DataAdvisor (data-advisor v0.4.0)

Autonomous Dataset Auditor, Diagnostic Engine & IoT Exam Code Bank
Engineered for students, practitioners, and engineers during timed practical examinations and data analysis tasks.
100% Offline: Requires zero internet connection to inspect datasets, search code, or retrieve solutions.

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⚡ Quickstart

1. Search Any Keyword Across the Code Bank

Search instantly across all IoT questions, Arduino sketches, Python protocols, and Machine Learning models:

# Terminal CLI:
data-advisor --search "dht22"
data-advisor --search "coap"
data-advisor --search "mqtt"
data-advisor --search "random forest"
data-advisor --list                    # List all 36 available modules

Or inside a Python script / Jupyter Notebook:

from data_advisor import search

# List all available questions and experiments:
search()

# Search for any topic or keyword:
search("dht22")
search("coap")
search(["esp32", "mqtt"])

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2. View / Retrieve Complete Question & Code Files (get_file)

Retrieve the full question statement, README setup instructions, and all source codes:

# Terminal CLI:
data-advisor --get Q1
data-advisor --get Q3
data-advisor --get CQ1
data-advisor --get SQ1
data-advisor --get 15 --line-numbers

Or inside Python:

from data_advisor import get_file, read_file

# Print the complete question, instructions, and clean code:
get_file("CQ1")
get_file("Q3", line_numbers=True)

# Or return the content as a string:
content = read_file("08")

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3. Audit Any Exam Dataset & Generate Python Code (data_advisor)

When given an unknown dataset (CSV, TSV, Excel, Parquet):

# Terminal CLI: Print diagnostic report + executable Python solution:
data-advisor exam_dataset.csv --code

# Specify target column and save generated Python script to a file:
data-advisor exam_dataset.csv --target churn --output solution.py

Or inside Python:

from data_advisor import data_advisor

# 1-Line: Outputs diagnostic report AND prints full executable Python code:
data_advisor("exam_dataset.csv")

# With target variable and script export:
data_advisor("exam_dataset.csv", target="churn", output_script="solution.py")

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📄 Master Files in Root Directory

• combined.txt: A single master file combining all 36 code modules with zero comments inside code blocks (ready for direct copy-pasting), searchable jump tags ([EXPERIMENT XX], [QUESTION QX], [COMBINED QUESTION CQX], [SIMPLE QUESTION SQX]), and an interactive Table of Contents.
• notes.txt: Complete IoT practical examination cheat sheet covering one-time pip install commands, terminal execution order, protocol port mappings (CoAP 5683, MQTT 1883, Streamlit 8501), dataset-to-model feature matrices, Supabase REST syntax, and common exam error fixes.

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📦 IoT Code Bank Architecture (36 Comprehensive Modules)

The code_bank/ directory contains 36 comprehensive modules divided into four logical tiers:

Complete Index of All 36 Modules:

Part 1: Standard Lab Experiments (01 to 17)

#	Question File	Category / Topic	Included Code Files
1	01_dht22_serial.txt	ESP32 DHT22 Temperature & Humidity (Serial)	1. sketch.ino, 2. diagram.json, 3. libraries.txt
2	02_ldr_serial.txt	ESP32 LDR Light Intensity Monitoring	1. sketch.ino, 2. diagram.json
3	03_pir_serial.txt	ESP32 PIR Motion Detection	1. sketch.ino, 2. diagram.json
4	04_mqtt_esp32_python.txt	ESP32 MQTT Publisher & Python Subscriber	1. sketch.ino, 2. subscriber.py, 3. diagram.json, 4. libraries.txt
5	05_http_rest_supabase.txt	HTTP REST API Cloud Storage & Web Dashboard	1. send_http_data.py, 2. app_http.py
6	06_mqtt_cloud_dashboard.txt	MQTT Cloud Bridge & Streamlit Dashboard	1. publisher.py, 2. mqtt_bridge.py, 3. app.py
7	07_thingsboard_simulation.txt	ThingsBoard MQTT Telemetry Simulation	1. simulate_thingsboard.py, 2. environmental_dashboard.json
8	08_coap_server_client_dashboard.txt	CoAP Server & Client Dashboard	1. server.py, 2. client.py
9	09_mqtt_hivemq_mosquitto_dual.txt	Dual MQTT (HiveMQ Public & Mosquitto Local)	1. dual_broker.py
10	10_coap_openmeteo_cloud.txt	CoAP Environmental Monitoring & Cloud DB	1. server.py, 2. app.py
11	11_iss_tracker.txt	Real-Time ISS Location Tracker & Plotter	1. iss_tracker.py
12	12_earthquake_dashboard.txt	USGS Earthquake Analysis Dashboard	1. app.py
13	13_weather_analysis.txt	Open-Meteo Weather Trend Visualizations	1. weather_analysis.py
14	14_air_quality_kochi.txt	Kochi Air Quality Dashboard (PM2.5, PM10, AQI)	1. app.py
15	15_decision_tree_occupancy.txt	Decision Tree Room Occupancy Classifier	1. occupancy_cli.py, 2. occupancy_app.py
16	16_linear_regression_beijing.txt	Linear Regression Temperature Prediction	1. temperature_cli.py, 2. temperature_app.py
17	17_random_forest_air_quality.txt	Random Forest 6-Class Air Quality Classifier	1. air_quality_cli.py, 2. air_quality_app.py

