{"product_id":"ml-feature-visualiser-kit-with-arduino-mega-radar-chart-adxl345","title":"ML Feature Visualiser Kit with Arduino Mega - Radar Chart ADXL345","description":"\u003ch1\u003eArduino Mega ADXL345 Radar Chart Kit\u003c\/h1\u003e\n\n\u003cp class=\"value-summary\"\u003eEvery part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.\u003c\/p\u003e\n\n\u003cdiv class=\"specs-strip\"\u003e\n  \u003cspan\u003e\u003cstrong\u003eDifficulty:\u003c\/strong\u003e Advanced\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eBuild Time:\u003c\/strong\u003e 5-6 hrs\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eAge:\u003c\/strong\u003e 18-21\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eSkill:\u003c\/strong\u003e ML Feature Engineering\u003c\/span\u003e\n\u003c\/div\u003e\n\n\u003cp\u003eEver wondered how raw sensor data gets transformed into the features that train a machine learning model? With this kit, you'll capture 3-axis acceleration from an ADXL345, compute statistical measures like mean and standard deviation, extract frequency-domain features via FFT bin magnitudes, and plot them all as a live radar chart on a TFT display. It's a hands-on bridge between sensor data and ML feature extraction.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eYou'll assemble a self-contained instrument that reads accelerometer data, runs on-board DSP calculations on the Arduino Mega, and renders a 6-parameter radar chart (mean X\/Y\/Z, standard deviation, and dominant FFT bins) updated in real time. The timestamped data also logs to an SD card, creating a dataset you can export to Python or MATLAB for further ML training.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eCompute statistical features (mean, standard deviation) from streaming sensor data using fixed-point arithmetic on an 8-bit microcontroller.\u003c\/li\u003e\n  \u003cli\u003eImplement Fast Fourier Transform (FFT) bin extraction to identify dominant vibration frequencies in the signal.\u003c\/li\u003e\n  \u003cli\u003eDrive a 2.4-inch TFT ILI9341 display via SPI to draw dynamic radar chart geometries.\u003c\/li\u003e\n  \u003cli\u003eLog timestamped feature vectors to a MicroSD card using SPI in a multi-device bus environment, managing CS lines and power constraints.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eKit Contents\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eComponent\u003c\/th\u003e\n\u003cth\u003eQuantity\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eArduino Mega 2560\u003c\/td\u003e\n\u003ctd\u003ex1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eADXL345 Accelerometer\u003c\/td\u003e\n\u003ctd\u003ex1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003e2.4in TFT ILI9341\u003c\/td\u003e\n\u003ctd\u003ex1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eDS3231 RTC\u003c\/td\u003e\n\u003ctd\u003ex1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eMicroSD Module\u003c\/td\u003e\n\u003ctd\u003ex1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003e4.7k? Resistors\u003c\/td\u003e\n\u003ctd\u003ex5\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003e100nF Capacitors\u003c\/td\u003e\n\u003ctd\u003ex5\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003ePCB Prototype Board\u003c\/td\u003e\n\u003ctd\u003ex2\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003e9V Battery Snap\u003c\/td\u003e\n\u003ctd\u003ex1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eSoldering Iron\u003c\/td\u003e\n\u003ctd\u003ex1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eSolder Wire\u003c\/td\u003e\n\u003ctd\u003ex1\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eWhy Buy This Kit Instead of Sourcing Parts Separately\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eFactor\u003c\/th\u003e\n\u003cth\u003eSourcing Separately\u003c\/th\u003e\n\u003cth\u003eCompoden Kit\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eCompatibility checks\u003c\/td\u003e\n\u003ctd\u003eYou verify every part\u003c\/td\u003e\n\u003ctd\u003ePre-tested as a system\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eBuild support\u003c\/td\u003e\n\u003ctd\u003eForums and scattered tutorials\u003c\/td\u003e\n\u003ctd\u003eAI companion trained on this exact project\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eTime to first working build\u003c\/td\u003e\n\u003ctd\u003eDays of debugging\u003c\/td\u003e\n\u003ctd\u003eHours, with step-by-step guidance\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eShipping coordination\u003c\/td\u003e\n\u003ctd\u003eMultiple sellers, multiple delays\u003c\/td\u003e\n\u003ctd\u003eOne shipment from Bengaluru in 3-5 days\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eWho This Kit Is For\u003c\/h2\u003e\n\u003cp\u003eIf you're a BTech ECE or EEE student exploring the intersection of IoT and machine learning, this kit gives you a tangible project to add to your portfolio. It's tailor-made for Smart India Hackathon teams tackling sensor analytics, for final-year projects at IIT, NIT, VIT, or BITS Pilani, and for tinkering labs like ATL that want to demonstrate the signal-processing pipeline behind edge ML.\u003c\/p\u003e\n\n\u003ch2\u003eBuilt and Backed by Compoden\u003c\/h2\u003e\n\u003cp\u003eEvery Compoden kit ships with an AI build companion trained on this exact project - accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.\u003c\/p\u003e\n\n\u003cdetails\u003e\u003csummary\u003eWhat if I get stuck during the build?\u003c\/summary\u003e\u003cp\u003eThe AI companion, reachable through the QR code or WhatsApp, will guide you step-by-step through wiring, code upload, and debugging specific to this radar chart setup.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eDo I need prior experience with FFTs or radar charts?\u003c\/summary\u003e\u003cp\u003eNo. The companion explains the math intuitively and provides fully commented Arduino code that handles the FFT and display drawing, so you learn by building.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I export the logged data to train an actual ML model?