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README.md

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-# Dynamic Pricing Engine for E-commerce Website
-
-An end-to-end intermediate MVP for real-time dynamic pricing with:
-
-- Synthetic data generation for 50,000+ SKU style scenarios
-- Random Forest and XGBoost training pipelines
-- FastAPI inference API for real-time price recommendations across synthetic and Kaggle retail model profiles
-- Streamlit dashboard for simulation and monitoring
-- Competitor price blending, inventory adjustments, and flash sale detection
-- Optional Redis and Kafka integrations
-
-## Architecture
-
-1. `scripts/generate_sample_data.py` creates synthetic click, order, inventory, and competitor signals.
-2. `scripts/train_model.py` trains a baseline Random Forest and an XGBoost regressor, then saves the best model.
-3. `app/api.py` serves real-time pricing recommendations through FastAPI.
-4. `app/dashboard.py` provides a Streamlit control panel for scenario analysis and monitoring.
-5. `app/streaming.py` provides optional Kafka event producer and consumer helpers.
-
-## Project Structure
-
-```text
-app/
-  api.py
-  config.py
-  dashboard.py
-  feature_engineering.py
-  modeling.py
-  pricing_engine.py
-  schemas.py
-  streaming.py
-data/
-  processed/
-  raw/
-models/
-scripts/
-  generate_sample_data.py
-  train_model.py
-tests/
-  smoke_test.py
-```
-
-## Quick Start
-
-### 1. Create a virtual environment
-
-```powershell
-python -m venv .venv
-.venv\Scripts\Activate.ps1
-pip install -r requirements.txt
-```
-
-### 2. Generate sample data
-
-```powershell
-python scripts/generate_sample_data.py --rows 25000
-```
-
-### 3. Train the model
-
-```powershell
-python scripts/train_model.py
-```
-
-### Train on a real Kaggle dataset
-
-Recommended dataset:
-
-- Kaggle `Retail Price Optimization`: https://www.kaggle.com/datasets/suddharshan/retail-price-optimization
-
-Download `retail_price.csv` and place it at:
-
-```text
-data/raw/kaggle/retail_price.csv
-```
-
-Then train with:
-
-```powershell
-python scripts/train_model.py --profile kaggle_retail --data-path data/raw/kaggle/retail_price.csv
-```
-
-Notes:
-
-- This trains a real-dataset pricing model using the Kaggle retail schema.
-- The FastAPI service now exposes a dedicated Kaggle request schema at `/price/recommend/kaggle` when a `kaggle_retail` model is loaded.
-
-### 4. Run the FastAPI service
-
-```powershell
-uvicorn app.api:app --reload
-```
-
-Docs will be available at `http://127.0.0.1:8000/docs`.
-
-### 5. Run the Streamlit dashboard
-
-```powershell
-streamlit run app/dashboard.py
-```
-
-## API Endpoints
-
-- `GET /health`: Service health and artifact status
-- `POST /price/recommend`: Returns a recommended price, confidence score, and pricing signals for the synthetic profile
-- `POST /price/recommend/kaggle`: Returns a recommended price and price gap analysis for the Kaggle retail profile
-- `POST /events/order`: Registers an order event for flash sale detection
-- `GET /monitoring/summary`: Returns pricing and event summary metrics
-
-## Core Pricing Logic
-
-The engine combines:
-
-- ML-predicted baseline price
-- Competitor blending: default `70% model / 30% competitor`
-- Inventory pressure adjustments
-- Demand surge adjustments
-- Flash sale emergency multiplier
-- Floor and ceiling guardrails
-
-## Example Request
-
-```json
-{
-  "sku_id": "SKU-1024",
-  "category": "electronics",
-  "brand": "brand_b",
-  "customer_segment": "premium",
-  "hour_of_day": 20,
-  "day_of_week": 5,
-  "is_weekend": 1,
-  "is_festival": 1,
-  "inventory_level": 23,
-  "inventory_days_cover": 4.1,
-  "competitor_price": 1849.0,
-  "click_through_rate": 0.074,
-  "conversion_rate": 0.038,
-  "units_sold_last_5m": 7,
-  "units_sold_last_1h": 33,
-  "base_cost": 1210.0,
-  "current_price": 1799.0
-}
-```
-
-## AWS Deployment Notes
-
-- EC2: Host FastAPI and Streamlit on one instance for a demo, or split them later if needed.
-- S3: Good fit for model artifacts and price history snapshots.
-- Lambda: Optional competitor polling and scheduled retraining.
-- CloudWatch: Use for API logs, dashboard process logs, and alert thresholds.
-- Redis: Cache recent recommendations and event counters.
-- Kafka: Use self-managed Kafka for demos, or Amazon MSK only after checking cost.
