Intro to Predictive Accounting Analytics

This project is a demonstration for current cohort of accounting students that undertaking Chartered Accounting Data Analytics elective to demonstrate a combination of predictive analytics with traditional financial analysis techniques for comprehensive accounting insights.

Features

1. Revenue Prediction

   • Linear Regression model
   • ARIMA time series forecasting

2. Financial Statement Analysis

   • Trend Analysis
   • Horizontal Analysis
   • Vertical Analysis
   • Key Financial Ratios

3. Data Generation

   • Revenue time series
   • Financial statement data

4. Visualizations

   • Interactive Plotly graphs

Analyses Available

• Revenue forecasting
• Historical financial performance trends
• Profitability and efficiency metrics
• Financial structure composition
• Comparative period analysis

Tools Used

• Python
• Pandas, NumPy
• Scikit-learn, Statsmodels
• Plotly

Getting Started

1. Clone this repository
2. Create a virtual environment: python -m venv venv
3. Activate the virtual environment:
   • Windows: venv\Scripts\activate
   • macOS/Linux: source venv/bin/activate
4. Install requirements: pip install -r requirements.txt

Usage

1. Generate sample data: python src/data/generate_data.py
2. Run revenue prediction models: python src/models/revenue_prediction.py
3. Explore the Jupyter notebook for detailed analysis: jupyter notebook notebooks/revenue_analysis.ipynb

Project Structure

• data/: Contains raw and processed data
• notebooks/: Jupyter notebooks for analysis
• src/: Source code for data generation, models, and visualization
• results/: Output figures and analysis results

Folder Structure

intro-predictive-accounting-analytics/ │ ├── data/ │ ├── raw/ │ │ ├── revenue_data.csv │ │ └── financial_data.csv │ └── processed/ │ ├── notebooks/ │ ├── revenue_analysis.ipynb │ └── financial_analysis.ipynb │ ├── src/ │ ├── data/ │ │ ├── generate_data.py │ │ └── generate_financial_data.py │ ├── models/ │ │ └── revenue_prediction.py │ └── visualization/ │ └── plots.py │ ├── results/ │ └── figures/ │ ├── requirements.txt └── README.md

To run the project

1. Create the folder structure as shown above.
2. Copy and paste the provided code into the respective files.
3. Open a terminal and navigate to the project root directory.
4. Run the following commands in order:
   • python src/data/generate_data.py
   • python src/data/generate_financial_data.py
   • python src/models/revenue_prediction.py
   • jupyter notebook notebooks/revenue_analysis.ipynb

What will happen after running the commands?

• This will generate the data files and run the analyses, creating output in the results directory.
• Remember, you don't run the Python files to create them - you create them first, then run them to execute the code they contain.
• For the Jupyter notebooks, you would typically open them using Jupyter Lab or Jupyter Notebook and add content interactively.
• This process will set up the basic structure of your project. You can then start filling in the details, writing more code, and expanding the analysis as needed.

The analysis includes:

• Trend Analysis
• Horizontal Analysis
• Vertical Analysis
• Financial Ratios

When you are done

• You can add more data and models to the project as needed.
• You can also use this as a template for your own projects.
• Or deactivate the virtual environment using the command deactivate.

Important Notes

• This is a simple example to get you started. In a real-world scenario, you would likely use more sophisticated models and techniques.

• This project is not intended to be a comprehensive analysis of accounting data. It is simply a demonstration of the basic techniques.

• You need to activate the virtual environment every time you open a new terminal window and want to work on this project. Make sure you're in the project root directory when activating the virtual environment and running the scripts.

• If you're using an IDE like PyCharm or VS Code, you may need to select the virtual environment as the Python interpreter for your project.

• If you're still having trouble, please let me know what specific error you're encountering, and I'll be happy to help further.

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