🧮 AnalyticTradeFinance

AnalyticTradeFinance is a personal finance analytics project leveraging advanced data science techniques to analyze income trends and develop strategic financial insights. Using personal banking data from ING, this project explores patterns, creates visualizations, and identifies actionable strategies to optimize financial planning and decision-making.

The focus is on providing an advanced understanding of income trends, spending behavior, and potential opportunities for investment and savings growth.

---

✨ Features

• 📊 Advanced Income Analysis: Identify trends, anomalies, and patterns in income flows.
• 💡 Strategic Financial Insights: Develop actionable strategies for savings, investments, and expense management.
• 📈 Custom Visualizations: Intuitive and interactive plots for clear financial insights.
• 🧩 Scalable and Modular Design: Easily extendable framework for integrating additional financial datasets or analytical modules.

---

🔧 Technologies and Languages

• 🐍 Python: Data processing, analytics, and visualization (pandas, numpy, matplotlib, seaborn, plotly)
• 📓 Jupyter Notebook: Interactive data exploration and presentation
• 🗃️ SQL: Querying and managing dataset storage
• 🛠️ Git: Version control and collaboration

📊 Expense Report (2023-10-07 to 2024-08-18):

----------------------------------------
Other                €28,869.83
Groceries            €3,117.80
Public Transport     €2,147.81
Dining out           €942.85
----------------------------------------
Total Spent:         €35,078.29
Daily Average:       €110.66

---

📥 Installation and Setup

1. Clone the repository:

   git clone https://github.com/yourusername/AnalyticTradeFinance.git

2. Navigate to the project directory:

Move into the cloned repository folder:

   cd AnalyticTradeFinance

3. Set up a virtual environment (optional):

It’s recommended to create a virtual environment to manage dependencies:

python -m venv env
source env/bin/activate  # For Linux/Mac
env\Scripts\activate     # For Windows

4. Install dependencies:

Install all the required Python libraries:

pip install -r requirements.txt

5.Add your data file:

Place your ING bank statement file named bank_statement_expenses.csv in the /data folder.

6.Run the Jupyter Notebook server:

Start the Jupyter Notebook server to interact with the project:

jupyter notebook

7.Open the analysis notebook:

In your browser, navigate to the notebook file (e.g., income_analysis.ipynb) and start exploring your financial data.