the following is the resume of a person. Some information in the resume is masked. Please replace the <MASK> in the resume with a appropriate word.
Resume:
<MASK>s * Programming Languages: Python (pandas, numpy, scipy, scikit-learn, matplotlib), <MASK>, NAME_1, JavaScript/JQuery. * Machine <MASK>: Regression, <MASK>, NaÃ¯ve <MASK>, KNN, Random Forest, Decision Trees, Boosting techniques, Cluster Analysis, Word Embedding, Sentiment Analysis, Natural Language processing, Dimensionality reduction, Topic Modelling (LDA, NMF), <MASK> & <MASK> Nets. * Database Visualizations: Mysql, <MASK>Server, Cassandra, Hbase, ElasticSearch D3.js, DC.js, Plotly, kibana, matplotlib, ggplot, Tableau. * Others: <MASK> Expression, HTML, CSS, Angular <MASK>, Logstash, NAME_2, Python <MASK>, Git, Docker, computer vision - Open CV and understanding of Deep <MASK>.Education Details 

Data <MASK> Assurance <MASK> 

Data <MASK> Assurance <MASK> - <MASK> & <MASK> LLP
<MASK> Details 
JAVASCRIPT- <MASK> - 24 months
jQuery- <MASK> - 24 months
Python- <MASK> - 24 monthsCompany Details 
company - <MASK> & <MASK> LLP
description - Fraud <MASK> and Dispute Services   Assurance
TECHNOLOGY ASSISTED REVIEW
TAR (Technology <MASK> Review) assists in <MASK> the review process and run <MASK> and <MASK> reports.
* Core <MASK> of a team helped in developing automated review platform <MASK> from scratch for assisting E discovery domain, this <MASK> implements predictive coding and topic modelling by automating reviews, resulting in reduced <MASK> costs and time spent during the lawyers review.
* Understand the end to end flow of the <MASK>, doing re<MASK> and <MASK> for classification models, predictive analysis and mining of the information present in text <MASK>. Worked on analyzing the <MASK> and precision monitoring for the entire <MASK>.
* TAR assists in predictive coding, topic modelling from the evidence by following <MASK> standards. <MASK> the classifier models in order to identify "red flags" and fraud-related issues.

Tools & Technologies: Python, scikit-learn, tfidf, word2vec, doc2vec, cosine similarity, NaÃ¯ve <MASK>, LDA, NMF for topic modelling, Vader and text blob for sentiment analysis. Matplot lib, Tableau dashboard for reporting.

MULTIPLE DATA <MASK> AND ANALYTIC PROJECTS (<MASK> CLIENTS)
TEXT <MASK> - MOTOR VEHICLE CUSTOMER REVIEW DATA * Received customer feedback survey <MASK> for past one year. Performed sentiment (Positive, Negative & Neutral) and time series analysis on customer comments across all 4 categories.
* Created heat map of terms Your response should contain less than 400 words.
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