Corner-Detection-Method
This Python script demonstrates the Shi-Tomasi corner detection method using OpenCV. Shi-Tomasi corner detection is a feature detection technique that identifies distinctive points or corners in an image. It is often used in computer vision applications for tasks like feature matching, object tracking, and image stitching.Face-Detection-And-Sketching
Face detection and sketchingHough-Circle
This Python code utilizes OpenCV to detect and draw circles in an image. It applies grayscale conversion and median blur to reduce noise, then employs the Hough Circle Transform for circle detection. Detected circles are highlighted in red on the image.Multiply-Linear-Regression
A Python code for data analysis and salary predictions using a multiple linear regression model. The code calculates the intercept and coefficients of the model and makes predictions on sample data.Cancer_Data_Classification_LogisticRegression
Creating a logistic regression algorithm without using a library and making cancer classification with this algorithm model (Kaggle Explained)Machine-Learning
Here are the Machine Learning structuresDicord-Bot
Dicord BotPython-Mini-Projects
Real-Time-Shape-Detection
Multiplex identification program on webcam by changing HSV settings with OpenCvLinear-Regression
This Python code represents a machine learning project that builds a simple linear regression model using experience and salary data. It plots the data, constructs the regression model, and visualizes the results.OpenCv-Tracbar-Code
The OpenCv TrackBar application is shown herePolynomial-Linear-Regression
Applying Polynomial Regression to Improve Predictive Accuracy in Nonlinear Data Modeling, achieving more accurate resultsRandom-Forest-Grandstand-Price
This Python script employs a Random Forest Regressor to predict prices based on 'Grandstand Level.' It's versatile and delivers accurate results.Breast-Cancer-SVM
Breast Cancer Diagnosis using SVM: A Python project for classifying tumors as malignant or benign based on tumor features with a Support Vector Machine.Decision-Tree-Regression-Grandstand-Price
"Decision tree regression applied to ticket pricing. Visualized scatter data points and regression lineBreast-Cancer-RF-Classification
A project that uses Random Forest for descriptive breast cancer diagnosis, classifying breast tumors as malignant or benign.Contours-Convex-Hull
Contours and Convex Hull are crucial concepts in computer vision. Contours outline object boundaries in images, while Convex Hull simplifies shapes for efficient analysis and object recognition.Cancer_Classification_NaiveBayes
Using Naive Bayes for tumor classification in medical images. Great for healthcare & data science. Python & scikit-learn poweredOpenCV-Projects
Lesson and Project Notes for OpenCv Library From Beginner to Difficult LevelOpenCv-video-processing-Code
Here the videos were edited with the OpenCv libraryDetection-Processes
There is a review and application of the methods of Detection OperationsVoice-Asisstant
Python voice assistantPrometheussx
Predicting_Median_Home_Values_in_Boston_with_Regression_Trees
Python code predicts real estate prices using Decision Tree Regression on features like bedrooms, square footage, and location. Well-documented and beginner-friendly for learning about real estate price prediction.Circle-Detection
Identifying and marking circles in images with OpenCvLane-Tracking-App
The project that enables to identify and follow the yellow tracking lanes at the corners of the highwaysMachine-Learning-Notes-Py
Beginner and Advanced Machine Learning NotesObject-Oriented-Programming-Notes-Py
Object Oriented Programming Notes PyClassification-Cancer-Data-With-K-NN
Making cancer classification with knn module (Kaggle Expression)shape-detection
Determines Polygons According to the Number of Edges with OpenCVReal_Time_HSV_Object_Detection
this structure allows us to separate the object colors from the photo and make object separation thanks to the masking of HSV colors with trackbar valuesCancer-Classification-DecisionTree
Breast cancer classification using Decision Tree. Practice machine learning skills. Achieve 90.6% accuracy. Informative project for ML enthusiasts.Sign-Language-Classification-Tutorial
This project utilizes logistic regression to classify numbers 0 and 1 using sign language gestures. It successfully achieves the task of sign language classification, reaching a test accuracy of 93.54%.Thresholding-Methods
This script demonstrates three essential image thresholding techniques: global thresholding, adaptive mean thresholding, and adaptive Gaussian thresholding, aiding image analysis and segmentation in your projects.OpenCV-Line-Detection-Project
This Python code employs OpenCV for efficient line detection in an image. It reads, processes, and visualizes lines, making it a valuable tool for computer vision applications.Kaggle-Prediction-Cancer-Data-With-K-NN-Acc-95
Utilize K-Nearest Neighbors (K-NN) for precise benign and malignant cancer cell classification in our Cancer Data Classification project.knn-customer-segmentation
This repository contains the code for a K-Nearest Neighbors (KNN) model built to classify customer segments in Tรผrkiye using the teleCust1000T dataset. The project includes data cleaning, visualization, feature scaling, model training, and evaluation with accuracy metrics.Patient-Profile-Based-Medication-Recommendation-System-Decision-Tree-Analysis
This project involves a drug recommendation system based on patients' demographic characteristics. The dataset includes characteristics such as age, gender, blood pressure (BP), cholesterol level and sodium-potassium ratio. The project involves building a decision tree using `DecisionTreeClassifier` and making drug recommendations using this tree.Kaggle-Notebook-Cancer-Prediction-ACC96.5-With-Logistic-Regression
Logistic Regression for Cancer Data Classification: Achieve 96.50% accuracy in benign vs. malignant cell classification.Object-Tracking-Dog
Here we will process the visual tracking of an object determined by color contours and differences. In the video used here, we will create a visual tracking of a dog that is different from the general color contrast.Love Open Source and this site? Check out how you can help us