Customer Segmentation using K-Means Clustering
This project implements customer segmentation using the K-Means clustering algorithm in Python. It analyzes customer datasets to group similar customers based on key features such as purchase history, demographics, spending patterns, and behavioral data. The implementation includes comprehensive data preprocessing, feature scaling, optimal cluster determination using the elbow method, model training and evaluation, and interactive visualizations of the resulting customer segments. or data manipulation, matplotlib and seaborn for data visualization
Iris Flower Classifier
This project implements a machine learning classification system for Iris flower species identification. It includes exploratory data analysis and visualization of feature relationships in the dataset. Multiple algorithms are implemented and evaluated, including KNN, Logistic Regression, and Decision Trees, with hyperparameter tuning for optimal performance. The system features a real-time web application built with Streamlit for user input and predictions. Technologies used include Python, scikit-learn, NumPy, Pandas, and Jupyter Notebook.
Smart RGB LED System using ESP32
A compact ESP32-based lighting solution that responds to both movement and ambient brightness. The system uses a PIR sensor to detect motion and an LDR sensor to measure light levels, then controls a relay and RGB LED output based on those conditions. It is designed to activate lighting only when necessary and to indicate system state clearly using color feedback.
Smart Trash Bin
This project creates an automatic smart trash bin using Arduino. It features ultrasonic sensor for hand detection to open the lid automatically, OLED display for status information, and buzzer for audio feedback including Mario tunes. The lid closes after a set delay. Hardware includes Arduino Uno, Ultrasonic Sensor (HC-SR04), Servo Motor, Buzzer, and OLED Display (SSD1306 I2C).