Nlp Projects

Data Engineer for Chatbots - Prompt Engineer

Job Description: Data Consulting - Coordinator Engineer for Chatbots We are seeking a Data Consulting – Coordinator Engineer for Chatbots, a specialist in designing, implementing, and optimizing data solutions tailored for chatbot systems. In this role, you will leverage your expertise in data engineering, conversational AI, and consulting to help organizations build and maintain intelligent, high-performing chatbot solutions. As data quality is the cornerstone of any successful chatbot, you will play a pivotal role in ensuring the data is accurate, structured, and actionable. ________________________________________ Key Responsibilities 1. Data Strategy and Consultation • Collaborate with clients to understand their business objectives and design data strategies that align with their goals. • Provide clear guidance and instructions to Data Engineers on what data to collect and where to source it. 2. Data Collection and Quality Control • Collect large volumes of conversational data from diverse sources, ensuring its relevance and accuracy. • Oversee and verify the data collected by other Data Engineers to ensure it meets quality standards. • Preprocess data by removing irrelevant or duplicate entries, handling missing values, and correcting errors. • Structure the data appropriately for training machine learning models. 3. Data Preparation for AI Training • Format and prepare data in a Q&A structure for NLP (Natural Language Processing) model training. • Collaborate with Data Scientists and NLP Engineers to develop and optimize chatbot models. • Ensure chatbot responses align with the collected and curated data. 4. Data Infrastructure and Analysis • Design and implement scalable data pipelines, databases, and data warehouses. • Analyze data using tools such as SQL, Python, or R to extract insights. • Develop dashboards and reports with visualization tools like Tableau, Power BI, or Looker to present actionable insights. • Work with big data technologies, including Hadoop, Spark, or Snowflake. 5. Continuous Improvement and Support • Monitor chatbot performance and fine-tune its responses based on data-driven insights. • Provide ongoing support to ensure data solutions and chatbot systems remain effective and aligned with business needs. • Collaborate closely with Data Scientists, Software Developers, and key business stakeholders. ________________________________________ Required Skills and Qualifications • NLP Expertise: Familiarity with NLP frameworks such as spaCy, Transformers, and Rasa. • Data Engineering: Proficiency in ETL processes, SQL, and data pipeline tools. • Programming: Strong knowledge of Python, essential for chatbot and AI development. • Cloud & AI Platforms: Experience with cloud-based AI services (e.g., AWS Lex, Google Dialogflow, Azure Bot Service). • Analytical Thinking: Ability to interpret user data and derive actionable insights for continuous improvement. • Consulting Skills: Excellent communication skills to explain technical solutions and strategies to clients effectively. ________________________________________ This role is ideal for someone who thrives at the intersection of technology and business, with a passion for building data-driven solutions that enhance conversational AI systems. If you’re ready to make an impact in this dynamic field, we’d love to hear from you! The first two projects will serve as an opportunity for us to get to know each other and establish a working relationship. These initial projects will be completed at a fixed rate of $100 to $150, depending on their complexity. After that, we will determine the pricing for each subsequent project based on the specific requirements and demands of our clients. Kindly let us know if your skills and experience align with our requirements. Many Thanks, Erio

Dashboard and Analysis for Questionnaire Data

This project involves developing a dynamic dashboard to analyze 150 responses to a 40 question survey with both closed and open-ended questions. The scope of work includes cleaning and structuring the data, creating visualizations like charts and heatmaps for closed responses, and employing AI techniques like sentiment analysis, topic modeling and keyword extraction to enhance insights from open responses. The assistant will apply their expertise in data analysis, Excel, Power BI and AI/NLP skills to first prepare the dataset and then design an interactive dashboard utilizing tools like slicers and filters to explore patterns in the data. Automated insights utilizing AI will highlight any trends, outliers or deeper understandings within the information. Topic modeling and clustering will be used to categorize common themes among open responses, while sentiment analysis assesses the positive, negative or neutral tone. Keyword extraction will identify frequently discussed terms. Documentation will summarize the methods applied and key findings uncovered. The completed dashboard deliverable will provide clear and engaging visual representations of both the qualitative and quantitative data in an editable file for the client's continued reference and analysis. Creativity in visualizing insights and translating both types of questions into meaningful visuals for the non-technical audience is important.

