Classification Projects
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Complete Explanation For Data Science Resume Projects
I want help in explaining my data science projects in interview in a detailed manner . I am data science professional looking for a job switch of 3 years. Projects: 1) CONSUMER COMPLAINT CLASSIFICATION (NLP) Building an API and training model to classify future complaints based on its content for a banking firm. The dataset is of 2 million rows, 5+ years historical data. Skills/Technology: Python, SVM classification, Random Forest ,Flask, Glove ,Word2Vec 2) PREDICTIVE MAINTENANCE Built an end to end machine learning model for a heavy industry firm which records different features like power,temperature etc for machines and predicting whether failure will happen or not. Skill/Technology: Python, FastAPI, Digital Ocean.,Streamlit,Docker 3) RECOMMENDATION ENGINE FOR AUTOMATED TRADING PLATFORM Developed custom Technical Indicators Functions to analyse historical price data and market trends, triggering buying signals when specific conditions were met. Empowered traders clients with actionable insights by providing timely buy signals aligned with market trends. Skill/Technology: TA-Lib, TensorFlow, NumPy, Pandas, sklearn, statsmodels 4) INSURANCE POLICY CROSS SELLING A classification/ranking project aimed to detect health insurance customers most likely to buy a new type of insurance from the company - car insurance. To solve this problem a machine learning model was built to classify the customers by their probability of buying the insurance. The Heroku platform was used to deploy the ML model, which will respond to requests via API. Skill/Technology: XGBoost,LightGBM, NumPy, Flask,Heroku 5) CUSTOMER CHURN PREDICTION FOR A MALAYSIAN BANK Developed a model to analyse customer data and predict churn to boost customer retention.Employed statistical techniques on customer data using Pandas, Seaborn, and Sklearn. Reduced customer churn rate by 7%, leading to increased revenue, lower marketing costs, and enhanced customer loyalty.
21 days ago16 proposalsRemotePackaging design for carton and plastic bag
We seek a skilled graphic designer to develop packaging designs for carton boxes and plastic bags used to transport kiln dried logs. The designs should incorporate necessary product information while appealing to target customers. The carton box and bag designs must include accurate references to weight, dimensions and barcode/product identification number. Descriptive text showcasing key attributes of the kiln dried logs such as moisture content, recommended end uses and firewood classification should also feature prominently in the designs. Aesthetically pleasing designs showcasing the natural beauty and textures of kiln dried logs are desired. Designs may leverlage minimalistic styles, earthy colour palettes and tasteful typography to highlight the craftsmanship and quality inherent in the product. Packaging should protect logs from damage during transportation but also attract potential buyers on retail shelves. Flexible sizing options allowing branding continuity across various carton and bag sizes are preferable. The successful candidate will have proven experience designing packaging for firewood or lumber industries. Proficiency with industry standard design software for vector images and layout is expected. A portfolio demonstrating creative solutions within packaging briefs and ability to produce press and camera ready artwork within set timelines will be assets. We aim to select a designer with the vision and skills to professionally represent our kiln dried logs through attractive, informative and durable carton box and plastic bag designs.
7 days ago29 proposalsRemote
Past "Classification" Projects
I Need An Experienced Data Scientist for a Deep Learning task
I urgently need a deep learning freelancer for a classification Task
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Need an expert in Vision Transformer Model
I am looking for an expert in Vision Transformer Model for a project that involves image detection and classification to classify and display the result as the tumour in to glioma, meningioma and pituitary and no tumor type.. The specific task is to develop a model that can accurately classify images. Can use Kaggle dataset for the model to train on. The preferred programming language for this project is Python/MATLAB. Ideal skills and experience: Strong knowledge and experience in Vision Transformer Model Proficiency in Python programming language Familiarity with image classification techniques and algorithms Experience in training and fine-tuning models using large datasets Ability to optimize and improve model performance
Lidar Classification
Project Description: Lidar Classification I am looking for a freelancer who can assist me with a lidar classification project. The project involves working with airborne lidar data for the purpose of land surveying. Specific requirements: - The freelancer should have experience working with airborne lidar data. - The classification should be done with a high level of detail. - The ideal freelancer should have expertise in lidar classification for land surveying purposes. - Knowledge of environmental analysis and infrastructure planning is also a plus. - Lidar is actually processed with automatic clasification If you have the necessary skills and experience, please submit your proposal.
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Data scrape
HI, I am after a data scrape from https://transparentfarms.org.uk/facilities?classifications[]=Farm&classifications[]=DairyFarm&classifications[]=IntensivePigFarm&classifications[]=IntensivePoultryFarm&classifications[]=IntensiveSowPigFarm&page=1 columns I'm after: Farm/Unit name classification Company (operated by) Address I am after the farms. (Dairy farm, Intensive pig farm, intensive poultry, intensive sow pig). This will be over 8k records, so it is for someone who can automate this.
