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alice

PERSONAL SECRATORY

analysis_wiki_pages_multithreading

assignment_1_feb_2020

bank_churn_classification_tensorflow_serving

bert-indian-judges-name-extraction

Indian judge name entity extraction project

coding-problems-solution

customer-segment-s-identfication

Job was to identify segment's of different customer landing on departmental store so that wholesale distributor can make out it's further decision regarding distribution scheme of 5 days to 3 days , A/B testing , using unsupervised learning and principal component analysis finding out correct no of components , elbow method and silhouette analysis of clustering Size = 400 Customer's Data

data-analytics-on-us-flights

data-engineer-nanodegree-projects-udacity

Projects done in the Data Engineer Nanodegree Program by Udacity.com

data-engineering-nanodegree

Projects done in the Data Engineering Nanodegree by Udacity.com

data-engineering-nanodegree-1

notebooks produced throughout the Udacity's Nanodegree Data Engineering Course

deep-autoencoders-collaborative-filtering-production

Collaborative Filtering is a method used by recommender systems to make predictions about an interest of an specific user by collecting taste or preferences information from many other users. The technique of Collaborative Filtering has the underlying assumption that if a user A has the same taste or opinion on an issue as the person B, A is more likely to have B’s opinion on a different issue. In this project I predict the ratings a user would give a movie based on this user's taste and the taste of other users who watched and rated the same and similar movies.

devops_assigment

devopsclasscodes

edureka_devops_projects

A repository where devops assignments have been practised

emotion_detection_cl_lab

enron-intent-classification-

Problem statement Intent detection on Enron email set. We define "intent" here to correspond primarily to the categories "request" and "propose". In some cases, we also apply the positive label to some sentences from the "commit" category if they contain datetime, which makes them useful. Detecting the presence of intent in email is useful in many applications, e.g., machine mediation between human and email. The dataset contains parsed sentences from the email along with their intent (either 'yes' or 'no'). You need to build a learning model which detects whether a given sentence has intent or not. Its a 2-class classification problem. Although its not required you can refer this paper for more information on the dataset : Cohen, William W., Vitor R. Carvalho, and Tom M. Mitchell. "Learning to Classify Email into``Speech Acts''." EMNLP. 2004.

fake-news-classifier-stacked-nets-

Web scraped data from New york times and kaggle dataset to build dataset and to fake news classifier

financ-news-data-nlp-augumentation

flight-data-analytics

Analysing various flight arrival and departure delay Analysing particular flight of each category arrival and departure delay Multivariate analysis on Arrival delay , Departure Delay and Distance Plotting charts on various analysis Statistical Modelling Size= 1 lakh record's

fraud-detection-production

Fraud Detection in financial data using auto-encoders deploy-able code

fraud-detection-with-auto-encoder-v1.0

fraud-detection-with-auto-encoder-v1.o

hacker_news_pipeline

we will use the pipeline we have been building, and apply it to a real world data pipeline project. From a JSON API, we will filter, clean, aggregate, and summarize data in a sequence of tasks that will apply these transformations for us. The data we will use comes from a Hacker News (HN) API that returns JSON data of the top stories in 2014. If you're unfamiliar with Hacker News, it's a link aggregator website that users vote up stories that are interesting to the community. It is similar to Reddit, but the community only revolves around on computer science and entrepreneurship posts.

hr-analytics-and-predictive-modelling

Job was to analyse various correlative reason's of employees leaving the company , analysis on what types employees are suffering from this , finding-out in which dept these employees's are showing there presence , prediction of each individual of each dept of whether they leave or not , accuracy and precision on that model , model evaluation size = 15000 employees

human_behavior_classification-v1.0-

hyperspectral-swt-cnn-classification

image segmentation project going in IEEE In collaboration with GITAM University , research used was of wavelet transformation with CNN on two dataset Paviau and Indian pines

kaggle-cifar10-extract

Python script for extraction of pixel data from CIFAR10 images

milvus

Vector database for scalable similarity search and AI applications.

movie-sentiment-analysis

Sentiment analysis is a challenging subject in machine learning. People express their emotions in language that is often obscured by sarcasm, ambiguity, and plays on words, all of which could be very misleading for both humans and computers. There's another Kaggle competition for movie review sentiment analysis. The labeled data set consists of 50,000 IMDB movie reviews, specially selected for sentiment analysis. The sentiment of reviews is binary, meaning the IMDB rating < 5 results in a sentiment score of 0, and rating >=7 have a sentiment score of 1. No individual movie has more than 30 reviews. The 25,000 review labeled training set does not include any of the same movies as the 25,000 review test set. In addition, there are another 50,000 IMDB reviews provided without any rating labels. Accuracy at two epochs with my deep stack models .95 and AUC score 1.00 with F1 - score .94