AI GMAIL API REPLY SYSTEM + DASHBOARD

This project involves building an AI-powered email response system that uses Natural Language Processing (NLP) to screen, categorize and generate contextual replies to incoming emails via the Gmail API. The system will analyze each email's intent, sentiment and priority to determine if an automated reply is suitable or requires human review. Emails will be fetched from Gmail using the API and stored along with metadata for categorization and filtering. NLP models will then classify emails by intent such as inquiries, requests or complaints. Sentiment analysis will also detect the emotional tone. Based on intent, sentiment and complexity, emails will be placed into priority categories like high-importance or routine. Spam emails will additionally be filtered out. For categorized emails deemed suitable for automated response, the system will use fine-tuned GPT language models to generate contextual replies based on the detected intent and content of the email. Replies with signals of uncertainty or sensitive topics will be flagged for manual assessment prior to sending. Feedback from approved or adjusted responses will further train the models to better match expectations over time. The overall effectiveness of AI-generated replies will be monitored through sentiment analysis of follow-up emails as well as quantitative metrics measuring factors like reduced response times and customer satisfaction. Periodic manual auditing through a web-based dashboard can also track key performance indicators. The dashboard will provide

Techno Functional Consultant Freelancer

Key Responsibilities: Pre-Sales Engagement: Collaborate with the sales team to engage with potential clients, understand their business challenges, and articulate how our Generative AI solutions can address their needs. Solution Design: Work with clients to design tailored solutions that leverage Generative AI technologies, ensuring alignment with their strategic objectives and technical requirements. Product Demonstrations: Conduct compelling demonstrations and presentations of our Generative AI solutions, highlighting features, benefits, and value propositions. Technical Expertise: Provide deep technical expertise and guidance on Generative AI technologies, including natural language processing (NLP), machine learning models, and related innovations. Documentation: Develop and deliver comprehensive proposal documents, technical specifications, and solution architecture diagrams. Collaboration: Work closely with internal teams, including product development, engineering, and support, to ensure successful implementation and delivery of solutions. Market Insights: Stay updated on industry trends, competitive landscape, and emerging technologies in the Generative AI space to position our solutions effectively. Qualifications: Technical Skills: Good to have: expertise in Generative AI, including knowledge of NLP, machine learning frameworks, and AI-driven applications. Consulting Skills: Ability to translate complex technical concepts into clear, business-oriented solutions and communicate effectively with both technical and non-technical stakeholders. Client Engagement: Experience in managing client relationships, understanding business needs, and delivering high-quality pre-sales support. Excellent communication and presentation skills as this is a client facing role. Preferred Skills: Good to have experience with specific Generative AI platforms or tools (e.g., GPT, BERT, etc.). Good to have familiarity with enterprise software solutions and cloud computing environments. Strong analytical and problem-solving skills, with a proactive and client-focused approach. Location: Preferably in Detroit, Michigan, but open to other locations on the West Coast (USA). Remote work is available, but occasional travel may be required. Education: Good to have: Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field. Advanced degrees or relevant certifications are a plus. Experience: Good to have: 3-5 years of experience in a techno- functional consulting or pre-sales role, with exposure to AI and Generative AI technologies.

