Data classifiers työt

...my project with the main objective of implementing predictive analysis. The data available for the project is largely categorical in nature. The ideal candidate should possess: - Mastery in Python - Experience working with categorical data - Demonstrated expertise in building predictive models using Machine Learning algorithms The mission at hand involves using Python, the preferred programming language for this project, to design an effective analysis solution. The key objective here is to convert raw data into a format that can be better interpreted for decision-making with the help of predictive analysis. The right professional for this project should further understand and interpret complex categorical data and effectively use it to build accurate an...

...talented clothing designer who can cater to my need for beach microfibre changing robes. Here's what I'm after: - Design for casual beachwear changing robes, blending function and style. - Use a variety of print and color themes. These range from basic colors to fun beach designs, including both geometrical and floral prints. - Craft designs that appeal to the vibrant, serene, and earthy tones classifiers. Ideally, you'll have experience in fashion design, particularly in beachwear or casual wear. Proven ability and a knack for working with varying colour and print themes will be a major plus. The designs must feature the brand name in a tasteful design. we want a template for this product that we can repeat on different colours and prints. Please find attached ...

...involve: - Developing a solution for posture detection and face recognition. - Collecting and recording data related to the detected postures into a CSV file. These are just an example papers Some code: 1). **Moved Left/Moved Right:** - Implement modules to detect instances where individuals in the video move left or right, capturing directional movements. 2) **Standing/Sitting Detection:** - Develop classifiers to distinguish between instances where individuals are standing or sitting in a group of 10 to 15 people

I am seeking an experienced coder who can expertly synchronize my uncategorized product divisions to align with a specific website (to be determined). Key responsibilities include: - Reviewing my current product categories - Identifying differences with the target website - Updating our product classifiers to achieve coherence As this task requires tinkering with the minutia, prior experience is beneficial. If you've handled a similar project before or have a keen understanding of product categorization, you might be the perfect fit for the job. Please detail your experience in your application. Let's optimize our product categories now, for a more organized and easy-to-navigate website tomorrow.

We are looking to create an application that allows the user to select a directory on their computer. Once they select the directory the application will search all the images in the directory and Identify the following animals in the image. 1. Deer 2. Bear 3. Turkey 4. Human Once the images are processed the user can select one of the animals a...a directory on their computer. Once they select the directory the application will search all the images in the directory and Identify the following animals in the image. 1. Deer 2. Bear 3. Turkey 4. Human Once the images are processed the user can select one of the animals above and it will display all images identified. This application needs to be available offline so any cascades or classifiers would have to be saved in the ap...

Writing Python code (by colab) 9 Models: 1-Decision Tree Classifiers 2-K-Nearest Neighbors 3-Gaussian Naive Bayes 4-ANN 5-SVC 6-LogisticRegression 7-RandomForest Classifier 8-AdaBoostClassifier 9-XGBoost) the link to the dataset will be sent later.

...will implement two different naive Bayes approaches. In class we have considered document representations based on a bag-of-words. In this Application our “documents” 1 2 scikit-learn () is a popular tool for doing many machine learning tasks in Python. It includes implementations of many classifiers (including naive Bayes, but we’re implementing it ourselves in this Application ). 1 ...................................................... requirements details in attached section You will submit: 1)Complete source code zip file 2)Output running video of this application 3)Report 4)write details code comments Remember: you can't do anything outside this requirements . Must be complete all

...neutral, positive, and very positive. Hence BERT model is more efficient and can process extensive text data and perform fine-grained SA on the Twitter data of the ICC Men’s Cricket World Cup -2022 for two countries, India and Pakistan. This research project aims to extract people's sentiments and opinions using the BERT model from the Twitter data of the ICC Men’s Cricket World Cup -2022 for two countries, India and Pakistan. The BERT model converts words or text into numbers by leveraging them and using them to train a machine learning (ML) model to predict what the people of both countries think before and after the game. These predictions can be made by different ML and NLP classifiers ...

