<UPDATE_REPORT>
# Exploratory Data Analysis Report: Olympic Medal Prediction

## 1. Data Overview
- **Source**: Updated dataset with enhanced feature engineering
- **Records**: 5,670 rows (increased from 4,752)
- **Features**: 10 columns (expanded from 7)
- **Primary Target**: `total_medals` - medals won by country in specific Olympic year

## 2. Data Structure
| Column | Type | Description |
|--------|------|-------------|
| id | int64 | Unique identifier for each record |
| medals_lag_1 | float64 | Medal count from previous Olympics (1 lag) |
| medals_lag_2 | float64 | Medal count from two Olympics prior (2 lag) |
| medals_lag_3 | float64 | Medal count from three Olympics prior (3 lag) |
| medals_ma_3 | float64 | 3-Olympic moving average of medal counts |
| is_host | int64 | Binary indicator (1 if host country) |
| country_avg_medals | float64 | Historical average medals per Olympics for country |
| country_max_medals | float64 | Maximum medals ever won by country in single Olympics |
| country_total_medals | float64 | Total historical medals won by country |
| total_medals | int64 | Target: medals won by country in current Olympic year |

## 3. Key Dataset Changes
- **Enhanced Features**: Added comprehensive country-level statistics (avg, max, total)
- **Moving Average**: New `medals_ma_3` feature captures trend momentum
- **Record Increase**: Additional 918 records (19% increase)
- **Identifier**: `id` column added for unique record identification

## 4. Feature Engineering Assessment
### Lag Features (medals_lag_1, medals_lag_2, medals_lag_3)
- Maintain temporal dependency structure
- Provide short-to-medium term performance memory

### Moving Average (medals_ma_3)
- Smooths out Olympic-cycle fluctuations
- Captures performance trends beyond single lags

### Country-Level Statistics
- **country_avg_medals**: Baseline performance expectation
- **country_max_medals**: Peak performance capability
- **country_total_medals**: Overall historical success

## 5. Data Quality Implications
- **Missing Country Column**: Analysis now focuses on statistical patterns rather than country-specific narratives
- **Enhanced Predictive Power**: Country-level features provide strong baseline predictors
- **Comprehensive Coverage**: Expanded feature set captures multiple performance dimensions

## 6. Modeling Advantages
- **Rich Feature Set**: Multiple complementary perspectives on performance
- **Baseline Establishment**: Country statistics provide strong priors
- **Trend Capture**: Moving average adds momentum component
- **Reduced Overfitting**: Multiple correlated features allow regularization benefits

## 7. Analytical Considerations
- **Feature Correlation**: Country statistics likely highly correlated with lag features
- **Scale Differences**: Features span different measurement scales (may require normalization)
- **Interpretability**: Country-level features provide intuitive performance benchmarks

This updated dataset represents a significant enhancement with comprehensive feature engineering that captures both recent performance trends and historical country capabilities.
</UPDATE_REPORT>