No code SaaS Web app (Conversation intelligence)

Project Overview We’re building a conversation intelligence web app focused on simplicity for smaller sales teams. The goal is to analyze sales conversations to improve performance and ease coaching. Users will get a dashboard displaying key metrics and insights into what went wrong in conversations. Additional features include AI-driven note-taking, transcription, and summaries. Example: Link Competitors: Rafiki, Grain, Novacy, Winn AI, Aviso, Clari, Gong, Chorus (by ZoomInfo). Qualifications: Prioritize quality. Bubble.io experience (with portfolio). Strong motivation and ability to take initiative. GDPR knowledge + experience in conversation intelligence tools (preferred) Clear, open communication and focus on user-friendliness. Long-term visionary: Consider beyond MVP and suggest scalable features. Familiarity with NLP and AI Core Features (MVP): Automatic Call Recording: Integrate with Zoom, Teams, or Google Meet. Speech-to-Text Transcription: AI converts recorded calls into transcripts. AI-Generated Analytics: Extract insights like key topics, sentiment, and performance indicators. Process: Sales calls are recorded via Zoom or another platform. The audio is sent to a speech-to-text AI via API to generate a transcript. The transcript is analyzed by an AI (e.g., GPT) to produce actionable insights. Analyzed data is stored in the database for easy retrieval. Technical Requirements: Platform: Built on Bubble.io. Integrations: Zoom, Teams, or Google Meet for call recording. Google Cloud Speech-to-Text or OpenAI for transcription and analysis. Storage: Use Amazon S3 or Google Cloud for storing recordings and transcripts. Important: Ensure GDPR compliance. Implement role-based access control to protect sensitive data. The app must be scalable. Data privacy: No use of customer data for external training purposes. Next Steps: Please provide a timeline, development cost, and expected monthly costs. There is an opportunity for long-term collaboration if the project is successful. I’m open to other recommendations or suggestions for improvement.

opportunity

Develop a Lifelike AI Companion for older people Using UR

We are seeking a skilled Unreal Engine developer to create a highly realistic AI companion for care home residents. This AI companion will function as a lifelike, human avatar integrated within a 32"/40" touchscreen table device, acting as a permanent assistant and companion to residents of care settings. The avatar will be visually represented as a middle-aged, well-spoken British lady, designed to offer interactive support, entertainment, and routine tracking. The successful developer will be responsible for building the following features: Key Project Requirements: Lifelike Avatar Creation: Create a highly realistic human avatar using Unreal Engine’s MetaHuman Creator. The avatar should be a middle-aged woman with a warm, approachable demeanor and a British accent. Include detailed facial animations (smiling, frowning, blinking) and emotional expressions (happy, calm, concerned) for natural interactions. Facial Animation and Lip-Sync: Implement lip-syncing using NVIDIA Omniverse Audio2Face or Unreal Engine’s built-in lip-syncing tools to match the avatar's mouth movements with the voice output. Ensure smooth, real-time facial animations triggered by user interactions. Text-to-Speech Integration: Integrate a Text-to-Speech (TTS) engine such as Google Cloud TTS or Amazon Polly to provide the avatar with a realistic British voice. The avatar must be able to speak naturally based on AI-generated text responses. Conversational AI: Integrate OpenAI GPT-4 or similar NLP models for conversational capabilities. The AI must be able to process and respond to user commands and conversations in real-time. Ensure that the AI learns and remembers the user’s name, preferences, and routines. Routine Learning and Notifications: The AI companion should recognize user routines (e.g., waking up, going to bed, favorite activities) and adjust suggestions accordingly. The avatar should notify care home staff if any unusual behaviour or emergencies (like falls) are detected. Mood Detection (Optional): Implement mood detection using facial recognition or other techniques to adapt the avatar’s expressions and tone to the user’s emotional state. Camera, Speaker, and Microphone Sync: Ensure the AI can fully sync with the Able Table’s built-in camera, microphone, and speakers to enable voice interactions and visual monitoring. The microphone will capture the resident’s voice for commands, which the AI will process and respond to. The camera will detect movements (for potential fall detection) and optionally recognize facial expressions to assess the resident’s mood. The speakers will provide clear, high-quality voice feedback and responses from the AI. Entertainment and App Control: The AI should be able to navigate and control Android apps like YouTube, Wikipedia, and Google Earth to provide entertainment or relevant content based on the user’s interests. Packaging for Android: Once the Unreal Engine project is completed, package the project as an Android APK that will run on a 32"/40" touchscreen Android device. Optimise the app for performance on an Android tablet, ensuring smooth operation, particularly for facial animations, voice control, and app navigation. Deliverables: Fully functional Unreal Engine project with the lifelike avatar and all core features. Android APK file ready for deployment on our 32"/40" touchscreen devices. Documentation on how to manage and update the app, as well as any specific configurations needed for optimal performance on Android. Timeline: The project should be completed within 4-6 weeks. Milestones will be set for avatar design, AI integration, and Android packaging. Required Skills: Expertise in Unreal Engine and MetaHuman Creator. Experience with Text-to-Speech and Natural Language Processing (NLP). Familiarity with NVIDIA Omniverse Audio2Face or similar lip-syncing tools. Experience in Android app development and packaging Unreal Engine projects for Android. Strong understanding of machine learning (optional but preferred for mood and routine learning features). This is phase one and we are looking to work with someone to develop this further