...Information to find patterns that will help improve our company. We will rely on you to build data products to extract valuable Business Insights, Invoice based PDF, Bills, Bank receipts, Tables data etc.  Develop state-of-the-art algorithms in one or all of the following areas: prompt engineering for LLM models, fine-tuning models, training open source models, large-scale distributed training, Conduct research, design, implement, optimize, and deploy deep learning models Work on comparing and bench marking the performance of different models. In this role, you should be highly analytical with a knack for analysis, Math and Statistics. Critical thinking and problem-solving skills are essential for interpreting data. We also want to see a passion for AI,Machin...

...dataset for this project should preferably be related to COVID-19. Furthermore, the selected developer must create a video tutorial in English or Arabic, detailing the implementation of the code and showcasing how the hybrid models operate. ** Project Description: The selected developer will undertake the following tasks: 1. Data Collection and Preprocessing: - Identify or acquire a relevant dataset related to COVID-19 for the topic classification task. - Preprocess the data to ensure its suitability for unsupervised NLP tasks. 2. Unsupervised Model Implementation: - Utilize unsupervised NLP techniques such as BerTopic, NMF, and LDA to develop individual topic classification models. - The learning methods for all models, including hybrid models, should be unsupervise...

I am looking for a Python data scientist who can assist me with various data analysis tasks using the Pandas library. The ideal candidate should have experience in data cleaning and preprocessing, exploratory data analysis, and statistical modeling and hypothesis testing. Specifically, I need help with: - Data cleaning and preprocessing to ensure the dataset is accurate and ready for analysis - Exploratory data analysis to understand the patterns and relationships within the dataset - Statistical modeling and hypothesis testing to make data-driven decisions and draw conclusions I already have a specific dataset that I would like the freelancer to use for the analysis. The expected timeline for the project is 1-2 weeks. The corporation &...

I am looking for a skilled C# developer to create a class that can detect eye pupils from pre-existing images. The images will be of medium resolution, ranging between 1000-2000 pixels. The algorithm should be designed to work specifically for desktop applications. The ideal candidate for this project should have experience with image analysis and C# programming. The project does not requi...pre-existing images. The images will be of medium resolution, ranging between 1000-2000 pixels. The algorithm should be designed to work specifically for desktop applications. The ideal candidate for this project should have experience with image analysis and C# programming. The project does not require real-time detection and only requires a C# class that does the job. You can use existing classifi...

can you help me with my machine learning assignment, It's a simple assignment about- you have to download a dataset of tweets describing US airlines. (Note: she will need to create a free account on Kaggle to be able to download the data.) and then You should create a Google Colab notebook, upload the data, extract the tweets and class labels (positive/negative/neutral) from the CSV file and then train (and later evaluate) one or more linear classifiers (e.g. Logistic regression on an SVM) on the text as we did in the second tutorial.

Ensemble of XLM-R classifiers [Python Code] I am looking for a freelancer who has past experience in text classification. The purpose of this project is to create an ensemble of XLM-R classifiers with an accuracy level of 75% and above. The training data is a parallel corpora in two languages and the model should make predictions on test data in either language. The programmer should suggest an appropriate ensemble method to boost the performance. Ideal skills and experience for this project include: - Proficiency in Python code - Experience in text classification - Knowledge of XLM-R classifiers - Ability to work with ensemble methods The successful freelancer will be expected to provide a detailed project proposal outlining their approach to achievi...

The work need to build classifiers using the techniques. At the very minimum, you need to produce a classifier for each method (DT, KNN, RF, SVM and NN). However, if you explore the problem very thoroughly, preprocessing the data, looking at different methods, choosing their best parameter settings and identifying the best classifier in a principled and explainable way, will be needed. If you choose to use KNIME and you show 'expert' use (i.e. exploring multiple classifiers, with different settings, choosing the best in a principled way and being able to explain why you built the model the way you did), optimize and test different models, this will be better.

I am trying to implement Haar classifiers algorith (Haar cascade detection) to develop an algorithm for facial/object recognition. I would like the developed mathematical model to be simulated in matlab. I need the algorithm not report writing

I am trying to implement Haar classifiers algorith (Haar cascade detection) to develop an algorithm for facial/object recognition. I would like the developed mathematical model to be simulated in matlab. The use of Haar classifiers along with explanation is essential.

