> ## Documentation Index
> Fetch the complete documentation index at: https://freelance-4888ed08.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Problem Statement

> Context and objectives of the Demand Forecasting System

> [!NOTE]
>
> **What this solves:** Bike-sharing systems lose revenue and customers when bikes aren't where riders need them. This system uses weather-aware AI to predict demand hours in advance, enabling proactive fleet management.

## Business Context

Effective demand forecasting is the cornerstone of efficient urban mobility systems. For bike-sharing networks, matching supply with demand is critical to user satisfaction and operational efficiency.

**Fleet Rebalancing**

Ensuring bikes are available at high-demand stations while preventing full docks at destination hubs.

**Operational Efficiency**

Optimizing the schedules of redistribution trucks to move bikes proactively based on predicted spikes.

**Service Availability**

Minimizing "loss of service" events where users cannot find a bike or a dock during peak hours.

> [!TIP]
>
> This system is designed to transition forecasting from static averages to a **weather-aware MLOps (The discipline of managing the entire machine learning lifecycle from development to production.) workflow**.

## Business Impact

> [card] Reduced Stockouts
>
> Proactive rebalancing can reduce "no bike available" events by up to **35%** during peak hours.

> [card] Operational Savings
>
> Predictive scheduling optimizes redistribution truck routes, cutting unnecessary trips and **fuel costs**.

> [card] Data-Driven Decisions
>
> Real-time dashboards give operations teams **hourly demand visibility** across the entire network.

## Who Benefits

| Stakeholder | What They Get |
| - | - |
| **Operations Managers** | Hourly demand forecasts for proactive fleet rebalancing |
| **Data Scientists** | Reproducible ML pipelines with experiment tracking and model versioning |
| **Engineering Teams** | Production-ready API, Docker-based infrastructure, and CI/CD-ready architecture |
| **Business Leaders** | Measurable KPIs (prediction accuracy, uptime) and cost reduction visibility |

## Objectives

1. **Analyze Utilization**: Ingest historical ride data to understand system-wide usage patterns.
2. **Pattern Recognition**: Identify complex dependencies between demand and environmental factors like **Temperature**, **Humidity**, and **Hour of Day**.
3. **Predictive Accuracy**: Deploy a **CatBoost** regressor to forecast the total count (`cnt`) of bike rentals for upcoming hours.
4. **Operational Resilience**: Ensure 99.9% pipeline uptime with automated self-healing and drift detection.

## Technical Goals

* **Robust ML Pipeline**: modular `app-ml` service using **Kedro** for reproducible processing.
* **Scalable Orchestration**: Distributed task execution via **Celery Strategy**.
* **Production Readiness**: Zero-dependency host setup via **Docker Compose**.

***

## Related Pages

> [card] [Glossary](https://freelance-4888ed08.mintlify.site/resources/glossary.md)
>
> Plain-English explanations of every technical term used in these docs.

> [card] [System Overview](https://freelance-4888ed08.mintlify.site/architecture/system-overview.md)
>
> How all the components connect in the full architecture.


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