pre-funded

Social Media Data Analysis using Web Scraping and AI

Description: We're seeking a skilled web scraping expert to collect data from Twitter, YouTube, and Facebook, and integrate it with an AI model/agent for analysis. The goal is to extract valuable insights from social media platforms using natural language processing (NLP) and machine learning techniques. Responsibilities: - Web scrape Twitter, YouTube, and Facebook for specific data points (e.g., hashtags, keywords, user engagement) - Design and implement a data pipeline to store and process the collected data - Integrate the data with an AI model/agent for analysis (e.g., sentiment analysis, topic modeling, entity recognition) - Collaborate with our team to refine the AI model and improve results Requirements: - Experience with web scraping tools (e.g., Scrapy, Beautiful Soup) and languages (e.g., Python, JavaScript) - Knowledge of social media APIs and data structures - Familiarity with AI and NLP concepts, including machine learning frameworks (e.g., TensorFlow, PyTorch) - Strong programming skills and attention to detail Deliverables: - A functional web scraping pipeline for Twitter, YouTube, and Facebook - Integrated AI model/agent for data analysis - Documentation and insights from the analysis Timeline: - this is a small project and should be done in two to five days - there is a milestone for each social media platform If you're interested in this project, please submit your proposal, including your experience, approach, and estimated timeline.

I need a to humanise, proofread and edit my content 1600 words

I need a professional to humanise, proofread and edit my content 1600 words, I have created content using AI, I'm looking to humanise the content so it passes al AI detectors and also be edited and proofread the word count is 1600 word and there is about 100 NLP Terms that needs to be remain in the content.

AI Platform

Description: We are seeking a talented AI Developer to join our team and help build and enhance The AI Room, a cutting-edge platform that empowers artists with AI-driven tools for personalised marketing, song mastering, album artwork design, and business management. This is an exciting opportunity to work on a platform that streamlines both creative and business processes, allowing artists to focus on their craft while maximising their success. Key Responsibilities: Develop, maintain, and optimise AI algorithms for personalised marketing, song mastering, and album artwork generation. Work with machine learning models, particularly in NLP, audio processing, and generative design (GANs). Design and implement data pipelines for user interaction, audio, and visual data processing. Ensure seamless integration with third-party APIs for distribution, social media, and royalty management. Collaborate with designers and product teams to create an intuitive and user-friendly platform experience. Focus on platform scalability, security, and cloud infrastructure management.

I need Plagiarisam Checking Tool

I seek a software developer to create a plagiarism detection application. The tool should have the ability to analyze written work uploaded by users and check for duplicated or improperly attributed content by comparing it to information from various online sources. Specifcially, the program must utilize web scraping techniques to build an internal database of published works that can be referenced when new submissions are received. It then needs to parse the uploaded files, extract text, and compare it on a word for word or phrasal basis to the archive in order to identify matching segments and generate originality reports. Additional requirements include support for multiple file formats like doc, pdf and odt, configurable sensitivity settings to handle various levels of plagiarism, and a user-friendly interface to display scan results and percent matches found. The successful bidder will have a strong portfolio demonstrating expertise in NLP, web scraping, database management and frontend design skills to create a robust plagiarism detection application as outlined. Experience developing similar tools and APIs would be beneficial.

