Erdem Taha's Projects
A project that uses Random Forest for descriptive breast cancer diagnosis, classifying breast tumors as malignant or benign.
Breast Cancer Diagnosis using SVM: A Python project for classifying tumors as malignant or benign based on tumor features with a Support Vector Machine.
Breast cancer classification using Decision Tree. Practice machine learning skills. Achieve 90.6% accuracy. Informative project for ML enthusiasts.
Using Naive Bayes for tumor classification in medical images. Great for healthcare & data science. Python & scikit-learn powered
Creating a logistic regression algorithm without using a library and making cancer classification with this algorithm model (Kaggle Explained)
Identifying and marking circles in images with OpenCv
Making cancer classification with knn module (Kaggle Expression)
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.
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.
"Decision tree regression applied to ticket pricing. Visualized scatter data points and regression line
There is a review and application of the methods of Detection Operations
Dicord Bot
Face detection and sketching
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.
Human-detection-and-Tracking
This project is a demo that applies PCA (Principal Component Analysis) analysis on the Iris dataset using Python and the Scikit-learn library. PCA is utilized to reduce high-dimensional data to lower dimensions.
Logistic Regression for Cancer Data Classification: Achieve 96.50% accuracy in benign vs. malignant cell classification.
Utilize K-Nearest Neighbors (K-NN) for precise benign and malignant cancer cell classification in our Cancer Data Classification project.
This repository contains the code for a K-Nearest Neighbors (KNN) model built to classify customer segments in Türkiye using the teleCust1000T dataset. The project includes data cleaning, visualization, feature scaling, model training, and evaluation with accuracy metrics.
The project that enables to identify and follow the yellow tracking lanes at the corners of the highways
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.
Here are the Machine Learning structures
Beginner and Advanced Machine Learning Notes
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.
Object Oriented Programming Notes Py
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.
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.
Lesson and Project Notes for OpenCv Library From Beginner to Difficult Level
The OpenCv TrackBar application is shown here
Here the videos were edited with the OpenCv library
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent