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$45 USD / hour
Flag of INDIA
ichalkaranji, india
$45 USD / hour
It's currently 11:28 AM here
Joined June 12, 2021
2 Recommendations

Keshav S.

@keshavsarda503

verified.svg
 5.0 (18 reviews)
4.5
4.5
100%
100%
$45 USD / hour
Flag of INDIA
ichalkaranji, india
$45 USD / hour
95%
Jobs Completed
94%
On Budget
95%
On Time
16%
Repeat Hire Rate

Data Scientist | Machine Learning Engineer

I am an experienced Data Scientist with skills in Tech stacks such as Cloud Computing, Machine Learning, Artificial Intelligence, Data Engineering. Work Experience: LSTM for forecasting of Stocks, Economic/Financial Time Series data, Sales and Customer Value. Creating Data Pipelines for purpose of ETL, Automation, ML Pipelines with Cloud, Python, PySpark. Personal Tutoring on ML and Cloud for educational purposes. Machine Learning using Cloud Services like Sagemaker, AutoML, Rekognition, Cloud Vision, NLP, etc. Tools on ML: Python, Data preprocessing, Scripting, ETL, SQL. Regression, Statistical Analysis, Pandas, Random Forest, Xgboost, SVM, KNN, Clustering, Deep Learning, RNN, LSTM, ARIMA, etc. Cloud Services & Big Data: PySpark, BigQuery, CosmosDB, AWS Lambda, DataFlow, DataProc, Databricks, S3, Cloud Storage, etc. AWS, GCP, Azure. Official Certifications: 1. AWS Certified Machine Learning - Specialty (March 2021) 2. AWS Certified Developer - Associate (May 2021) 3. Google Certified Professional Cloud Architect (March 2021) 4. AWS Certified Solutions Architect - Associate (July 2020) 5. Google Certified Associate Cloud Engineer (Jan 2020) After having 3+ years of full-time experience and 3+ months as a part-time freelancer, I plan to master all that I know, with the curiosity of learning and gaining new knowledge. I assure to commit to work and provide you with excellent, on time, efficient solutions.
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Portfolio

