
| Tools Utilized | Skills |
|---|---|
| Google Colaboratory | Logical Thinking |
| Tableau for Data Visualization | Analysis and Representation |
| Google Sheets | Python |
| Google Slides | Problem Solving |
| Creativity |
- Clone the Repository:
git clone https://github.com/yourusername/vehicle-sales-data-analytics.git
- Install Dependencies:
Navigate to the project directory and run to install all necessary libraries:
pip install -r requirements.txt
- Run the Notebooks: Open Jupyter Notebook and explore the provided notebooks to see the data analysis and visualizing steps in action.
- Explore the Data: Use the provided scripts and tools to conduct your own analysis on the vehicle sales data.