NitinBhore/GenerativeAI-Generate_new_reviews_using_BERT

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NitinBhore/GenerativeAI-Generate_new_reviews_using_BERT

NitinBhore

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Use the Yelp Dataset, which contains user reviews and ratings of businesses across various categories to perform sentiment analysis on the reviews and classify them as positive, negative, or neutral. Generative AI: Build a system to generate new reviews using pre-trained language models such as BERT

face_recognition

Facial recognition is a category of biometric software that maps an individual's facial features mathematically and stores the data as a faceprint. The software uses deep learning algorithms to compare a live capture or digital image to the stored faceprint in order to verify an individual's identity.

text_extraction_from_docs_and_docs_classification

The objective of this assignment is to build an NLP solution forthe provided dataset. The dataset consists of scanned documents from an archive.

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documentAI

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BuildingChatbotDatasetforUNGeneralDebateStatements

Your task is to create an end-to-end solution for building a chatbot dataset based on the text of each country's statement from the general debate at the United Nations (UN).

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predict_qualitative_properties_of_a_solar_panel

To predict qualitative properties (data-set B) of a solar panel using quantitative properties (data-set A or I-V curve) of the same solar panel (module)

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document_classifier_using_NLP

Document Classification or Document Categorization is a process to assign different classes or categories to documents as required, eventually helping with storage, management, and analysis of the documents. It has become an important part of the computer sciences and the daily functioning of many companies today.

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cifar10_images

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Daily_Demand_Orders_Forecast

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customer_churn_classification

Customer churn, also called customer attrition, is the number of paying customers who fail to become repeat customers. In this context, churn is a quantifiable rate of change that occurs over a specified amount of time

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predict_loan_defaulters

The Bank Indessa has not done well in the last 3 quarters. Their NPAs (Non Performing Assets) have reached all time high. It is starting to lose the confidence of its investors. As a result, it’s stock has fallen by 20% in the previous quarter alone

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IMDB_sentiment_classification

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Traffic_Flow_Prediction

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IntelImageClassification

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paisabazaar

Predict_top_Menu_Item_and_Item_Qty

The dataset is for restaurant sales for Friday and Saturday, both at lunch and dinner time. There are few instances of 'To-Go' orders like Uber Eats in this dataset. Typical lunch hour is 11:30 AM-2:00 PM, and dinner hour is 6:30 PM-10:00 PM some of the data is missing so is represented as 'na' in the data set

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MedicalChargesPrediction

This dataset is dedicated to the cost of treatment of different patients. The cost of treatment depends on many factors: diagnosis, type of clinic, city of residence, age and so on. There is no data on the diagnosis of patients.

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Detecting-Helmet-and-Non-Helmet

Detecting Helmet and Non-Helmet Wearing Individuals in Images

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Natural-language-processing

Predict_survival

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guess_the_product

A manufacturing company is facing challenge to classify raw materials(products) received from vendors. To help them in classifying the raw materials, build a machine learning model that could predict the category based on certain input features.

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predict_the_category_of_news_article

Use the News Category Dataset from Kaggle to predict the category of a news article based on the headline. The model should be able to classify the news articles into one of the 41 categories present in the given dataset. The data should be preprocessed and feature selection should be performed before the model is built.

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predict_likes_of_posts

The assignment is about building an AI model that could predict the likes of posts on our platform X. We power engaging content in various interests to our users across multiple countries. All of the content is being scrapped from the web from multiple sources.

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