9165487
Created Python scripts for EDA, Data Cleaning, training ML models, and predictions. Explored data, applied feature engineering techniques, trained models using Logistic Regression, Random Forest, and XGBoost. 3 separate training scripts for each of the models giving output model file, ROC curve, Feature Importance graph. Another prediction script uses output model/s and new unlabeled data to predict Classification probabilities. Preprocessed by joining over the appropriate key column. Explored and cleaned by handling null and missing values. Applied PCA to reduce feature dimension. Trained 3 models with appropriate hyperparameters. Applied Inverse PCA transformation on prediction data before using model/s. Working on creating a single file for providing data, input argument (customize), other requirements/resources to implement actions. Looking to create a pipeline on Cloud. https://github.com/keshavs0305/scripting-training-and-prediction-tasks
Python Scripting : Train, Test and Predict with ML models
Created Python scripts for EDA, Data Cleaning, training ML models, and predictions. Explored data, applied feature engineering techniques, trained models using Logistic Regression, Random Forest, and XGBoost. 3 separate training scripts for each of the models giving output model file, ROC curve, Feature Importance graph. Another prediction script uses output model/s and new unlabeled data to predict Classification probabilities. Preprocessed by joining over the appropriate key column. Explored and cleaned by handling null and missing values. Applied PCA to reduce feature dimension. Trained 3 models with appropriate hyperparameters. Applied Inverse PCA transformation on prediction data before using model/s. Working on creating a single file for providing data, input argument (customize), other requirements/resources to implement actions. Looking to create a pipeline on Cloud. https://github.com/keshavs0305/scripting-training-and-prediction-tasks
Python Scripting : Train, Test and Predict with ML models
Created Python scripts for EDA, Data Cleaning, training ML models, and predictions. Explored data, applied feature engineering techniques, trained models using Logistic Regression, Random Forest, and XGBoost. 3 separate training scripts for each of the models giving output model file, ROC curve, Feature Importance graph. Another prediction script uses output model/s and new unlabeled data to predict Classification probabilities. Preprocessed by joining over the appropriate key column. Explored and cleaned by handling null and missing values. Applied PCA to reduce feature dimension. Trained 3 models with appropriate hyperparameters. Applied Inverse PCA transformation on prediction data before using model/s. Working on creating a single file for providing data, input argument (customize), other requirements/resources to implement actions. Looking to create a pipeline on Cloud. https://github.com/keshavs0305/scripting-training-and-prediction-tasks
Python Scripting : Train, Test and Predict with ML models
Created Python scripts for EDA, Data Cleaning, training ML models, and predictions. Explored data, applied feature engineering techniques, trained models using Logistic Regression, Random Forest, and XGBoost. 3 separate training scripts for each of the models giving output model file, ROC curve, Feature Importance graph. Another prediction script uses output model/s and new unlabeled data to predict Classification probabilities. Preprocessed by joining over the appropriate key column. Explored and cleaned by handling null and missing values. Applied PCA to reduce feature dimension. Trained 3 models with appropriate hyperparameters. Applied Inverse PCA transformation on prediction data before using model/s. Working on creating a single file for providing data, input argument (customize), other requirements/resources to implement actions. Looking to create a pipeline on Cloud. https://github.com/keshavs0305/scripting-training-and-prediction-tasks
Python Scripting : Train, Test and Predict with ML models
Created Python scripts for EDA, Data Cleaning, training ML models, and predictions. Explored data, applied feature engineering techniques, trained models using Logistic Regression, Random Forest, and XGBoost. 3 separate training scripts for each of the models giving output model file, ROC curve, Feature Importance graph. Another prediction script uses output model/s and new unlabeled data to predict Classification probabilities. Preprocessed by joining over the appropriate key column. Explored and cleaned by handling null and missing values. Applied PCA to reduce feature dimension. Trained 3 models with appropriate hyperparameters. Applied Inverse PCA transformation on prediction data before using model/s. Working on creating a single file for providing data, input argument (customize), other requirements/resources to implement actions. Looking to create a pipeline on Cloud. https://github.com/keshavs0305/scripting-training-and-prediction-tasks
Python Scripting : Train, Test and Predict with ML models
9122392
I have created a workflow for an automated Machine Learning System. It starts with taking new data, joining it with old data if available. Then it starts training with the required ML algorithm. After saving the trained model, it deploys that to an endpoint. Workflow is triggered after a new data file comes to an S3 location. Step function state machine is where the rest of the process is done. The task of joining data is done by Lambda, which is responsible to join new data with old data if any. The Task of training is done next and Saving the trained model then. The last Task is done by Lambda by deploying the endpoint.
Automatic Model Training and Endpoint Deployment
9122363
Automatic Text Reading and Language Translation
9165487
Created Python scripts for EDA, Data Cleaning, training ML models, and predictions. Explored data, applied feature engineering techniques, trained models using Logistic Regression, Random Forest, and XGBoost. 3 separate training scripts for each of the models giving output model file, ROC curve, Feature Importance graph. Another prediction script uses output model/s and new unlabeled data to predict Classification probabilities. Preprocessed by joining over the appropriate key column. Explored and cleaned by handling null and missing values. Applied PCA to reduce feature dimension. Trained 3 models with appropriate hyperparameters. Applied Inverse PCA transformation on prediction data before using model/s. Working on creating a single file for providing data, input argument (customize), other requirements/resources to implement actions. Looking to create a pipeline on Cloud. https://github.com/keshavs0305/scripting-training-and-prediction-tasks
Python Scripting : Train, Test and Predict with ML models
Created Python scripts for EDA, Data Cleaning, training ML models, and predictions. Explored data, applied feature engineering techniques, trained models using Logistic Regression, Random Forest, and XGBoost. 3 separate training scripts for each of the models giving output model file, ROC curve, Feature Importance graph. Another prediction script uses output model/s and new unlabeled data to predict Classification probabilities. Preprocessed by joining over the appropriate key column. Explored and cleaned by handling null and missing values. Applied PCA to reduce feature dimension. Trained 3 models with appropriate hyperparameters. Applied Inverse PCA transformation on prediction data before using model/s. Working on creating a single file for providing data, input argument (customize), other requirements/resources to implement actions. Looking to create a pipeline on Cloud. https://github.com/keshavs0305/scripting-training-and-prediction-tasks
Python Scripting : Train, Test and Predict with ML models
Created Python scripts for EDA, Data Cleaning, training ML models, and predictions. Explored data, applied feature engineering techniques, trained models using Logistic Regression, Random Forest, and XGBoost. 3 separate training scripts for each of the models giving output model file, ROC curve, Feature Importance graph. Another prediction script uses output model/s and new unlabeled data to predict Classification probabilities. Preprocessed by joining over the appropriate key column. Explored and cleaned by handling null and missing values. Applied PCA to reduce feature dimension. Trained 3 models with appropriate hyperparameters. Applied Inverse PCA transformation on prediction data before using model/s. Working on creating a single file for providing data, input argument (customize), other requirements/resources to implement actions. Looking to create a pipeline on Cloud. https://github.com/keshavs0305/scripting-training-and-prediction-tasks
Python Scripting : Train, Test and Predict with ML models
Created Python scripts for EDA, Data Cleaning, training ML models, and predictions. Explored data, applied feature engineering techniques, trained models using Logistic Regression, Random Forest, and XGBoost. 3 separate training scripts for each of the models giving output model file, ROC curve, Feature Importance graph. Another prediction script uses output model/s and new unlabeled data to predict Classification probabilities. Preprocessed by joining over the appropriate key column. Explored and cleaned by handling null and missing values. Applied PCA to reduce feature dimension. Trained 3 models with appropriate hyperparameters. Applied Inverse PCA transformation on prediction data before using model/s. Working on creating a single file for providing data, input argument (customize), other requirements/resources to implement actions. Looking to create a pipeline on Cloud. https://github.com/keshavs0305/scripting-training-and-prediction-tasks
Python Scripting : Train, Test and Predict with ML models
Created Python scripts for EDA, Data Cleaning, training ML models, and predictions. Explored data, applied feature engineering techniques, trained models using Logistic Regression, Random Forest, and XGBoost. 3 separate training scripts for each of the models giving output model file, ROC curve, Feature Importance graph. Another prediction script uses output model/s and new unlabeled data to predict Classification probabilities. Preprocessed by joining over the appropriate key column. Explored and cleaned by handling null and missing values. Applied PCA to reduce feature dimension. Trained 3 models with appropriate hyperparameters. Applied Inverse PCA transformation on prediction data before using model/s. Working on creating a single file for providing data, input argument (customize), other requirements/resources to implement actions. Looking to create a pipeline on Cloud. https://github.com/keshavs0305/scripting-training-and-prediction-tasks
Python Scripting : Train, Test and Predict with ML models
9122392
I have created a workflow for an automated Machine Learning System. It starts with taking new data, joining it with old data if available. Then it starts training with the required ML algorithm. After saving the trained model, it deploys that to an endpoint. Workflow is triggered after a new data file comes to an S3 location. Step function state machine is where the rest of the process is done. The task of joining data is done by Lambda, which is responsible to join new data with old data if any. The Task of training is done next and Saving the trained model then. The last Task is done by Lambda by deploying the endpoint.
Automatic Model Training and Endpoint Deployment
9122363
Automatic Text Reading and Language Translation

