# Exploratory Data Analysis - Paavo Dataset

An exploratory data analysis (EDA) pipeline for **Statistics Finland's Paavo open dataset**, which provides area 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

```
├── 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 Visualizations & Analytical Insights

Below are the detailed logical notes, observations, and conclusions derived from each generated plot:

### 1. `01_distributions.png` - Key Demographic & Income Distributions
* **Inhabitants Distribution:** Highly right-skewed. The vast majority of postal code areas have small populations, while a few urban hubs house large concentrations of people.
* **Average vs. Median Income:** `Average income` typically exhibits a rightward tail compared to `Median income`, indicating income inequality where a small percentage of high earners elevates average regional figures.
* **Average Age & Household Size:** Average age shows a normal to slightly multi-modal distribution, distinguishing university/younger urban areas from aging rural communities. Household sizes cluster tightly around 1.5 to 2.5 people per household, reflecting national trends of smaller urban households.

---

### 2. `02_correlation_heatmap.png` - Feature Correlation Matrix
* **Population vs. Total Purchasing Power:** Strong positive correlation between total `Inhabitants` / `Households` and `Accumulated purchasing power`, confirming that volume drives overall market size.
* **Income & Age Dynamics:** Median income often correlates negatively with higher average age in rural regions, whereas prime working-age areas show elevated median income.
* **Housing vs. Population:** High correlation between the number of residential units and population, though variations indicate differences in housing structures (apartment-heavy urban vs. single-family rural areas).

---

### 3. `03_income_categories_top20.png` - Individual Income Composition (Top 20 Populated Areas)
* **Urban Middle-Class Dominance:** Highly populated postal codes (e.g., major urban centers in Helsinki, Tampere, Oulu) show a large proportion of inhabitants falling into the middle-income tier.
* **Student & Young Professional Enclaves:** Top areas containing major universities exhibit higher ratios of the lowest income category due to non-working student populations, despite high total population counts.

---

### 4. `04_household_income_categories_top20.png` - Household Income Composition (Top 20 Household Areas)
* **Single-Person Household Impact:** Household income distributions shift relative to individual income distributions because single-person households are more likely to fall into lower household-income brackets compared to dual-income families.
* **Socioeconomic Stratification:** Comparing suburban centers against inner-city areas reveals clear differences: suburban hub areas tend to have larger shares of upper-tier household incomes, whereas dense inner-city postal codes show a higher volume of single-income and entry-level households.

---

### 5. `05_inhabitants_per_building_top20.png` - Urban Housing Density
* **Multi-Storey Concentration:** Dense urban postal codes dominate this metric, as high-rise residential complexes house significantly more people per single residential structure building footprint.
* **Infrastructure Demand:** Areas ranking highest in inhabitants per building represent key focus zones for high-density municipal services, public transit routes, and commercial utility planning.

---

### 6. `06_purchasing_power_top20.png` - Accumulated Purchasing Power
* **Market Size Drivers:** Total purchasing power is heavily weighted toward high-population, high-income urban districts (primarily Uusimaa/Helsinki region).
* **Commercial Viability:** While some areas may have lower average incomes, their high population density compensates to produce massive aggregate purchasing capacity (EUR), making them critical target zones for retail and enterprise expansion.

---

## Summary of Output 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). |