I have a dataset, the data set is about labeled users' reviews. I want to apply this to preprocessing of the dataset. Split the dataset into testing data and actual data, then input the data into the next stage which uses traditional techniques for word embedding and traditional classifiers as below. Apply on all Datasets TF-IDF and Random Forest Bag of Words and Random Forest TF-IDF and naive bayes Bag of Words and naive bayes

This work will involve comparison of the LDA, Decision Tree, and SVM (linear kernel) classifiers as implemented in scikit-learn

I need python expert having good knowledge about implementing a speed design detector and classifiers Only expert no middle man and outsourcer person

...You may want to tweak your preprocessing code to deal with particularities of tweets, e.g. #hashtags or @user mentions. Exercise guidelines • Data: The training, development and test sets can be downloaded from the module website (). This compressed archive includes 5 files, one that is used for training () another one for development () and another 3 that are used as different subsets for testing (twitter-test[1-3].txt). You may use the development set as the test set while you are developing your classifier, so that you tweak your classifiers and features; the development set can also be useful to compute hyperparameters, where needed. The files are formatted as TSV (tab-separated-values)

- You should be familiar with classifiers such as support vector machines and random forest algorithms - Well versed in python - Understand t-SNE and related algorithms and able to implement those in python

...like an AI tool to help identify what category of learning is going on in the video - which will help towards determining the "microcategory" of the clip/video. During uploading of videos to howclip, there is an area which requires users to select or add "microcategories" which is a parent/child multi-level category management tool There are many AI solutions for computer vision classifiers. We want to be able to claim we are an AI powered website for venture capital interest. Adding AI to elearning video platform - computer vision microcategory classifier process for microlearning videos These are some of the potential tools that can be used to implement (we are open to the solution) - 1. (

... During uploading of videos to howclip, there is an area which requires users to select or add "microcategories" which is a parent/child multi-level category management tool We would like an AI tool to help identify what category of learning is going on in the video - which will help towards determining the "microcategory" of the clip/video. There are many AI solutions for computer vision classifiers. We want to be able to claim we are an AI powered website for venture capital interest. These are some of the potential tools that can be used to implement (we are open to the solution) - 1. () 2. AWS 3.

... During uploading of videos to howclip, there is an area which requires users to select or add "microcategories" which is a parent/child multi-level category management tool We would like an AI tool to help identify what category of learning is going on in the video - which will help towards determining the "microcategory" of the clip/video. There are many AI solutions for computer vision classifiers. We want to be able to claim we are an AI powered website for venture capital interest. These are some of the potential tools that can be used to implement (we are open to the solution) - 1. () 2. AWS 3.

...During uploading of videos to howclip, there is an area which requires users to select or add "microcategories" which is a parent/child multi-level category management tool We would like an AI tool to help identify what category of learning is going on in the video - which will help towards determining the "microcategory" of the clip/video. There are many AI solutions for computer vision classifiers. We want to be able to claim we are an AI powered website for venture capital interest. These are some of the potential tools that can be used to implement (we are open to the solution) - 1. () 2. AWS 3.

The proposed framework aims to create a machine learning-based malware detection system for IOS and Android in order to detect malware applications and improve smartphone users' security and privacy. This system uses machine learning classifiers to classify whether an application is good ware or malware by monitoring numerous behavioural patterns.

The proposed framework aims to create a machine learning-based malware detection system for IOS and Android in order to detect malware applications and improve smartphone users' security and privacy. This system uses machine learning classifiers to classify whether an application is good ware or malware by monitoring numerous behavioural patterns.

You will implement K-Nearest-Neighbor (kNN), Centroidbased Rocchio, Naive Bayes (NB) and Support Vector Machine (SVM) classifiers from scratch (that is, without using scikit-learn APIs) in Python. You will use a subset of the 20 Newsgroups dataset. The full data set contains 20,000 newsgroup documents, partitioned (nearly) evenly across 20 different newsgroups and has been used for experiments in text applications of machine learning techniques, such as text classification and text clustering. This assignment dataset contains a pre-processed subset of 1000 documents and a vocabulary (dictionary) of 5,500 terms.

