Ashish Agarwal (ikigai-aa)

ikigai-aa

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Location:Bengaluru, India

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Ashish Agarwal's repositories

Automatic-License-Plate-Recognition

Automatic License Plate Recognition for Traffic Violation Management made with YOLOv4, Darknet, Tensorflow Lite

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Face-Mask-Detector-using-MobileNetV2

This is a simple image classification project trained on the top of Keras/Tensorflow API with MobileNetV2 deep neural network architecture having weights considered as pre-trained 'imagenet' weights. The trained model (mask-detector-model.h5) takes the real-time video from webcam as an input and predicts if the face landmarks in Region of Interest (ROI) is 'Mask' or 'No Mask' with real-time on screen accuracy.

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Amazon-Products-Sentiment-Analysis

Sentiment analysis is the interpretation and classification of emotions (positive, negative and neutral) within text data using text analysis techniques. Sentiment analysis allows businesses to identify customer sentiment toward products, brands or services in online conversations and feedback. Sentiment analysis is a text analysis method that detects polarity (e.g. a positive or negative opinion) within text, whether a whole document, paragraph, sentence, or clause. Why Perform Sentiment Analysis? It’s estimated that 80% of the world’s data is unstructured, in other words it’s unorganized. Huge volumes of text data (emails, support tickets, chats, social media conversations, surveys, articles, documents, etc), is created every day but it’s hard to analyze, understand, and sort through, not to mention time-consuming and expensive. Sentiment analysis, however, helps businesses make sense of all this unstructured text by automatically tagging it. Benefits of sentiment analysis include: Sorting Data at Scale Can you imagine manually sorting through thousands of tweets, customer support conversations, or surveys? There’s just too much data to process manually. Sentiment analysis helps businesses process huge amounts of data in an efficient and cost-effective way. Real-Time Analysis Sentiment analysis can identify critical issues in real-time, for example is a PR crisis on social media escalating? Is an angry customer about to churn? Sentiment analysis models can help you immediately identify these kinds of situations and gauge brand sentiment, so you can take action right away. Consistent criteria It’s estimated that people only agree around 60-65% of the time when determining the sentiment of a particular text. Tagging text by sentiment is highly subjective, influenced by personal experiences, thoughts, and beliefs. By using a centralized sentiment analysis system, companies can apply the same criteria to all of their data, helping them improve accuracy and gain better insights.

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Sentiment-Analysis-Amazon-Mobile-Phones-Product-Reviews

This is a IPython Notebook focused on Sentiment analysis which refers to the class of computational and natural language processing based techniques used to identify, extract or characterize subjective information, such as opinions, expressed in a given piece of text. The main purpose of sentiment analysis is to classify a writer’s attitude towards various topics into positive, negative or neutral categories

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Basic-text-classification-with-Naive-Bayes

In the mini-project, we'll learn the basics of text analysis using a subset of movie reviews from the rotten tomatoes database. We'll also use a fundamental technique in Bayesian inference, called Naive Bayes. This mini-project is based on Lab 10 of Harvard's CS109 class. Please free to go to the original lab for additional exercises and solutions.

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Invisible-Cloak--CV

This is a Computer Vision Project which lets you make thing invisible by covering it with a cloak of a particular color. I have took red color cloth.

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Understanding-Pipeline

This notebook works with Iris Flower dataset

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insurance-charge-predictor

This projects make use of Machine Learning Algorithms, MLOps and help predict the insurance charges one has to pay depending upon some relevant independent features.

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SARS-CoV-2-Outbreak-Analysis

Beneficial to concerned client(s) desiring to have a detailed analysis of the present outbreak to have essence of present situation in order to discover and analyze the trend, behavior, responses to various factors affecting the market and invent , strategize precautionary measures for any future pandemic like this.

