# Comprehensive Analysis Report: Bike Sharing Demand Prediction

## Executive Summary

This analysis examines bike rental patterns using historical data containing temporal, weather, and seasonal features. The dataset reveals strong cyclical patterns driven by commuting behavior, weather conditions, and seasonal variations. Understanding these patterns is crucial for optimizing bike inventory management, maintenance scheduling, and marketing strategies for bike-sharing services.

## Key Findings

### 1. Temporal Patterns Drive Usage Behavior

![Hourly Rental Pattern](http://localhost:8002/outputs/dsat_run_new_my_task_01_4a56206a/sandbox/visualizations/hourly_trend.png)
**Hourly Rental Pattern Analysis**
The data reveals a clear bimodal distribution with distinct peaks during morning (8 AM) and evening (5-6 PM) commute hours, indicating that bike sharing serves primarily as a transportation solution for daily commuters. The lowest usage occurs during early morning hours (1-4 AM), suggesting limited recreational or late-night usage. This pattern highlights the importance of positioning bike-sharing as a reliable commuting alternative.

### 2. Day Type Influences Rental Volume

![Day Type Comparison](http://localhost:8002/outputs/dsat_run_new_my_task_01_4a56206a/sandbox/visualizations/day_type_comparison.png)
**Day Type Comparison**
Working days show slightly higher average rentals than holidays and weekends, reinforcing the commuting hypothesis. This suggests that bike-sharing services should focus on reliability and availability during weekday peak hours while potentially developing weekend-specific promotions to boost off-peak usage.

### 3. Weather Conditions Significantly Impact Demand

![Weather Impact Analysis](http://localhost:8002/outputs/dsat_run_new_my_task_01_4a56206a/sandbox/visualizations/weather_impact.png)
**Weather Impact Analysis**
Clear weather conditions significantly increase bike rentals, while adverse weather (rain/snow) dramatically reduces rental activity by up to 50-70%. This finding underscores the need for dynamic pricing strategies and proactive maintenance scheduling based on weather forecasts to optimize revenue and resource allocation.

### 4. Environmental Factors Show Strong Correlations

![Correlation Analysis](http://localhost:8002/outputs/dsat_run_new_my_task_01_4a56206a/sandbox/visualizations/correlation_matrix.png)
**Correlation Analysis**
Temperature demonstrates the strongest positive correlation with rental counts (r ≈ 0.40), indicating that warmer weather encourages bike usage. Humidity shows moderate negative correlation, suggesting discomfort discourages riding. The high correlation between temperature and "feels like" temperature (atemp) indicates potential multicollinearity that should be addressed in modeling.

### 5. Seasonal Variations Reveal Clear Patterns

![Seasonal Patterns Analysis](http://localhost:8002/outputs/dsat_run_new_my_task_01_4a56206a/sandbox/visualizations/seasonal_patterns.png)
**Seasonal Patterns Analysis**
Monthly trends show highest usage in summer months (June-August), with seasonal analysis confirming that summer and fall have the highest rental activity. This seasonal pattern suggests the need for strategic inventory management, with increased bike availability during peak seasons and maintenance scheduling during off-peak periods.

### 6. Target Variable Distribution

![Target Variable Distribution Analysis](http://localhost:8002/outputs/dsat_run_new_my_task_01_4a56206a/sandbox/visualizations/dist_target.png)
**Target Variable Distribution Analysis**
The original distribution shows a right-skewed pattern, which is common for count data. The log-transformed distribution demonstrates better modeling properties, suggesting that logarithmic transformations or Poisson-based models may yield superior predictive performance.

## Business Implications

1. **Operational Planning**: The strong commuting patterns indicate that bike availability should be optimized around peak hours (7-9 AM and 4-7 PM) to maximize utilization.
2. **Revenue Management**: Weather-dependent pricing strategies could help mitigate revenue loss during adverse conditions while maximizing profits during favorable weather.
3. **Marketing Strategy**: Targeted promotions during weekends and off-peak hours could help balance demand throughout the day and week.
4. **Maintenance Scheduling**: Seasonal patterns suggest optimal maintenance windows during winter months when demand is lowest.

## Recommendations

1. **Model Development**: Use time-series aware models that capture hourly, daily, and seasonal patterns while accounting for weather conditions.
2. **Feature Engineering**: Create derived features such as time-of-day indicators, weather severity indices, and holiday proximity metrics.
3. **Evaluation Strategy**: Implement time-based cross-validation to ensure model robustness across different seasons and conditions.
4. **Business Applications**: Develop a demand forecasting system that integrates weather forecasts to optimize bike redistribution and staffing.

The analysis confirms that bike-sharing demand is highly predictable when considering temporal, weather, and seasonal factors, providing a solid foundation for developing accurate forecasting models and data-driven business strategies.



## 🏁 Final Run Summary

### 📊 Performance
- **Score**: 0.0000
- **Cost**: $0.0172

### 💡 Methodology Summary
The solution employs a Random Forest Regressor to predict bike rental counts. The methodology begins with crucial data preprocessing, where the raw datetime string is converted into a datetime object. This allows for effective feature engineering by extracting multiple temporal components: hour, day, month, and year. These new features are created to help the model capture patterns related to time, such as daily commuting cycles, seasonal variations, and yearly trends, which are critical for accurate demand forecasting.

The model selection centers on a Random Forest Regressor, chosen for its ability to handle complex, non-linear relationships between the engineered features and the target variable. After training the model on all features, predictions are post-processed to ensure they are non-negative, as negative rental counts are impossible. Finally, the model's performance is evaluated using the Root Mean Squared Log Error (RMSLE) on the training data, a metric well-suited for this task as it penalizes underestimates more heavily than overestimates and handles large value ranges effectively.

### 🧩 Final Solution Code

```python
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_log_error
import numpy as np

# Load data
train_df = pd.read_csv('train.csv')
test_df = pd.read_csv('test.csv')

# Parse datetime and extract temporal features
for df in [train_df, test_df]:
    df['datetime'] = pd.to_datetime(df['datetime'])
    df['hour'] = df['datetime'].dt.hour
    df['day'] = df['datetime'].dt.day
    df['month'] = df['datetime'].dt.month
    df['year'] = df['datetime'].dt.year

# Prepare features and target
feature_cols = ['season', 'holiday', 'workingday', 'weather', 'temp', 'atemp', 'humidity', 'windspeed', 'hour', 'day', 'month', 'year']
X_train = train_df[feature_cols]
y_train = train_df['count']
X_test = test_df[feature_cols]

# Train model
model = RandomForestRegressor(n_estimators=100, random_state=42, n_jobs=-1)
model.fit(X_train, y_train)

# Make predictions
y_pred = model.predict(X_test)
y_pred = np.where(y_pred < 0, 0, y_pred)  # Ensure non-negative predictions

# Create submission file
submission_df = pd.DataFrame({
    'datetime': test_df['datetime'],
    'count': y_pred
})
submission_df.to_csv('submission_new-my-task-01_17ff77.csv', index=False)

# Print validation score on training data
train_pred = model.predict(X_train)
train_pred = np.where(train_pred < 0, 0, train_pred)
rmsle = np.sqrt(mean_squared_log_error(y_train, train_pred))
print(f"Training RMSLE: {rmsle:.4f}")
```
