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Name: Vaibhav Verma
Type: User
Location: Los Angeles, California
Name: Vaibhav Verma
Type: User
Location: Los Angeles, California
100 Days of ML Coding
Analyzed the Twitter Reviews for the 5 major US Airlines and found insights on the improvements. Used Natural Language Processing in Python to classify an airline review text. Trained the model using a variety of machine learning algorithms such as logistic regression, random forest, knn for tweet classification prediction and tested using model evaluation techniques such as holdout evaluation, cross-validation, ROC and AUC curve to conclude the best model. Used grid search method for hyperparameter tuning to find the best tuned model with an accuracy of 77.4%
To Analyze Edmund automobile discussion forum to understand the brand similarities, their association with other brands and important attributes.
Bypass Paywalls web browser extension for Chrome and Firefox.
Calculator in AngularJS
Analyzed the impact of CEO tweets on stock market and made recommendations to make them more influential
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
Analysed global superstore data and drew insights using exploratory data analysis
Analyzed a Portuguese bank dataset to determine whether a customer would agree to a term deposit or not in a time span of less than 24 hours using machine learning techniques of Random Forest and Logistic Regression classification in R.
Exploring this topic as I think it is important to categorize hate speech and understand the sentiment behind it. There is a very fine line on whether to classify a tweet as hate speech and we are looking to define this threshold.To build a model which classifies tweets as hate speech/racist one or not. Also considered an alternative model which assigns these tweets a score between 0 and 1 which signifies the hatred/racism measure.
Code for Head First Design Patterns book (2014)
Analyzed an HR dataset with the aim to help companies to solve the employee attrition problem. Data exploration analysis was conducted on the dataset to derive insights and a classification model was recommended to predict whether an employee is likely to leave the company.
A python script to convert webpages into .mobi format for reading on Kindle
Engagement analysis of the Instagram posts for National Geography using image labels and captions
Vaibhav Verma Portfolio
Vaibhav Verma Portfolio
Html Javascript based small programmer survey
Python Monte Carlo Simulation to model returns from randomly generated portfolios against a benchmark index.
Analyzed the regular season and play off data for the NFL and predicted the scores for the final Superbowl 54. Ensemble model using Alteryx and R predicted Kansas City Chiefs to win with a score of 30. Voila, Kansas did win with a score of 31
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
Created a Twitter app
start
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Open source projects and samples from Microsoft.
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Data-Driven Documents codes.
China tencent open source team.