527 skills found · Page 6 of 18
Hazrat-Ali9 / R Programming🏀 R Programming ⚽ the go-to 🎳 language 🥎 data science 🪀statistical analysis 🏉 and data 🎮 visualization 🏈 Perfect data 🦁 analysts 🏬 researchers and 🐯 aspiring data 🦊 scientists statistical 🐲 modeling hypothesis 🐳 testing and 🐠 regression analysis 🚂 machine learning 🚞 in R with 🚢 caret random ✈ Forest 🚁 and xgboost☂
zbchern / Awesome Machine Learning ReliabilityA curated list of awesome resources regarding machine learning reliability.
NITHISHKUMAR-C / CODSOFT CREDIT CARD FRAUD DETECTIONBuild a machine learning model to identify fraudulent credit card transactions. Preprocess and normalize the transaction data, handle class imbalance issues, and split the dataset into training and testing sets.
MPA2suite / K SRMEHeat-conductivity benchmark test for foundational machine-learning potentials
gangeshbaskerr / Phishing Website DetectionA project that predicts a phishing URL by extracting 17 features in 3 different categories and then train and test the machine learning models using a dataset from Phishtank.
shantanu1109 / IBM HR Analytics Employee Attrition And Performance PredictionIn this project, we enlisted the numerical and categorical attributes present in the publicly available dataset. Missing values were dropped to give better insights in data analysis. ANOVA and Chi-Square tests were carried out during statistical analysis. Machine Learning algo's were applied to understand, manage, and mitigate employee attrition.
sharath573 / Object Recognition For Autonomous Driving System MATLAB ProjectThe goal of this project is to provide object detection and information on environment model on traffic activity which helps autonomous vehicles or surveillance systems. Computer vision is an essential component for autonomous scars. Accurate detection of vehicles, street buildings, pedestrians, and road signs could assist self-driving cars the drive as safely as humans. However, object detection has been a challenging task for years since images of objects in the real-world environment are affected by illumination, rotation, scale, and occlusion. A unified object detection model, You Only Look Once (YOLO), is used which could directly regress from the input image to object class scores and positions. In this project, we applied YOLO to two different datasets to test its general applicability. We fully analyzed its performance from various aspects on KITTI data set which is specialized for autonomous driving. We proposed a novel technique called memory map, which considers inter-frame information, to strengthen YOLO's detection ability in the driving scene. We broadened the model's applicability scope by applying it to a new orientation estimation task. For this project objective is to provide a information of quality and environmental model on traffic activity and to signal potentially anomalous situation and also apply various machine learning models for object detection such as SVM and CNN and compare and contrast the results with YOLO .
toybox-rs / ToyboxThe Machine Learning Toybox for testing the behavior of autonomous agents.
KwFung7 / Trading Bot[Testing on dummy ac] Using Oanda API for current forex data and order creation. Fetched data used for machine learning model training (SVR) to predict future ask price.
mistersharmaa / BreastCancerPredictionBreast cancer has the second highest mortality rate in women next to lung cancer. As per clinical statistics, 1 in every 8 women is diagnosed with breast cancer in their lifetime. However, periodic clinical check-ups and self-tests help in early detection and thereby significantly increase the chances of survival. Invasive detection techniques cause rupture of the tumor, accelerating the spread of cancer to adjoining areas. Hence, there arises the need for a more robust, fast, accurate, and efficient non-invasive cancer detection system. Early detection can give patients more treatment options. In order to detect signs of cancer, breast tissue from biopsies is stained to enhance the nuclei and cytoplasm for microscopic examination. Then, pathologists evaluate the extent of any abnormal structural variation to determine whether there are tumors. Architectural Distortion (AD) is a very subtle contraction of the breast tissue and may represent the earliest sign of cancer. Since it is very likely to be unnoticed by radiologists, several approaches have been proposed over the years but none using deep learning techniques. AI will become a transformational force in healthcare and soon, computer vision models will be able to get a higher accuracy when researchers have the access to more medical imaging datasets. The application of machine learning models for prediction and prognosis of disease development has become an irrevocable part of cancer studies aimed at improving the subsequent therapy and management of patients. The application of machine learning models for accurate prediction of survival time in breast cancer on the basis of clinical data is the main objective. We have developed a computer vision model to detect breast cancer in histopathological images. Two classes will be used in this project: Benign and Malignant
Kavan-Patel / Fruits And Vegetable Detection For POS With Deep LearningThe self-checkout portal at the supermarket gives the idea to the classification of fresh fruits and vegetables. Nowadays, more self-checkout portals are added to the time saving of the customers at the supermarket. When it comes to fresh fruits and vegetables, It still needs to enter manually into the computer for purchase and, it is a bit time-consuming and increases cheating (By putting the wrong item name). We can make use of the camera at self-checkout to get a prediction of the items using machine learning. For example, when we put tomatoes on the counter, it detects the tomatoes through a semi-transparent bag and gives various tomatoes as a list. The problem with different object detection models is to see through semi-transparent bags to classify the image. You Only Look Once (YOLO) object detection did this job well If we train the model correctly. The main stages of Object detection are data acquisition, Augmentation, Model training, Model Evaluation, and Deployment. It gives 99.4% max accuracy on the training and testing dataset to classify 14 different classes for fruits and vegetables. On real-life images, it provides approx 90% accuracy on images to classify. Prediction execution performs under a sec is considered a good result for the self-checkout terminal.