Part 2: Official Practical Exam Questions (Q1 to Q4)

#	Question File	Category / Topic	Included Code Files
18	Q1_dht22_temperature_humidity.txt	Section A: Q1 DHT22 Monitoring [30M]	1. sketch.ino, 2. diagram.json, 3. libraries.txt
19	Q2_ldr_light_intensity.txt	Section A: Q2 LDR Intensity [30M]	1. sketch.ino, 2. diagram.json
20	Q3_coap_air_quality_random_forest.txt	Section B: Q3 CoAP Air Quality & RF [30M]	1. server.py, 2. client.py, 3. app.py
21	Q4_coap_weather_decision_tree.txt	Section B: Q4 CoAP Weather & DT [30M]	1. server.py, 2. client.py, 3. app.py

Part 3: Combined 2-Topic Exam Questions (CQ1 to CQ8)

#	Question File	Category / Combination	Included Code Files
22	CQ1_coap_linear_regression.txt	CoAP + Linear Regression + Streamlit	1. server.py, 2. client.py, 3. app.py
23	CQ2_coap_decision_tree_occupancy.txt	CoAP + Decision Tree (Occupancy) + Streamlit	1. server.py, 2. client.py, 3. app.py
24	CQ3_mqtt_decision_tree_occupancy.txt	MQTT + Decision Tree (Occupancy) + Streamlit	1. publisher.py, 2. subscriber.py, 3. app.py
25	CQ4_mqtt_random_forest_air_quality.txt	MQTT + Random Forest (AQI) + Streamlit	1. publisher.py, 2. subscriber.py, 3. app.py
26	CQ5_mqtt_linear_regression.txt	MQTT + Linear Regression (Temp) + Streamlit	1. publisher.py, 2. subscriber.py, 3. app.py
27	CQ6_esp32_serial_ml_occupancy.txt	ESP32 Serial (DHT22+LDR) + Decision Tree + Streamlit	1. esp32_sensors.ino, 2. serial_classifier.py, 3. app.py
28	CQ7_mqtt_supabase_cloud.txt	MQTT + Supabase Cloud DB + Streamlit	1. publisher.py, 2. mqtt_bridge.py, 3. app.py
29	CQ8_coap_supabase_cloud.txt	CoAP + Supabase Cloud DB + Streamlit	1. server.py, 2. client_bridge.py, 3. app.py

Part 4: Minimal Diagnostic & Verification Scripts (SQ1 to SQ7)

#	Question File	Target Technology Tested	Included Code Files
30	SQ1_supabase_crud.txt	Supabase Cloud REST POST & GET Connection	1. test_supabase.py
31	SQ2_streamlit_minimal.txt	Streamlit Dashboard Layout, Metrics & Alert Badges	1. test_app.py
32	SQ3_coap_minimal.txt	Asynchronous CoAP Server & Client Pair (aiocoap)	1. server.py, 2. client.py
33	SQ4_mqtt_minimal.txt	MQTT Publisher & Subscriber Pair (paho-mqtt)	1. publisher.py, 2. subscriber.py
34	SQ5_ml_minimal.txt	Decision Tree, Random Forest & Linear Regression	1. test_models.py
35	SQ6_openmeteo_api.txt	Direct Open-Meteo REST API Ingestion (requests)	1. test_api.py
36	SQ7_esp32_serial.txt	ESP32 Serial JSON Transmitter & Python Listener	1. sketch.ino, 2. read_serial.py

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🔒 100% Offline Capability

• Zero Network Required: Does not connect to the internet to run searches, load code files, or inspect datasets.
• Local Fallback Datasets: Machine learning scripts support local offline CSV datasets (city_day.csv, PRSA_Data_...csv, datatraining.txt) and include automatic synthetic data generators if dataset files are missing.
• Local Protocol Testing: CoAP server/client examples bind to 127.0.0.1:5683 (localhost UDP) and local MQTT uses localhost:1883.

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🚀 Building & Publishing to PyPI

1. Build the Distribution

python -m build

(Generates the universal wheel .whl and source distribution .tar.gz in dist/ bundling all 36 code bank modules, notes.txt, and combined.txt).

2. Upload to PyPI

python -m twine upload dist/*

• Username: __token__
• Password: <paste your PyPI API token starting with pypi-...>

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💻 Installation on Other Machines

On any lab system, exam machine, or virtual environment:

# Install directly from PyPI
pip install data-advisor

# Or install locally in editable mode
pip install -e .

CLI Command Summary:

Command	Action
data-advisor --list or -l	List all 36 questions & files in the code bank
data-advisor --search "<keyword>" or -s	Search questions matching any keyword or phrase
data-advisor --get <CQ1/Q3/SQ1/08> or -g	Print complete question statement, setup instructions & code
data-advisor <dataset.csv> --code	Audit dataset and output complete copy-pasteable Python code
data-advisor <dataset.csv> -o run.py	Save generated Python data science pipeline to run.py