...this study, we aim to investigate the potential of NSCLC histology classification into AC and SCC by applying different feature extraction and classification techniques on pre-treatment CT images. The employed image dataset (102 patients) was taken from the publicly available cancer imaging archive collection (TCIA). We investigated four different families of techniques: (a) radiomics with two classifiers (kNN and SVM), (b) four state-of-the-art convolutional neural networks (CNNs) with transfer learning and fine tuning (Alexnet, ResNet101, Inceptionv3 and InceptionResnetv2), (c) a CNN combined with a long short-term memory (LSTM) network to fuse information about the spatial coherency of tumor’s CT slices, and (d) combinatorial models (LSTM + CNN + radiomics). In addition,...

recommended to use Python language on the ROS environment, and any Python library of your choice for the classifiers.

Need to write a technical report about automatic waste image classification using machine learning classifiers. The project is already created. The report should have: 1. Introduction 2. Background 3. Methodology or Approach 4. Project Design 5. Implementation 6. Testing 7. Evaluation Word count: 10.000 words Full details uploaded in the docs.

Requirements, A WEB BASED UI SHOULD BE THERE TO CHOOSE AN IMAGE OR UPLOAD, AFETR UPLOADING THE IMAGE THE IMAGE SHOULD BE POSSESSED ALL THE STEP OF MACHINE LEARNING THAT'S IS PREPROCESSING, SEGMENTATION, FEATURES EXTRACTION AND SVM FOR CLASSIFIERS. FINAL OUTPUT: I WILL UPLOAD LEAF IMAGE BY BROWSER AND IT WILL GIVE FINAL DISEASE OF LEAF AND ACCURACY.

The project will require you to perform data pre-processing that is needed for each dataset that will be used in this project. Thereafter, you will be required to build models for each of the two classifiers and optimize them for accuracy. Finally, you will experiment with different data distributions and then compare the two classifiers in terms of stability of their model accuracy. The following tasks detail the requirements of this project.

The project will require you to perform data pre-processing that is needed for each dataset that will be used in this project. Thereafter, you will be required to build models for each of the two classifiers and optimize them for accuracy. Finally, you will experiment with different data distributions and then compare the two classifiers in terms of stability of their model accuracy.

Hey Tev, I need you to do 2 tasks for me. Task 1 is analyze this dataset with several classifiers and compare the results at the end. For task 2, use gym library and create me an environment of Mario. Can you do it Tev?

...discovered while I was sorting that there are actually more than 20 different labels, there are parts I didn’t know about that only occur once or twice in five hundred parts. I would like to be able to identify and handle parts like this separately. I am using to classify the images (parts). I get about a 96% accuracy of a part being identified correctly when I use two different classifiers to look at the same image and then check their agreement. I do this because I would rather reject parts than have them sorted incorrectly. My images are 300x300. I look at the weight of the prediction, and 95% of my predictions are almost certain, say .99999, so I haven’t been able to do the simple thing of just looking at the strength of the prediction. In case this ...

To develop a model that will predict accurately using python. Classification on multiclass and many features. Performance based on various classifiers and a hybrid of the best.

I have a data and paper i want to make a report with a complete information

...machine learning in my spare time, so this is gonna help me in my learning journey. You can use any text classifier (naive bayes, SVM, GBoosting, clustering) as long as it's good enough. It would be a dream if the model was a pre-trained model like BERT or XLNet, but it's not a requirement. Also, it'll be nice if you compare at least 2 classifiers. The dataset consists of social media posts and it's structured (xlsx) and labelled (by human classification). The data requires minimum cleaning I think. Please, note that I'll study the code, so you'll need to explain the steps in the notebook provided. I have a fairly good knowledge about machine learning, more conceptual than practice. P.S.: I have a limited budget! Also, the completion shoul...

i want a LR about facial expression classifiers, and i want to conclude the SVM and CNN has a high and good accurecy . the LR from two pages 1.5 space and has the table summrize the all LR at the end the file as example

Train the classifier on expressions from user A. Use an appropriate classification method among those you know (e.g. hard SVM, soft SVM...(training + testing) by inverting the roles of the user (i.e., training on user B and testing on user A), and comment on the results g. Repeat the analysis (training + testing) by considering a different feature representation than the original landmark coordinate vector, and comment on the results h. Use of performance measures other than simple accuracy 5. Conclusion Compare the results of the two classifiers, and comment on their performance. Summarise your main findings. Explain possible current limitations of your solutions and possible further strategies to improve on the results. All arguments must be evidence-based. THE WORK NEEDS TO BE D...