JanaJarecki / CognitivemodelsAn R software package to write, train, tune, test, and compare machine-learning models of cognition
qbarthelemy / PyPermutPython package for permutation tests, for statistics and machine learning.
BioinfoMachineLearning / CryoVirusDBA dataset of labeled virus particles in cryo-EM micrographs (images) for training and testing machine learning methods of virus particle picking
Kunal30 / Non Intrusive Attendance Marking System Using AIThe project that we worked on this summer internship falls in the domain of research in IoT (Internet of Things). Initially, the mentor asked us to find real-life problems, which we would attempt to solve by using the tools of Information Technology. We were allowed to discuss and work in a group of three. We picked the problem of devising an attendance monitoring system, which would mark the presence of the students in a big room, in a non-intrusive manner using image recognition, for e.g. an auditorium or our college’s lecture theatre. Our project was divided into two phases, which would be illustrated in the subsequent passages. The first phase involved doing a literature survey on the tools and technologies through various authentic research papers and the existing libraries, which would enable us to devise a backend structure for our project. We, then developed a flowchart, which comprised of two modules of processes, through which the procedure would pass through. The first module involves the initial training of a machine learning based classifier by training it with the various images of a specific person. The second module involves the testing part in the real environment, which involves face detection and face recognition. A camera would take the frames/image of a live audience. Then, these frames would be pre-processed (involves grey-scaling and image resizing) for achieving better performance in the subsequent face detection module. The face-detection algorithm would detect all the faces present in the frame, and would crop the detected faces, and would pass them to the face recognition classifier for testing. The classifier would classify the cropped images and would mark the attendance accordingly. The libraries used for face-detection were that of OpenCV, and a convolutional neural network was trained for the image recognition part. The libraries which were used for training the convolutional neural network was Keras. The second phase involved the implementation part, where we had to gather the data for training the neural network, and find out the parameters of the image, for which we are getting better accuracy performance. We trained the neural network with the images of about 64 students, with about 20 images per student, covering different angles and brightness levels. We trained the network with 70 percent of the image corpus, and used the remaining 30 percent for testing. We got an accuracy of 93 percent. For testing the face detection part, we took a video of a classroom of about 40 students. Then, we generated frames from the video and passed it to the face detection algorithm. We extrapolated that the accuracy of an individual frame was not that high, but if we consider all the detected members in all the frames, we are covering almost every student. Hence, considering multiple frames for testing is crucial to get a high detection accuracy. We are currently trying to figure out the camera and its mounting position, which would be conducive for the algorithm, to give us accurate results.
Nemshan / Predicting Paid Amount For Claims DataIntroduction The context is the 2016 public use NH medical claims files obtained from NH CHIS (Comprehensive Health Care Information System). The dataset contains Commercial Insurance claims, and a small fraction of Medicaid and Medicare payments for dually eligible people. The primary purpose of this assignment is to test machine learning (ML) skills in a real case analysis setting. You are expected to clean and process data and then apply various ML techniques like Linear and no linear models like regularized regression, MARS, and Partitioning methods. You are expected to use at least two of R, Python and JMP software. Data details: Medical claims file for 2016 contains ~17 millions rows and ~60 columns of data, containing ~6.5 million individual medical claims. These claims are all commercial claims that were filed by healthcare providers in 2016 in the state of NH. These claims were ~88% for residents of NH and the remaining for out of state visitors who sought care in NH. Each claim consists of one or more line items, each indicating a procedure done during the doctor’s visit. Two columns indicating Billed amount and the Paid amount for the care provided, are of primary interest. The main objective is to predict “Paid amount per procedure” by mapping a plethora of features available in the dataset. It is also an expectation that you would create new features using the existing ones or external data sources. Objectives: Step 1: Take a random sample of 1 million unique claims, such that all line items related to each claim are included in the sample. This will result in a little less than 3 million rows of data. Step 2: Clean up the data, understand the distributions, and create new features if necessary. Step 3: Run predictive models using validation method of your choice. Step 4: Write a descriptive report (less than 10 pages) describing the process and your findings.
VincentGranville / Experimental Math Number TheoryResearch on number theory conjectures, leading to the development of new machine learning techniques, initially applied to math problems before being tested on real-life problems.
czhu12 / Light Bulb💡Light Bulb is a tool to help you label, train, test and deploy machine learning models without any coding.
ptyadana / Python Projects DojoCollections of python projects including machine learning projects, image and pdf processing, password checkers, sending emails, sms, web scraping,flask web app,selenium automation testing,etc
hendersontrent / CorrectRR package for computing corrected test statistics for comparing machine learning models on correlated samples