Developer needed for AI, LLM, and RAG Chatbot

I am looking for a highly skilled Developer to spearhead the development of Gen AI, LLM-based, and Retrieval-Augmented Generation (RAG) Chatbots. Responsibilities: - Use Langchain for Implementing the chatbot - Develop and integrate RAG techniques to improve the accuracy and relevance of chatbot responses. - Use Pincone as vector database. - Use OpenAI GPT-4o as a LLM. - Stay informed on the latest advancements in AI/ML and RAG technologies to continuously innovate and improve chatbot solutions. Requirements: - Know OpenAI, Langchain, Python, Pinecone (Open for suggestions) - Strong problem-solving skills, with a deep understanding of NLP and RAG methodologies. You need to answer to following questions: 1.Have you ever created an ecommerce chatbot? 2.Describe your recent experience with similar projects If you're passionate about GEN AI development and have a track record in creating advanced Chatbots with RAG integration, please apply!

Machine Learning Engineer for Sustainability Startup

About Us: We are an early-stage startup passionate about leveraging machine learning to revolutionize sustainability in the built environment. We are currently in stealth mode but have a clear vision and exciting projects in the pipeline. Project Description: We are looking for a highly motivated ML intern with a strong foundation in machine learning and a keen interest in natural language processing (NLP) to join our team. The intern will play a crucial role in building a robust Retrieval Augmented Generation (RAG) pipeline using Large Language Models (LLMs). Responsibilities: Research and explore state-of-the-art RAG architectures and techniques. Develop and implement data preprocessing and cleaning pipelines for RAG systems. Build efficient document embedding models for effective information retrieval. Fine-tune LLMs for enhanced performance in the context of RAG. Experiment with different RAG components and hyperparameters to optimize performance. Collaborate with the ML team to integrate the RAG pipeline into existing applications. Contribute to the development of new AI products and features. Skills and Qualifications: Experience with building RAG pipeline Experience with huggingface, streamlit, neo4j graph database, Gemini API Optional: Experience with LlamaIndex Passion for sustainability and the built environment is a plus! To Apply: Please submit your profile, GitHub links with past works, your CV highlighting your interest in machine learning for sustainability, and any relevant examples of your work Note: This can be a medium to long term contract to hire opportunity.