Simple python excercise based on an example.
Using two AI techniques, Neural networks, and Bayesian networks create two classification models in Google Colab based on an example provided with dataset provided.
Machine Learning - Tensor Flow model improvement
I have a binary classification problem and tensor flow based model. I want to improve the performance of the model
Sentiment Classification
Implement Sentiment Classification using Deep Learning Concepts. Use the IMDB Movies Review Dataset from Tensorflow Dataset Catalog. You can refer to this link - https://www.tensorflow.org/datasets/catalog/imdb_reviews The Solution should be in the given Python Notebook template. Link for the project template - https://www.dropbox.com/s/x5en0zekymi56mb/Deep%20Learning%20Project.zip?dl=0 Important Notes: Neat and Clean code with proper code comments. Implement the code with the only use of Tensorflow and Keras library. Completion Date: 15 June 2023 GMT 16:00 GMT * The project will not be accepted if the solution uses RNN, LSTM, and/or any transfer learning approach.
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Data base analysis, revised
I've rewritten the text simply for absolute clarification -- see below. If you have an interest, please answer these questions: 1- What methodology and what software will you use? 2- The parameters of this project are m = 300 and n = 20. Will the solution you derive be applicable to the same database with an m > 300 and n > 20, say m = 500 and n = 30? PS I'm a mathematician, UC Berkeley, so you can give me technical details. Revised description of project: I have a 2-dimensional database in an Excel sheet, derived from many observations in an experiment. Each observation consists of n variables (default n = 20) and the experiment has yielded m observations (default m = 300). Each observation’s n data items are contained in a row of the database with n+1 columns, the first column of which is a dependent variable that depends upon the values of the n other variables in the n columns of its row. The default database thus has 300 rows, a row for each observed sample of n variables, and n+1 columns, the first column for a dependent variable, and each of the next 20 columns containing the independent variables. The dependent variable is either -1, 0, or +1, and is the result of the behavior of the independent variables. Each independent variable’s importance in affecting the value of the dependent variable is a function not only of its value, but of the change in its value over the last 3 rows, thus the change in its derivative for the previous 3 rows. I want to find: 1- The best rms error predictor of the dependent variable given the values and derivatives of the 20 independent variables. This is a simple regression problem, easily solved with simple software. 2- If it exists, a subset of the independent variables whose dependent variable is always 1 when the derivatives satisfy certain conditions; and those conditions. This can likely best be done with CART (classification and regression tree software, https://www.intellspot.com/open-source-decision-tree/) and experience with such software would be helpful. If such a subset does not exist, I want the best such subset of size k, defined as, for any integer k, the subset of k independent variables, thus those k columns and the derivative conditions, whose proportion of rows with dependent variable equals 1.
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Professional data analysis
I am getting ridiculous responses from people who in no way are qualified for this task. Presumably, some people send out automated responses with cardboard language having little to do with the task. Please do not waste my time and yours and the web site’s developers, and respond to this only you have the qualifications. If you are qualified and have an interest, please answer these questions: 1- What methodology and what software will you use? 2- The parameters of this project are m = 300 and n = 20. Will the solution you derive be applicable to the same database with an m > 300 and n > 20, say m = 500 and n = 30? PS I'm a mathematician, UC Berkeley, so you can give me technical details. Revised description of project: I have a 2-dimensional database in an Excel sheet, derived from many observations in an experiment. Each observation consists of n variables (default n = 20) and the experiment has yielded m observations (default m = 300). Each observation’s n data items are contained in a row of the database with n+1 columns, the first column of which is a dependent variable that depends upon the values of the n other variables in the n columns of its row. The default database thus has 300 rows, a row for each observed sample of n variables, and n+1 columns, the first column for a dependent variable, and each of the next 20 columns containing the independent variables. The dependent variable is either -1, 0, or +1, and is the result of the behavior of the independent variables. Each independent variable’s importance in affecting the value of the dependent variable is a function not only of its value, but of the change in its value over the last 3 rows, thus the change in its derivative for the previous 3 rows. I want to find: 1- The best rms error predictor of the dependent variable given the values and derivatives of the 20 independent variables. This is a simple regression problem, easily solved with simple software. 2- If it exists, a subset of the independent variables whose dependent variable is always 1 when the derivatives satisfy certain conditions; and those conditions. This can likely best be done with CART (classification and regression tree software, https://www.intellspot.com/open-source-decision-tree/) and experience with such software would be helpful. If such a subset does not exist, I want the best such subset of size k, defined as, for any integer k, the subset of k independent variables, thus those k columns and the derivative conditions, whose proportion of rows with dependent variable equals 1.
