priyansh4320

Pinned Repositories

• Abstractive-Text-Summarization-Enhancing-Sequence-to-Sequence-Models-Using-Word-Sense-Disambiguatio

  This repository contains code and resources for abstractive text summarization (TS) using a novel framework that leverages knowledge-based word sense disambiguation (WSD) and semantic content generalization to enhance the performance of sequence-to-sequence (seq2seq) neural-based TS.

  Language:Jupyter Notebook13100

• alexa-reviews-sentiment-prediction-model

  a classification model to predict the sentiment either (1 or 0) based on Amazon Alexa reviews

  Language:Jupyter Notebook0101

• BBC-News-Classification-using-hugging-face

  classify news into five categories: business, entertainment, politics, sport, and tech.

  Language:Jupyter Notebook1100

• classification-mobile-price-analysis-

  Create a classification model to predict whether price range of mobile based on certain specifications

  Language:Jupyter Notebook1100

• coffee-machine-project

  1. Prompt user by asking “ What would you like? (espresso/latte/cappuccino): ” a. Check the user’s input to decide what to do next. b. The prompt should show every time action has completed, e.g. once the drink is dispensed. The prompt should show again to serve the next customer. 2. Turn off the Coffee Machine by entering “ off ” to the prompt. a. For maintainers of the coffee machine, they can use “off” as the secret word to turn off the machine. Your code should end execution when this happens. 3. Print report. a. When the user enters “report” to the prompt, a report should be generated that shows the current resource values. e.g. Water: 100ml Milk: 50ml Coffee: 76g Money: $2.5 4. Check resources sufficient? a. When the user chooses a drink, the program should check if there are enough resources to make that drink. b. E.g. if Latte requires 200ml water but there is only 100ml left in the machine. It should not continue to make the drink but print: “ Sorry there is not enough water. ” c. The same should happen if another resource is depleted, e.g. milk or coffee. 5. Process coins. a. If there are sufficient resources to make the drink selected, then the program should prompt the user to insert coins. b. Remember that quarters = $0.25, dimes = $0.10, nickles = $0.05, pennies = $0.01 c. Calculate the monetary value of the coins inserted. E.g. 1 quarter, 2 dimes, 1 nickel, 2 pennies = 0.25 + 0.1 x 2 + 0.05 + 0.01 x 2 = $0.52 6. Check transaction successful? a. Check that the user has inserted enough money to purchase the drink they selected. E.g Latte cost $2.50, but they only inserted $0.52 then after counting the coins the program should say “ Sorry that's not enough money. Money refunded. ”. b. But if the user has inserted enough money, then the cost of the drink gets added to the machine as the profit and this will be reflected the next time “report” is triggered. E.g. Water: 100ml Milk: 50ml Coffee: 76g Money: $2.5 c. If the user has inserted too much money, the machine should offer change. E.g. “Here is $2.45 dollars in change.” The change should be rounded to 2 decimal places. 7. Make Coffee. a. If the transaction is successful and there are enough resources to make the drink the user selected, then the ingredients to make the drink should be deducted from the coffee machine resources. E.g. report before purchasing latte: Water: 300ml Milk: 200ml Coffee: 100g Money: $0 Report after purchasing latte: Water: 100ml Milk: 50ml Coffee: 76g Money: $2.5 b. Once all resources have been deducted, tell the user “Here is your latte. Enjoy!”. If latte was their choice of drink.

  Language:Python1100

• ecommerce-website-customer-churn-data-analysis

  given an ecommerce website I have to perform data analysis on customer churn and extract insights from it

  Language:HTML1100

• google-cloud-genrative-ai

  This repos contains notebooks for the Advanced Solutions Lab: ML Immersion

  Language:Jupyter Notebook1000

• Ice-Sejj

  Introducing Ice-Sejj: Your Ultimate NLP and Machine Learning Solution

  Language:Python2201

• sEmantic_sentence_similarity_model_api_with_transformers_and_tensorflow

  an api for the sns model created (semantic sentence similarity model using tansformers ,tenosrflow and fastapi ) : hosted on aws

  Language:Jupyter Notebook1100

• Text-Guided-Sketch-to-photo-Image-Synthesis

  a text-guided sketch-to-image synthesis model that semantically mixes style and content features from the latent space of an inverted Generative Adversarial Network (GAN). Our goal is to synthesize plausible images from human facial sketches and their respective text descriptions.

  Language:Python3203

priyansh4320's Repositories