Data Sourcing and RAG Creation for LLM

For now our main goal is to have the right data to train our recommendation Chat AI engine. The data quality, availability and value is our main purpose for this first project, which of course continues till our MVP launch. By data quality we mean, the sources, deduplication, handle missing values, normalization, stemming, tokenization indexing etc.. everything so that this RAG is properly scalable and prepared for embedding and vectorization, making it suitable for machine learning purposes.. This includes how and where we data mine/harvest, the date sources themselves that are relevant to the travel industry within the verticals mentioned: Restaurants, Hotels, Nightlife and Experiences (viator, airbnb experiences, getyourguide, etc…) Performance Tracking and Visualization: Visual Indicators: Utilizing platforms like PowerBI or Tableau, I will create dashboards to visualize and track the performance of your language models. This will help in quickly identifying patterns and areas for improvement Data Handling Libraries: Experienced with Pandas, NumPy, Beautiful Soup, Scrapy for data manipulation and web scraping. Machine Learning and NLP: Knowledgeable in using libraries such as NLTK and spaCy for natural language processing tasks, which are critical for RAG systems. We will not use any corporate LLM such as ChatGPT, LLamada or Gemini that in the future can become our competitor, we want to build a value business in this space and have a very well trained proprietary model so for this we want to use an open source LLM. This is important to us and the individual that we bring onboard should be ok with building pipelines and APS for whatever open source LLM we all decide will achieve our desired outcome. We will use self deployed open source LLMs. Overview outline: Foundational model Building Prompt templates and prompt engineering tools Vector databases Data SDKs and frameworks Fine-tuning tools Deployment and monitoring tools Skills and Tools: Data Handling Libraries: Experienced with Pandas, NumPy, Beautiful Soup, Scrapy for data manipulation and web scraping. Database Management: Skilled in PostgreSQL and MongoDB for structuring and managing large datasets. Machine Learning and NLP: Knowledgeable in using libraries such as NLTK and spaCy for natural language processing tasks, which are critical for RAG systems. Conversational AI: Implement a conversational AI capable of handling various travel-related queries. Use frameworks like Rasa or Dialogflow for building the chatbot infrastructure. Personalization: Ensure the chatbot can personalize responses based on user data and preferences, improving over time with machine learning algorithms. This will be a worldwide launch of the chatbot AI, since launching a Travel AI app specific to a region will lead to a terrible user experience. Imagine opening Expedia and searching for a trip to Mexico and an error message is returned saying "Sorry, we do not have any information about this location, please only search in the US for now" We can't do that. Also, there is so much data and info out there in the internet now, that it would be lazy for us not to build the best Travel AI tool out there. Our goal is an app better than this: https://justasklayla.com/. You should try it and then you would understand our primary AI goal.

ML Engineer: Develop offline LLM

Project Objective: Develop an offline Large Language Model (LLM) for UK legal professionals with advanced NLP capabilities. Will also include a simple CRM that is updated automatically. Key Features: - Chat interface with q and a - Document generation (contracts, engagement letter's etc.) - Audio transcription for user input for chatbot (alternative to typing) and TTS ouput from chat bot. -Audio transcription for client meetings and update CRM automatically - Legal research with up-to-date UK law database and uk case law - Split-screen citation display Tech Stack: - Open-source, commercially licensable frameworks (e.g., AnythingLLM, Ollama) - MIT-licenced codebase for customisation - Retrieval-Augmented Generation (RAG) for enhanced context handling and/ or Grokking Additional Considerations: - Exploring grokking techniques for improved model comprehension. This would be preferred to RAG if the claimed 99%+ accuracy is achievable - Potential integration of unsloth for optimised training pipelines - software would have to run locally and entirely offline Project Background: - Established relationships with UK legal sector - Validated market demand through discussions with Law Society representatives - Clear path to commercialisation Ideal Candidate: - Strong ML/NLP expertise - Experience with LLM fine-tuning and deployment - Familiarity with legal domain (preferred) - Passion for developing AI solutions with real-world impact If you're excited about this project and have relevant expertise, I'd love to hear from you.

opportunity

ML Engineer for Legal AI Project: Develop offline LLM

Project Objective: Develop an offline LLM for UK legal professionals with advanced NLP capabilities. Key Features: - Chat interface - Document generation (contracts, engagement letters etc.) - Audio transcription from; user input (instead of typing), client meetings - Legal research with up-to-date UK law database and uk case law - Split-screen citation display Tech Stack: - Open-source, commercially licensable frameworks (e.g., AnythingLLM, Ollama) - MIT-licenced codebase for customisation - Retrieval-Augmented Generation (RAG) for enhanced context handling Additional Considerations: - Exploring grokking techniques for improved model comprehension. This method is preferable over RAG vector database if the the grokked transformer can achieve 99% accuracy for retrieval. - Potential integration of unsloth for optimised training pipelines -The 'finished' software must run locally on consumer grade hardware and be completely offline Project Background: - Established relationships with UK legal sector - Validated market demand through discussions with Law Society representatives - Clear path to commercialisation Ideal Candidate: - Strong ML/NLP expertise - Experience with LLM fine-tuning and deployment - Familiarity with legal domain (preferred) - Passion for developing AI solutions with real-world impact If you're excited about this project and have relevant expertise, I'd love to hear from you. This project offers a unique opportunity to shape the future of legal technology in the UK.