I need a Python Web Scraping Script
Hi, I am new to python and I am having difficulties creating my own web scraping. I have all the necessary libraries installed (Pandas, requests, beautifulsoup). 1 - I need python to iterate the news websites defined in the "Channels" tab 2 - Download news headlines, date, time, company in the news and classify the time (available in the "classification" tab) 3 - Match the company in the news with the company ticker 4 - Create a sentiment score* The outputs need to in the "sheet1" tab and saved in an excel file locally *Available here: https://medium.com/the-handbook-of-coding-in-finance/sentiment-analysis-of-stock-market-in-python-part-2-estimating-sentiment-scores-with-nltk-292d8ec7d69e
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Xero & Business Finance Consulting
We need some support preparing our accounts for both corporation tax, and selling our e-commerce business. We've recently imported our bank accounts into Xero and have started to reconcile our payments but need some help finalising it. We'd also like some support on preparing the business for sale as we're looking at our selling options in the near future. In Xero, we'd need some help on the following: - Understanding why some bank balances don't match up (and help digging out any errors) - Understanding the correct classification of transactions - Understanding what to do with director payments and investments - Understanding the reports and which are needed for tax etc.. (we're new to corporation tax) - Filing for corporation tax - Checking / filing VAT returns (previous & future) Also general help cleaning it up and checking everything is correct. If this is something you could help with, please get in touch. Would be great to jump on a call / or meet up to discuss further. Ignore the budget, open to discussion / quotes / retainers / hourly options.
Qualitative analysis - Classifying tesxt and scoring sentiment
I am hoping you might be able to help me with a project where I need a very quick turnaround! I need to create a table of major themes and sentiment from the text feedback within an Engagement survey. I am attaching the following files: 1) Example file "NM Comments Extract and Analysis" The Excel file has two worksheets; Analysis - This shows how we classified the text in a previous survey (columns B and C), giving each statement a series of classifications (Column D) and a Sentiment (Column D) that was Negative,Neutral or Positive. Summary Parsed - Shows how we grouped the classification into themes You are more than welcome to use the Classifications in the Example File as a starting point for your analysis in the Working file. (e.g. Communication, Pay, Management, Training, Stress, etc) 2) Working file (SG Comments Extract) This has just one worksheet: Raw Data You will see there are more columns of text because each year had slightly different questions. I need to have the Working File analysed in the same way as the Example File, with the exception that I want to extend the Sentiment to 5 scale items (Very Positive, Positive, Neutral, Negative, Very Negative) There are over three hundred responses (rows) over three years (2019 to 2022) in the full Working File. I am only providing a few here to be sure that you are happy with the project. To prove that you understand the project could you please complete rows 3, 8 and 12 from the Working File. If you believe that you can produce a better form of analysis then I am more than happy to discuss, however I must have the classification and sentiment as an absolute minimum. I need this completed really urgently, by Monday 5th June at the latest As you can see from the files you will need an exceptionally high standard of English to complete this. Can you let me know if you are comfortable with the project requirement and delivery schedule and how much you would charge for this? Many thanks Chris
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Akeneo Implementation and Product Categorization Solution
Project Description: We are seeking a skilled freelancer to assist us in implementing Akeneo, an open-source Product Information Management (PIM) system, and solving our product categorization challenge. Our goal is to import feeds from various furniture brands and map their catalogue categories to the desired categories within our marketplace. This is crucial to ensure consistency and coherence in product categorization across different furniture brands' catalogs. Project Requirements: Akeneo Implementation: The freelancer should have expertise in setting up and configuring Akeneo PIM system to meet our specific requirements. This includes installation, database configuration, user management, and customization of the system as needed. Data Import: The freelancer will be responsible for designing and implementing a robust data import process. This involves importing product feeds from different furniture brands, mapping their categories to our desired categories within Akeneo, and ensuring accurate data synchronization. Product Categorization: The freelancer should have experience in developing a classification system or taxonomy within Akeneo. This system should intelligently match the products to appropriate categories within our marketplace based on product attributes, styles, or other relevant criteria. Integration with Marketplace: The freelancer will need to integrate Akeneo with our existing marketplace platform. This includes establishing data synchronization between Akeneo and the marketplace, ensuring seamless product updates, and maintaining consistency in categorization. Skills and Experience: Proven experience in implementing Akeneo or other PIM systems. Strong understanding of data import/export processes and data mapping. Expertise in developing classification systems or taxonomies. Familiarity with marketplace platforms and data synchronization. Excellent communication skills and ability to collaborate with our team. Prior experience in the furniture or e-commerce industry is a plus. Deliverables: Fully configured and customized Akeneo PIM system. Successful import of product feeds from various furniture brands. Mapping of catalogue categories to desired categories within our marketplace. Seamless integration between Akeneo and our marketplace platform. User training sessions and comprehensive documentation. Project Timeline: The project timeline is negotiable and will depend on the complexity of the implementation and the availability of the freelancer. We are looking for a freelancer who can demonstrate expertise in Akeneo implementation and product categorization. If you have the skills and experience required, please submit your proposal outlining your approach, relevant experience, timeline, and budget. Note: Only qualified freelancers with relevant experience will be contacted for further discussion.