Reviews

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Showing 1 - 5 out of 18 reviews
Filter reviews by:
5.0
₹6,000.00 INR
Develop Speech Analytics App
Exceptional, delivered quality code within few days. Highly recommended
Java
Python
Machine Learning (ML)
Deep Learning
L
Flag of Lokesh S.
@lokeshpalak
•
2 years ago
3.0
$500.00 USD
Criar forecast de clientes usando Amazon Forecast
Haven´t delivered the project, even delaying many times the date of project delivery.
Software Architecture
Machine Learning (ML)
Amazon Web Services
Artificial Intelligence
Budgeting and Forecasting
User Avatar
Flag of Tássio M.
@tassiomarq
•
2 years ago
5.0
$102.05 CAD
Need small fix on Lambda function : MySQL : DevOps: Laravel Backend. -- 2
Keshav showcased great efficency with Lamba function and with delivering a project in a tight deadline.
Python
Machine Learning (ML)
Amazon Web Services
Google Cloud Platform
+1 more
User Avatar
Flag of Antonio B.
@BrossardDesign
•
2 years ago
5.0
$250.00 CAD
OTA updates on AWS IoT Core configuration.
good experience with AWS services. They are Patient and are willing to learn to up their implementaion.
Amazon Web Services
Internet of Things (IoT)
User Avatar
Flag of Antonio B.
@BrossardDesign
•
3 years ago
5.0
$50.00 USD
Project 31692255 has been deleted
Keshav is really helpful and expert. He tried the best and delivered a very good work.
R
Closed User
@rooyash
•
3 years ago

Experience

Data Scientist

Bosch
Mar 2022 - Present
Deep Learning, Pytorch, Computer Vision, GPU

Engineer Technology

Virtusa Consulting Services
Jul 2019 - Feb 2022 (2 years, 7 months)
1. Researched 2-3 Quantum Algorithms with the perspective of implementing Machine Learning Algorithms. 2. Monitored and managed 900+ BigData Pipelines using Dataflow, Dataproc, BigQuery, Stackdriver, DataStudio. 3. Resolved 8000+ ServiceNow incidents achieving 99.98% SLAs. 4. Performed activities like Data and Memory Cleanup in a timely manner using AppEngine application ensuring uptime of VM Instances. 5. Responsibly communicated handovers during a shift change of 24x7 Managed Operations.

Research Consultant

WorldQuant
Jul 2018 - Jul 2019 (1 year)
1. Created more than 125+ market-neutral strategy alphas over a sharp ratio of 2.0 and achieved a weightage of 1% in a production environment. 2. Applied creative statistical techniques as a Data Engineering method to clean data and reduce the correlation of alpha. 3. Implemented ML algorithms to create complex and robust alphas.

Education

BS Physics

Indian Institute of Technology, Kanpur, India 2013 - 2018
(5 years)

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Top Skills

Python 14
Amazon Web Services 8
Machine Learning (ML) 7
PySpark 4
Google Cloud Platform 3

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