SaaS platform development (turnkey)

Project Overview Project Name: AI-Powered SEO Content Creation and Management Tool Objective: To develop a SaaS platform that automates keyword research, content creation, SEO optimization, and auto-posting of articles, providing users with a comprehensive dashboard for managing and tracking content performance. Functional Requirements 1. User Management 1.1 User Registration and Authentication Users can register using email or social media accounts. Implement email verification and password recovery mechanisms. 1.2 User Roles and Permissions Admin: Full access to all features and settings. User: Limited access based on subscription tier. 1.3 User Profiles Users can manage their profile information, subscription details, and preferences. 2. Keyword Research 2.1 Keyword Data Collection Integrate with APIs like Google AdWords, Ahrefs, or SEMrush to gather keyword data. 2.2 Keyword Analysis Use NLP to analyze and select optimal keywords based on search volume, competition, and relevance. 2.3 Keyword Suggestions Provide users with keyword suggestions related to their niche and selected websites. 3. Content Creation 3.1 Article Generation Use AI models (e.g., GPT-4) to generate SEO-optimized articles based on selected keywords. Ensure articles are unique, well-structured, and include a table of contents. 3.2 SEO Optimization Integrate with tools like Yoast SEO or Surfer SEO for real-time SEO suggestions. 3.3 Metadata and Images Automatically generate meta descriptions and keywords. Integrate with stock image providers like Unsplash or a subscription-based service for relevant images. 3.4 Content Validation Implement plagiarism checks using tools like Copyscape. Use readability tools like Grammarly to ensure content quality. 4. Auto-Posting 4.1 Scheduling System Allow users to schedule articles to be posted at predetermined intervals (e.g., 3 to 6 articles per day). 4.2 CMS Integration Integrate with popular CMS platforms like WordPress and Joomla for automated publishing. 5. Analytics Dashboard 5.1 Traffic Analysis Integrate with Google Analytics API to track website traffic, user engagement, and other key metrics. 5.2 SERP Positioning Use Google Search Console to monitor and report keyword rankings and SEO performance. 5.3 Performance Reports Provide visual reports on traffic growth, keyword rankings, and article performance. 6. Subscription and Billing 6.1 Subscription Plans Implement multiple subscription tiers (e.g., Freemium, Basic, Professional, Enterprise) with different features and limits. 6.2 Payment Integration Integrate with payment gateways like Stripe or PayPal for subscription billing. 6.3 Billing Management Allow users to manage their subscriptions, view billing history, and upgrade or downgrade plans. 7. Notifications 7.1 Email Notifications Send email notifications for account-related events (e.g., registration, password recovery, subscription renewal). 7.2 In-App Notifications Provide in-app notifications for important updates (e.g., article generation completion, keyword suggestions). Non-Functional Requirements 1. Performance Ensure the platform can handle concurrent users efficiently. Optimize response times for keyword research and content generation. 2. Security Implement secure authentication and authorization mechanisms. Ensure data encryption in transit and at rest. 3. Scalability Design the platform to handle increasing loads as user base grows. Ensure the system can scale horizontally by adding more servers. 4. Usability Provide a user-friendly interface with intuitive navigation. Ensure accessibility compliance (e.g., WCAG standards). Technical Requirements 1. Technology Stack Backend: Python (Django/Flask) or Node.js Frontend: React.js or Vue.js Database: PostgreSQL or MongoDB Hosting: AWS, Google Cloud Platform, or Azure AI Models: OpenAI's GPT, BERT for SEO optimization 2. Integrations APIs: Google AdWords, Ahrefs, SEMrush, Yoast SEO, Surfer SEO, Unsplash, Google Analytics, Google Search Console, Stripe, PayPal CMS Platforms: WordPress, Joomla

