Linear-regression

AI-powered LPG analytics engine for precision consumption forecasting and atmospheric safety monitoring. Phase 1: Linear Predictive Foundations.

🔥 GASAI | Intelligence in Every Breath

An AI-Powered Ecosystem for Precision LPG Monitoring & Safety Analytics.

📍 The Vision

GASAI is a data-first solution designed to eliminate the uncertainty of LPG usage. By leveraging advanced edge-sensing and atmospheric analysis, we transform raw data into actionable insights for households and industrial supply chains.

This repository houses the Core Predictive Engine, focusing on the statistical models that power our depletion forecasting and leak detection alerts.

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🧠 The Intelligence Stack

The GASAI system is built on a progressive AI research path, moving from descriptive statistics to autonomous agents:

• Phase 1: Linear Foundations (Current) - Utilizing Multiple Linear Regression to baseline consumption rates.
• Phase 2: Non-Linear Complexity - Implementing Gradient Boosting for high-variance usage patterns.
• Phase 3: Deep Sensing - Neural Networks for multi-variate time-series forecasting.
• Phase 4: Agentic Logistics - Reinforcement Learning (RL) agents for autonomous supply chain optimization.

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📐 Statistical Core

Our engine doesn't just "read" weight; it models consumption dynamics using the following logic:

1. Instantaneous Consumption Rate ($\dot{C}$): Determines the velocity of fuel depletion relative to time intervals.

$$\dot{C} = - \frac{W_{t} - W_{t-\Delta t}}{\Delta t}$$

2. Atmospheric Density Analysis ($PPM$): A log-linear transformation of atmospheric signals to detect trace gas presence.

$$\log(PPM) = m \cdot \log(Ratio) + b$$

3. Depletion Forecasting: Predictive modeling of the "Time-to-Empty" ($T_e$) by adjusting for environmental variables and signal noise.

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🏗️ System Architecture

The GASAI ecosystem consists of three proprietary layers:

• The Edge Layer: High-precision sensing hardware (Proprietary Specifications) that captures mass and atmospheric data.
• The Intelligence Layer (This Repo): The Python-based processing engine that cleans, analyzes, and predicts.
• The Visual Layer: A premium, cinematic dashboard for end-users and distributors.

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📂 Repository Structure

• research/: Jupyter notebooks containing model training, validation, and EDA.
• models/: Serialized model files (.pkl / .onnx) ready for deployment.
• src/: Core Python modules for data ingestion and real-time inference.

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🚀 About the Developer

Joshua Mwangi Statistics Major, JKUAT | Full-Stack Software Engineer | Automation Engineer | Quantitative Analyst

Bridging the gap between raw physical signals and high-level predictive intelligence. GASAI is currently being prepared for showcase at the Africa Forward Summit as a prime example of Kenyan-led industrial innovation.

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🔒 Intellectual Property Notice

This repository focuses exclusively on the software and mathematical frameworks. For business inquiries or partnership opportunities, please contact the author directly at kingsleymwangi05@gmail.com

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