-
-Current AWS note:
-
-- AWS Free Tier changed on July 15, 2025. Official AWS docs say accounts created before July 15, 2025 can use `t2.micro` free tier eligibility in supported regions, while accounts created on or after July 15, 2025 use a newer credit-based/free-plan model with eligible instance types such as `t3.micro`. Verify your account type before launch.
-- Amazon S3 is covered through the newer AWS Free Tier credit/free-plan model, not a simple permanent 5 GB rule for all new accounts.
-- Amazon MSK pricing is pay-as-you-go on the official AWS pricing page. Treat MSK as a potential paid service unless your account credits cover it.
-
-## Docker Deployment
-
-Build and run the full stack locally or on a VM:
-
-```powershell
-docker compose up --build
-```
-
-Then open:
-
-- API docs: `http://127.0.0.1:8000/docs`
-- Streamlit dashboard: `http://127.0.0.1:8501`
-
-Notes:
-
-- Compose mounts `data/` and `models/` from the host so retrained artifacts persist outside the containers.
-- Replace `.env.example` with a real `.env` file before production deployment.
-
-If you want to build each image separately:
-
-```powershell
-docker build -t dynamic-pricing-api -f Dockerfile .
-docker build -t dynamic-pricing-dashboard -f Dockerfile.streamlit .
-```
-
-## EC2 Deployment
-
-Recommended target path:
-
-```text
-/opt/dynamic-pricing-engine
-```
-
-Quick setup outline on Ubuntu EC2:
-
-```bash
-sudo apt update
-sudo apt install -y python3.13-venv
-cd /opt
-sudo mkdir -p dynamic-pricing-engine
-sudo chown ubuntu:ubuntu dynamic-pricing-engine
-```
-
-Copy the repo to `/opt/dynamic-pricing-engine`, then:
-
-```bash
-cd /opt/dynamic-pricing-engine
-python3 -m venv .venv
-source .venv/bin/activate
-pip install --upgrade pip
-pip install -r requirements.txt
-cp .env.example .env
-chmod +x deploy/ec2/start_api.sh
-chmod +x deploy/ec2/start_dashboard.sh
-python scripts/generate_sample_data.py --rows 25000
-python scripts/train_model.py --profile kaggle_retail --data-path data/raw/kaggle/retail_price.csv
-```
-
-Important:
-
-- The current Streamlit dashboard expects a `kaggle_retail` model artifact.
-- If you deploy both API and dashboard together, train the `kaggle_retail` profile and use `POST /price/recommend/kaggle`.
-- If you want the synthetic API profile in production, deploy the API by itself or refactor the dashboard to use the synthetic schema.
-
-Install the included `systemd` services:
-
-```bash
-sudo cp deploy/ec2/dynamic-pricing-api.service /etc/systemd/system/
-sudo cp deploy/ec2/dynamic-pricing-dashboard.service /etc/systemd/system/
-sudo systemctl daemon-reload
-sudo systemctl enable dynamic-pricing-api
-sudo systemctl enable dynamic-pricing-dashboard
-sudo systemctl start dynamic-pricing-api
-sudo systemctl start dynamic-pricing-dashboard
-sudo systemctl status dynamic-pricing-api
-sudo systemctl status dynamic-pricing-dashboard
-```
-
-Open these after the instance is up and the EC2 security group allows inbound `8000` and `8501`:
-
-```text
-http://<your-ec2-public-ip>:8000/docs
-http://<your-ec2-public-ip>:8501
-```
-
-Suggested EC2 security group inbound rules for a demo:
-
-- `22` from your IP only
-- `8000` from your IP or a narrow trusted range
-- `8501` from your IP or a narrow trusted range
-
-For public production access, put Nginx in front and expose only `80` and `443`.
-
-## EC2 With Docker
-
-If you prefer containers on EC2 instead of `systemd`, install Docker on Ubuntu and run:
-
-```bash
-cd /opt/dynamic-pricing-engine
-docker compose up --build -d
-docker compose ps
-```
-
-Then open:
-
-```text
-http://<your-ec2-public-ip>:8000/docs
-http://<your-ec2-public-ip>:8501
-```
-
-## Extension Ideas
-
-- Replace synthetic data with marketplace telemetry
-- Add Flink or Spark Structured Streaming for event windows
-- Add SHAP explainability for pricing decisions
-- Introduce RL policy optimization for multi-step pricing
-- Connect to Amazon Personalize for customer-level contextual pricing
-"# Dynamic-Pricing-Engine" 
-"# Dynamic-Pricing-Engine" 
+---
+title: Dynamic Pricing Engine
+emoji: 💰
+colorFrom: blue
+colorTo: green
+sdk: docker
+pinned: false
+---