Request ...The basic functionalities would be a feature detection and extraction SIFT feature Feature representation MHI MEI Data training and recognition using Support Vector Machine SVM and Experimental results analysis If possible I would like to compare different features and different Classifiers with the neural network And run the system with a different data set with some more analysis Deliverables A written report on any background material you have used along with pre-existing code referencing it and where it has been found. Any formulas pseudo-code and diagrams that can help explain the system and what you have done. A description of the experimental protocols and any test data and numbers of training and test images. A summary of any insights drawn f...

...early diagnoses would help in diagnosing such a disease. This task consists of creating image classifiers to predict whether there is pneumonia (see image on the right) or not (see image on the left) in an input image. The dataset used in this task is from the following Kaggle competition: You are expected to explore a range of machine learning classifiers, inspired by the various models and categories explored within the module and beyond (i.e. from reading and literature). At least two of the deep learning classifiers discussed in the lectures and/or workshops should be included as baselines. In addition, at least one of your proposed classifiers should attempt to go beyond the module in terms of architectural, approach, and/or algorithmic

I have several classifiers showing good train/test accuracy. I need an expert to urgently use the model to predict live/upcoming features. N/B: I already have the code for the historical data, models, future data, how ever you may wish to optimize. I need the results separate dataframes please.

Deadline for this project is 13/04/2022. This project will be coded using Python. Read the notes section and make sure it is understood. Using Scikit-learn (and pandas to import the dataset), train and evaluate K-NN and decision tree classifiers using 70% of the dataset from training and 30% for testing. For this part of the project, we are not interested in optimising the parameters; we just want to get an idea of the dataset. Compare the results of both classifiers in a # comment. Following this, carry out the following methods, and explain the results of each one using a # comment at the start of each code segment: ● Hold-out and cross-validation. ● Hyper-parameter tuning. ● Feature reduction. ● Feature normalisation. Notes: Scikit-learn (and pandas just to import the...

Any dataset of your choice. Apply Random Forests and two other classifiers of your choice. Train and test with a random split (80% training & 20% testing). Calculate performance measures: sensitivity, specificity, and accuracy for all three algorithms. Also, generate the ROC curve, and calculate the AUC score. For each algorithm, tune the hyperparameters using grid search. Employ a feature selection method taught in the class on the training dataset and re-run the same classification experiments. Output the performance measures, the ROC curve, the AUC score before and after feature selection for all three algorithms.

use of Deep Learning classifiers and training deep neural networks.

Using Friedman and Nemenyi post- hoc test, I want to as...post- hoc test, I want to assess the performance results of these algorithms statistically. It will be help me know whether the classifiers are significantly different from each other or not. The null hypothesis (H0) considered in this case is that there is no performance difference among classifiers. While alternative hypothesis (HA) is that there is at least one classifier that performs significantly different than at least one other classifier for each performance metrics. Secondly, I need to find which classifier pairs performs significantly different. Obtain the p value of all the pairwise comparisons using Nemenyi post-hoc test. Let’s assume the classifiers being tested are ‘LR’ and &lsq...

I need a deep learning program to extract features, namely: a) Time Domain features: qEEG, Hjorth Features, Fractal Dimension, b) Frequency domain features: Band power, Hilbert-Huang Spectrum c) Time - frequency domain feature: DWT based RMS, DWT based band power d) Electrode combination feature: STFT based diff./ rational asymetry e) All feature Then feature selection and classify using: MLPNN, RBFNN, RF, DT, SVM (WITH BEST KERNER FUNCTION), BAGGING SVM, BAGGING DT, XG BOOSTING, GRADIENT BOOSTING AND EXTENDED ANN.