Upload README.md

README.md

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+# Exploratory Data Analysis - Paavo Dataset
+
+An exploratory data analysis (EDA) pipeline for **Statistics Finland's Paavo open dataset**, which provides postal code and municipality-level statistics on demographics, income levels, household structures, and building infrastructure across Finland.
+
+## Project Overview
+
+This script processes the Paavo dataset to generate summary statistics, distribution plots, correlation analysis, and targeted visualizations for key metrics across top-ranking postal areas.
+
+### Key Analysis Features:
+* **Data Cleaning:** Strips whitespace from headers and excludes regional summary records (`MK*` rows, e.g., "MK01 Uusimaa").
+* **Demographic Distributions:** Analyzes key attributes such as total inhabitants, average/median income, average age, and household sizes.
+* **Correlation Heatmap:** Generates feature relationships across all numeric metrics.
+* **Income & Household Breakdown:** Evaluates distribution across lowest, middle, and highest income tiers for top population and household centers.
+* **Density & Economic Power Metrics:** Computes housing density proxies (inhabitants per residential building) and analyzes top regional purchasing power.
+
+---
+
+## Directory Structure
+
+```text
+├── eda_script.py          # Main EDA script
+├── PaavoDataset.csv       # Source dataset (Statistics Finland)
+└── eda_outputs/           # Output directory generated by script
+    ├── summary_statistics.csv
+    ├── 01_distributions.png
+    ├── 02_correlation_heatmap.png
+    ├── 03_income_categories_top20.png
+    ├── 04_household_income_categories_top20.png
+    ├── 05_inhabitants_per_building_top20.png
+    └── 06_purchasing_power_top20.png
+```
+
+---
+
+## Prerequisites & Installation
+
+Ensure you have Python 3.8+ installed along with the required dependencies:
+
+```bash
+pip install pandas matplotlib seaborn
+```
+
+---
+
+## Data Input Requirements
+
+ place the raw dataset file **`PaavoDataset.csv`** in the root directory.
+
+* **Format:** Semicolon-separated values (`sep=";"`)
+* **Encoding:** `latin1`
+* **Key Columns Expected:**
+  * `Area`
+  * `Inhabitants`, `Average age of inhabitants`, `Average size of households`
+  * `Average income of inhabitants`, `Median income of inhabitants`
+  * `Inhabitants belonging to the lowest/middle/highest income category`
+  * `Households`, `Households belonging to the lowest/middle/highest income category`
+  * `Residential buildings`, `Accumulated purchasing power of inhabitants`
+
+---
+
+## Usage
+
+Run the Python analysis script:
+
+```bash
+python eda_script.py
+```
+
+---
+
+## Generated Artifacts
+
+All outputs are exported to the **`eda_outputs/`** directory:
+
+| Artifact | Description |
+| :--- | :--- |
+| `summary_statistics.csv` | Descriptive statistics (`count`, `mean`, `std`, `min`, `quartiles`, `max`) for numeric columns. |
+| `01_distributions.png` | Distribution plots with KDE curves for key demographic and income metrics. |
+| `02_correlation_heatmap.png` | Matrix correlation map across all numeric features. |
+| `03_income_categories_top20.png` | Stacked horizontal bar plot of income breakdown for top 20 areas by population. |
+| `04_household_income_categories_top20.png` | Stacked horizontal bar plot of household income breakdown for top 20 areas by households. |
+| `05_inhabitants_per_building_top20.png` | Bar chart of top 20 areas ranked by inhabitants per residential building. |
+| `06_purchasing_power_top20.png` | Bar chart of top 20 areas ranked by total accumulated purchasing power (EUR). |