urgent

Transformer to Forecast Time Series

MUST have deep expertise in Large Language Model (LLM), GPT, NLP, and Time Series analysis, PyTorch, Python This project is time sensitive, I need it ASAP. I would like to know how the Transformers diviner model provided below from Github can be used to forecast various time series in the original scale, and make sense of them. The model is already coded, trained, and tested, therefore the original coding should not be changed. Rather you are just adding extra coding at the bottom for the different kinds of dataset. Disregard all the time series provided in the original coding like ETT, WTH, etc, and from that rather just focus on the Exchange/gold prices data 60 day gold prices, and the new data that I recommend. These is a sequential project, meaning you should only move to the next point once you fully complete the current point. Use good visuals 1. Forecasting Gold Prices: - After obtaining the MSE/RMSE (already provided in the code), provide code to display all test sample predictions in the original scale alongside the actual prices, and the dates from the original table. (COMPLETE THE FULL REQUIREMENT HERE, BEFORE MOVING TO THE NEXT POINT) - Create a table with columns for date, "true" prices, and "pred" prices for a 60-day period for the entire test sample. For the predictions, I want to see each prediction in the time step until the 60 day period. Generate graphs in the original scale for gold prices and for the other asset prices to visualize "true" vs. "pred" performance over time. . For example, the goal is a table for Gold: Date, True, Pred of each time steps as column headers, underneath it, have the exact date the test sample, have the original price of the specific date, and have the forecasted values for each time step until the 60th day. So I would know, e.g Jul 19th 2018, the actual price was 1551,.. 60th day, the forecast from the model was 1544.25. So I know exactly how the model performed in real time. I know that if 60 days from today say in Sept 29, the model is telling me the price of Gold will be 1510, I want to see how that model actually performed when we actually get to that specific date. In a table and graph, that is what the project was about. 2. Data Collection and Forecasting for Multiple Assets: - Show how you’re pulling data from Excel file (csv) that contains the date, TARGET FEATURE, and THE VARIABLES NEXT TO IT, and HOW YOU are UPLOADing THAT INTO THE MODEL AND THE MODEL FORECASTED BASED ON ALL THOSE INFORMATION. - Daily Apple stock prices excel (alognside its P/E ratio and Earnings per Share for a 10 year period). 3. Attention Weights Analysis: - Clearly code and Display attention weights for the features/variables used to rank by importance in percentage terms. Use both the traditional attention weight for all layers, and separately the InverseDeepthDifferenceBlock because the original authors used it for a reason. (Clearly show/make notes how you are interpreting that visual. They should have dark backgrounds.) The point of the attention weight part is to know for instance since the model is forecasting Apple stock GDP to be 150, in the next 60 days, the from the variabes/features used in the model, attention weight is telling me that P/E ratio was responsible for 40% of that forecast, EPS was response for 20% of that forecast based on the attention weights results. 4. Steps 1 to 3 for new data: EUR/USD spot price 10 years of data as target feature along with two variables, U.S. interest rates and U.S. trade balance (net trade deficit for a 10 year period). 5. Repeat steps 1 to 3 for 10 years of data for US Real GDP as target feature along with three variables, U.S. interest rates and U.S. inflation CPI, and China’s Real GDP 6. Use the model to forecast for 60 days each of these 4 target features simultaneously, just like in the original mode it simultaneously forecasts ETT, WTH, etc 7. Implementation Details: - Ensure coding compatibility with Google Colab, utilizing an A100 GPU, because that’s what I’ll use 8. Include clear notes in the code for ease of understanding and replication, because eventually I’ll be using different assets with different kinds of variables. 6. Project Timeline: - This project is straight forward because most of the hard coding is already there. This project is time sensitive, I need it ASAP GitHub Links: - Transformers Diviner Model: [Diviner-Nonstationary-time-series-forecasting](https://github.com/CapricornGuang/Diviner-Nonstationary-time-series-forecasting/blob/main/README.md) Please let me know if anything is unclear and how soon can I expect the project. THIS PROJECT IS TIME SENSITIVE