Classification Problem Solution
Based on the attached dataset, required to showcase data science life cycle including business understanding, data preparation, data exploration, data wrangling and machine learning algorithms in attached solution template. Dataset & solution template can be found at https://www.dropbox.com/s/xf2gi84dhji440a/Classification.zip?dl=0 Timeline to deliver the project is: 15th March, 2023 16:00 GMT.
Need help with Image Classification using Pytorch.
I need help with Image classification using Pytorch. I have started it and am almost done with section 1 coding but its throwing some errors which I am struggling to fix. Ideally, I would want someone to sit with me and spend a few hours teaching me rather than solving it for me. The tasks are; 1. Function implementation 1.1 PyTorch Dataset and DataLoader classes 1.2 PyTorch Model class for a simple MLP model 1.3 PyTorch Model class for a simple CNN model 2. Model training 2.1 Train on TinyImageNet30 dataset 2.2 Generate confusion matrices and ROC curves 2.3 Strategies for tackling overfitting 2.3.1 Data augmentation 2.3.2 Dropout 2.3.3 Hyperparameter tuning (e.g. changing learning rate) 3. Model Fine-tuning on CIFAR10 dataset 3.1 Fine-tune your model (initialise your model with pretrained weights from (2)) 3.2 Fine-tune model with frozen base convolution layers 3.3 Compare complete model retraining with pretrained weights and with frozen layers. Comment on what you observe? 4. Model testing 4.1 Test your final model in (2) on test set - code to do this 4.2 Upload your result to Kaggle 5. Model comparison 5.1 Load pretrained AlexNet and finetune on TinyImageNet30 until model convergence 5.2 Compare the results of your CNN model with pretrained AlexNet on the same validation set. Provide performance values (loss graph, confusion matrix, top-1 accuracy, execution time) 6. Interpretation of results 6.1 Use grad-CAM on your model and on AlexNet 6.2 Visualise and compare the results from your model and from AlexNet Please only bid if you think you can help me learn this. Thanks
ML & DL Python Coder who can classify Pointclouds
I'm looking for someone who has a good Python skillset and has used Random forest classifiers before. I'm looking to commission the successful candidate on a two-stage basis where initially, we will develop a machine learning workflow for point cloud classification and then using this method, my team will create the training data for a deep neural network that can undertake the same task without manual labelling. Both methods have been proven by academia and most of the code is already available on Github, we just need someone to tweak the existing method to suit our needs. See attached doc for more detail. This is a private commission although it will play a part of a wider research project I'm associated with.
Migration contenu fichiers PSD vers fichier InDesign
Je recherche un freelance français pour m’aider à terminer les modifications sur mon livre sur les caractères chinois (pas besoin de parler chinois). Pour ça, il faut migrer le contenu de fichiers PSD (un fichier par page) vers le fichier InDesign (qui contient toutes les pages). Auparavant j’ai fait le livre à partir de Photoshop, il y a donc un fichier Photoshop par page. La maison d’édition m’a demandé de tout mettre sur InDesign, et de modifier le design des pages. Le contenu restant le même, il s’agit donc de faire plein de copier-coller entre les fichiers PSD et le fichier InDesign en s’adaptant à la nouvelle présentation. J’ai déjà fait les pages 1 à 207. A noté que pour les pages 208 à 230, la page de gauche est déjà faite. Il reste 53 pages en tout à faire. Sur 34 pages, il y aura une classification des exemples à faire pour les mettre dans leur catégorie grammaticale (nom, adverbe, adjectif, verbe…). Il n’y a pas besoin de migrer les images des “traits par traits” car ceux-ci vont être refait. Voir les pièces jointes pour les différences entre les fichiers PSD et le fichier InDesign. Lien vers l'ancienne et la nouvelle version du livre : https://drive.google.com/drive/folders/1-7NmJooPSlhTxzeEoo2_w4-Bv78g3kSg?usp=sharing
Small Python Project - Decision Tree & Random Forest Classes
In urgent need of Python programmer for a short project on generating a Decision Tree Classifer and a Random Forest Classifier for a dataset. Detailed information can be provided.