Exploratory Data Analysis (EDA) of daily transaction data to uncover patterns, trends, and insights using Python and Pandas. Descriptive Statistics The overall statistics of transaction amounts showed that most transactions are low to medium in value, with a few outliers representing high-value transactions.
.1 Distribution of Transaction Amounts The distribution of transaction values revealed that:
The majority of transactions are of smaller amounts.
There are a few high-value transactions, mostly associated with income or large bill payments.
2 Income vs Expense Analysis Expense transactions significantly outnumber income transactions.
Most records represent daily or regular spending behavior.
- Spending by Category The top three spending categories are:
Food (mostly groceries and eating out)
Bills (like electricity and internet)
Transportation (fuel, travel, etc.)
These categories make up the bulk of monthly expenses.
- Payment Modes UPI and Cash are the most commonly used payment methods.
Bank Transfers and Card Payments are also frequently used but less dominant.
- Monthly Trends Monthly aggregation shows spikes in expenses during certain months, possibly due to festivals or one-time payments.
Income is recorded less frequently, typically in regular monthly intervals.
- 🔁 Pivot Table Insights Pivot tables were used to summarize key findings:
Total spend by category and income/expense type helped identify which categories consume most of the household budget.
Number of transactions by payment mode provided insight into financial behavior and preferences.
Monthly trends showed spending habits across different months.
- 💡 Key Insights
Aspect Insight Spending Pattern Most transactions are small and related to groceries or utilities. Payment Preference UPI and Cash dominate daily transaction modes. Income Characteristics Income entries are fewer and usually labeled "Salary" or "Refund". High-Frequency Spending Repeated low-value transactions make up the majority of the expenses. Notes Analysis Custom notes often describe transaction purpose, useful for categorization. 8. 🔭 Suggestions for Future Work Build a dashboard for live financial tracking.
Set monthly budget thresholds and trigger alerts for overspending.
Categorize text notes using Natural Language Processing (NLP) to enhance auto-classification.
Apply time series forecasting to predict future spending trends.
- 🛠 Tools & Technologies Used Pandas: For data cleaning, manipulation, and summarization.
Matplotlib & Seaborn: For creating visualizations and graphs.
WordCloud: To analyze the most frequently used words in transaction notes.
Jupyter Notebook: For running and presenting the entire analysis workflow.
- ✅ Conclusion This EDA project provided a comprehensive understanding of household transaction behavior. The analysis highlighted spending trends, payment preferences, and potential financial planning opportunities. By continuing with forecasting and dashboard integration, this project could become a full-fledged personal finance management system.