\u003c\/summary\u003e\u003cp\u003eYes. The MicroSD stores timestamped CSV files with all extracted features. You can import them into Python, MATLAB, or any ML framework to train classifiers on your own gesture or vibration patterns.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eIs the Arduino IDE code included, and how is multi-SPI managed?\u003c\/summary\u003e\u003cp\u003eAbsolutely. The companion delivers optimized code for the Mega, with separate SPI chip-select handling for the TFT, SD card, and RTC, plus a pre-tuned FFT library matched to the ADXL345 data rate.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003eADXL345 features (mean, std, FFT bins) visualised as radar chart. Helps understand ML feature engineering.\u003c\/p\u003e\n  \u003ch4\u003eWhat's in this kit\u003c\/h4\u003e\n  \u003cul\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/mega-2560-r3-development-board-arduino-compatible-54-io-256kb\"\u003eArduino Mega 2560\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/adxl345-3-axis-accelerometer-module-16g-i2cspi\"\u003eADXL345 Accel\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/elecrow-24inch-esp32-solo-miner-lcd-display\"\u003e2.4in TFT ILI9341\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/ds3231-real-time-clock-module-i2c-rtc-with-battery-backup\"\u003eDS3231 RTC\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/microsd-card-reader-spi-module-for-arduino\"\u003eMicroSD Module\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/resistor-variety-pack-100-pcs-10-values-14w-carbon-film\"\u003e4.7k? Resistors\u003c\/a\u003e x5\u003c\/li\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/capacitor-variety-pack-6-values-100nf-to-470uf-30-pieces\"\u003e100nF Caps\u003c\/a\u003e x5\u003c\/li\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/esp-wroom-32-breakout-board-pcb-55x52mm\"\u003ePCB Prototype Board\u003c\/a\u003e x2\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/9v-battery-snap-connector-with-15cm-wires-compoden\"\u003e9V Battery Snap\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/soldering-kit-25w-with-solder-wire-flux-paste-compoden\"\u003eSoldering Iron\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/soldering-kit-25w-with-solder-wire-flux-paste-compoden\"\u003eSolder Wire\u003c\/a\u003e\u003c\/li\u003e\n  \u003c\/ul\u003e\n\u003c\/div\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What is included in the Machine Learning Feature Visualiser Kit with Arduino Mega?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The Machine Learning Feature Visualiser Kit with Arduino Mega includes all components needed: Arduino Mega 2560, ADXL345 Accel, 2.4in TFT ILI9341, DS3231 RTC, MicroSD Module and more. Everything is pre-tested for compatibility and shipped from Bengaluru, India.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What skill level is required for the Machine Learning Feature Visualiser Kit with Arduino Mega?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Advanced level makers, suitable for ages 18-21. ADXL345 features (mean, std, FFT bins) visualised as radar chart. Helps understand ML feature engineering. Estimated build time is 5-6 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the Machine Learning Feature Visualiser Kit with Arduino Mega online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the Machine Learning Feature Visualiser Kit with Arduino Mega is available online at Compoden (compoden.in), India's AI-powered electronics and robotics store. Ships from Bengaluru in 1-5 business days across India.\"\n      }\n    }\n  ]\n}\n\u003c\/script\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"Product\",\n  \"name\": \"Machine Learning Feature Visualiser Kit with Arduino Mega\",\n  \"description\": \"ADXL345 features (mean, std, FFT bins) visualised as radar chart. Helps understand ML feature engineering.\",\n  \"sku\": \"CDN-KIT-2069\",\n  \"brand\": {\"@type\": \"Brand\", \"name\": \"Compoden\"},\n  \"offers\": {\n    \"@type\": \"Offer\",\n    \"url\": \"https:\/\/compoden.in\/products\/kit-machine-learning-feature-visualiser\",\n    \"priceCurrency\": \"INR\",\n    \"price\": \"3915\",\n    \"availability\": \"https:\/\/schema.org\/InStock\",\n    \"seller\": {\"@type\": \"Organization\", \"name\": \"Compoden\"}\n  },\n  \"category\": \"Data \u0026 Visualization\"\n}\n\u003c\/script\u003e\u003cp\u003e\u003cstrong\u003eChoose your assembly option:\u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eSoldering Kit\u003c\/strong\u003e - 25W soldering iron, 60\/40 solder wire, flux, and small perfboard for permanent assembly.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eBreadboard Combo\u003c\/strong\u003e - 800-point full-size breadboard with 65-piece jumper wire pack for solderless prototyping.\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Compoden","offers":[{"title":"Clone \/ Soldering Kit","offer_id":53469310878061,"sku":"CDN-KIT-2069-CL-SLD","price":3240.0,"currency_code":"INR","in_stock":true},{"title":"Clone \/ Breadboard Combo","offer_id":53469310910829,"sku":"CDN-KIT-2069-CL-BB","price":2710.0,"currency_code":"INR","in_stock":true},{"title":"R3 Original \/ Soldering Kit","offer_id":53469310943597,"sku":"CDN-KIT-2069-R3-SLD","price":5680.0,"currency_code":"INR","in_stock":true},{"title":"R3 Original \/ Breadboard Combo","offer_id":53469310976365,"sku":"CDN-KIT-2069-R3-BB","price":5140.0,"currency_code":"INR","in_stock":true},{"title":"R4 WiFi \/ Soldering Kit","offer_id":53469311009133,"sku":"CDN-KIT-2069-R4-SLD","price":4990.0,"currency_code":"INR","in_stock":true},{"title":"R4 WiFi \/ Breadboard Combo","offer_id":53469311041901,"sku":"CDN-KIT-2069-R4-BB","price":4460.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0999\/3997\/5533\/files\/kit-machine-learning-feature-visualiser.png?v=1781947650","url":"https:\/\/compoden.com\/products\/ml-feature-visualiser-kit-with-arduino-mega-radar-chart-adxl345","provider":"Compoden","version":"1.0","type":"link"}