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API-Project-on-Frankfurt-Stock-Exchange

An API based project making use of the data from Frankfurt Stock Exchange extracted from Quandtl in order to do some research work and learning practices

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ashishagarwal

Porfolio Website

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Bayesian-Inference

For the final mini-project of the stats unit, you'll once again return tot he medical charge data you've used for the other mini-projects. Previously, we considered whether we believed that the actual average(non-insured) charge had fallen below a certain threshold. The hospital is now reviewing its financial resiliency plan, which requires a model for revenue under a range of conditions that include the number of patients treated. Its current model is based on a confidence interval for the mean, and scaling that by different numbers of patients for each scenario. This approach has a number of limitations, most acutely the breakdown of the central limit theorem for low patient volumes; the current model does not do a good job of reflecting the variability in revenue you would see as the number of cases drops. A bootstrap approach would return samples of the same size as the original. Taking subsamples would restrict the sampling to the values already present in the original sample and would not do a good job of representing the actual variability you might see. What is needed is a better model of individual charges. So the problem here is that we want to model the distribution of individual charges and we also really want to be able to capture our uncertainty about that distribution so we can better capture the range of values we might see. This naturally leads us to a powerful, probabilistic approach — we'll use the pymc3 library to perform Bayesian inference.

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BigMart-Sales-Prediction

Retail is another industry which extensively uses analytics to optimize business processes. Tasks like product placement, inventory management, customized offers, product bundling, etc. are being smartly handled using data science techniques. As the name suggests, this data comprises of transaction records of a sales store. This is a regression problem. The data has 8523 rows of 12 variables.

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Bootstrap-Inference-Project

A statistical analysis to based on Hypothesis Testing to check if there lies any difference between two groups of individuals (insured and non-insured) from a given datasets of details of patient in a hospital.

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Boston-Housing-Price-Prediction

In this project, we will evaluate the performance and predictive power of a model that has been trained and tested on data collected from homes in suburbs of Boston, Massachusetts. A model trained on this data that is seen as a good fit could then be used to make certain predictions about a home — in particular, its monetary value. This model would prove to be invaluable for someone like a real estate agent who could make use of such information on a daily basis. The dataset for this project originates from the UCI Machine Learning Repository. The Boston housing data was collected in 1978 and each of the 506 entries represent aggregated data about 14 features for homes from various suburbs in Boston, Massachusetts.

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Customer-Segmentation-by-Clustering-Analysis

his mini-project is based on this blog post by yhat. Please feel free to refer to the post for additional information, and solutions.

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Data-Wrangining-World-Bank-Project-JSON

This Project aims at working with the data sets of World bank (world_bank_projects.json) in order to solve three queries such as: 1. Find the 10 countries with most projects 2. Find the top 10 major project themes (using column 'mjtheme_namecode') 3. In point 2 above, there are some entries that have only the code and the name is missing. Create a DataFrame with the missing names filled in.

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Iris-Flower-Classification

The aim is to classify iris flowers among three species (setosa, versicolor or virginica) from measurements of length and width of sepals and petals. The iris data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. The central goal here is to design a model which makes good classifications for new flowers or, in other words, one which exhibits good generalization.

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Logistic-Regression--Springboard

A Logistic Regression pipeline involving modeling of the datasets of heights and weights , tuning the model for hyper-parameter optimization and checking the accuracy of the model through Grid-Search Cross Validation. We also get to know the intuition behind the Logistic Regression algorithm and interpreting the probabilistic model of it.

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tensorflow-yolov4-tflite

YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.0, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite

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Ultimate-Technologies-Inc.-

The neighboring cities of Gotham and Metropolis have complementary circadian rhythms: on weekdays, Ultimate Gotham is most active at night, and Ultimate Metropolis is most active during the day. On weekends, there is reasonable activity in both cities. However, a toll bridge, with a two way toll, between the two cities causes driver partners to tend to be exclusive to each city. The Ultimate managers of city operations for the two cities have proposed an experiment to encourage driver partners to be available in both cities, by reimbursing all toll costs.

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