Certainly! Here's a concise README for your GitHub project:

This project analyzes road accident data using Power BI. The raw data is sourced from Kaggle. Follow the steps below to explore and visualize insights related to road safety.

"The dataset is not included in the repository. You can collect the data yourself, or if you need any assistance, you can contact me at [email protected]."

1. Requirement Gathering: Identify project objectives and goals. Understand what insights stakeholders are seeking.

2. Stakeholders in Project: Identify and engage with stakeholders to gather requirements and expectations.

3. Connecting Data with Power BI: Import and connect the road accident datasets to Power BI for analysis.

4. Data Cleaning: Cleanse and preprocess the data to ensure accuracy and consistency.

5. Data Processing: Apply necessary transformations to prepare the data for modeling.

6. Data Modelling: Build data models to structure and organize the data for effective analysis. Create a new calendar table to enhance time-based analysis. Connect the calendar table to the main data table for seamless integration.

7. Data Visualization: Leverage Power BI's visualization tools to create insightful charts and graphs.

8. Reports and Dashboard Building: Develop comprehensive reports and interactive dashboards for stakeholders.

9. Insights: Derive meaningful insights from the visualizations to inform decision-making.

1. Clone the Repository:

   git clone https://github.com/yourusername/road-accident-analysis.git

2. Explore Datasets: Access raw datasets in the 'data' folder, sourced from Kaggle.

3. Connect with Power BI: Open Power BI Desktop and connect with the provided datasets.

4. Data Cleaning and Processing: Review scripts in the 'scripts' folder for data cleaning and processing.

5. Data Visualization: Open the Power BI file in the 'dashboard' folder for interactive visualizations.

6. Contribute: Feel free to contribute by opening issues or submitting pull requests.

• Kaggle for providing the road accident datasets.