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Liittynyt syyskuuta 17, 2022

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Saumaan M.

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Uudelleenpalkkausaste

Data Scientist

I'm a Data Scientist with experience in executing data-driven solutions to increase the efficiency, accuracy, and utility of internal data processing. Experienced at creating data regression models, using predictive data modeling, machine learning, natural language processing, and analyzing data mining algorithms to deliver insights and implement action-oriented solutions to complex business problems. I enjoy being challenged and engaging with projects that require me to work outside my comfort and knowledge set, as continuing to learn new languages and development techniques are important to me.

Katso kaikki Python Developers paikassa India

Ota yhteyttä käyttäjään Saumaan M. työhösi liittyen

Kirjaudu sisään keskustellaksesi yksityiskohdista chatin välityksellä.

Portfoliokohteet

Data Science approach for Sales Strategies & Budgeting

PLANT DISEASE DETECTION USING ML AND UAV

Medical specialty from Transcription

A recent study estimated that no-shows cost the U.S. health care system more than $150 billion a year and individual physicians an average of $200 per unused time slot.
What if you could predict your no-show percentage and identify appointments on your schedule at risk of a no-show?
Predictable does just that. With our powerful no-show predictions, your practice can go a long way in preparing your clinic to optimize your scheduling and work with patients to avoid no-shows.
Predictable offers a powerful library of ready-to-use data science-based predictive models.

https://coherencetechnologies.com/labs

Predictable

LaneNet: Road Lane Detection Networks-for Autonomous Driving

Restaurant recommender system

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Kokemus

British American Tobacco

lokak. 2021 - Voimassa

• Worked with the leaf analytics team to develop a model to predict tobacco yield delivered by the farmers based on weather and soil information with the end-to-end pipeline. • With the help of Statical(A/B) testing and predictive modeling to decide the best selling strategy and to guide future budget-setting • Using Time series forecasting to identify the extent of loss of potential sales due to a new competitor. • Created dashboards and web apps using DASH and Plotly

TomTom

kesäk. 2021 - Voimassa

•Implemented the auto-serving of task model (NLP and Graph analytics ) to assign the task to the MAP editors. Which speeds up the process by more than 20x times while maintaining the same efficiency. •Created 360 view editors to predict the wait time between the tasks. This alert increases productivity by 28% •Using Pysprak and Scala, I assisted in the optimization of data pipelines on Azure. • With the help of Reporting team created dashboards for Business Stakeholders(Apple, Microsoft, etc.,).

Locobuzz

tammik. 2022 - elok. 2022 (7 kuukautta, 1 )

•Created & deployed the Emotion, Toxicity & Sentiment detection models(NLP) to analyze social media posts, comments, & messages in real-time •With statistical analysis and predictive modeling, Optimize insights to maximize reach, engagement, and ROI •Using machine learning, we have created effective online reputation management for our customers(HDFC, Nestle, Reliance Jio, etc.,) •Created a chatbot for lead generation, customer support, campaign & crisis management across every messaging channel

Koulutus

Master of Technology (MTech) in Data Science & Engineering

Birla Institute of Technology and Science,

India

2020 - 2022

(2 vuotta)

Bachelor of Engineering (BEng) in Information Technology

University of Mumbai,

India

2016 - 2020

(4 vuotta)

Pätevyydet

Google

2019

Recognize the data-to-AI lifecycle on Google Cloud and the major big data and machine learning products. Analyze big data at scale with BigQuery. Identify different options to build machine learning solutions on Google Cloud. Describe a machine learning workflow and the key steps with Vertex AI.

Stanford University

2019

It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)

IBM

2019

The badge earner has demonstrated a good understanding and application of machine learning (ML) including when to use different ML techniques such as regression, classification, clustering and recommender systems. The individual has acquired the skills to use different machine learning libraries in Python, mainly Scikit-learn and Scipy, to generate and apply different types of ML algorithms such as decision trees, logistic regression, k-means, KNN, DBSCCAN, SVM and hierarchical clustering.

Julkaisut

PLANT DISEASE DETECTION USING ML AND UAV

International Research Journal of Engineering and Technology

The manual detection of disease realistically is an extremely challenging task. We can apply machine learning algorithms to develop a system that correctly identifies diseases in leaves. This will typically assist farmers to minimize crop failure and accurately identify the infections in plants more precisely and effortlessly. In this system, we also automate the process of capturing images of the leaves using an unmanned aerial vehicle(UAV) with a camera that automatically captures the images.