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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 sketchingMultiply-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 TΓΌrkiye 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