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Ajay Shewale's Projects

ai-lab icon ai-lab

Lab assignemnets for CS308 Introduction to AI class.

alexa_template icon alexa_template

A template and tutorial for building an Alexa Skill written in Python focused on readability.

dlnd-image-classification icon dlnd-image-classification

In this project, you'll classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. You'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded. You'll get to apply what you learned and build a convolutional, max pooling, dropout, and fully connected layers. At the end, you'll get to see your neural network's predictions on the sample images.

iiitvchatapp icon iiitvchatapp

An android chat application based on sockets and some additional features like maps integration ,calendar and notification and gmail integration

sahayak icon sahayak

Artificial intelligence is a technology that makes interactions between man and machines using natural language processing. Natural language processing is a field of computer science which involves making computers derive meaning from human language and input as a way of interacting with the real world. In this project, we are addressing central problems faced by farmers, students, and citizens. Nowadays, it is straightforward to google queries on the internet, but due to the millions of search results, relevant and semantic information regarding questions are difficult to obtain. Also, there is a high demand for such problems to be on a single platform and this is what our application will provide. A simple yet effective chat bot which will suggest and provide all the relevant information about the query. Our target audience will be the most important factors of this project (mainly farmers).

sentiment-analysis-of-text-data-tweets- icon sentiment-analysis-of-text-data-tweets-

This project addresses the problem of sentiment analysis on Twitter. The goal of this project was to predict sentiment for the given Twitter post using Python. Sentiment analysis can predict many different emotions attached to the text, but in this report, only 3 major were considered: positive, negative and neutral. The training dataset was small (just over 5900 examples) and the data within it was highly skewed, which greatly impacted on the difficulty of building a good classifier. After creating a lot of custom features, utilizing bag-of-words representations and applying the Extreme Gradient Boosting algorithm, the classification accuracy at the level of 58% was achieved. Analysing the public sentiment as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like the stock exchange.

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