Surendra Kumar Shringi

Data Scientist

https://talent.gravityer.com/surendra-kumar-shringi

Bengalore, IndiaMember since June 16, 2026

Key Strengths

Demonstrated experience in various machine learning applications including NLP (Spam Filtering, Chatbot, Language Detection, Sentiment Analysis), deep learning (DNN for sentiment, Object Recognition with CNNs), and traditional ML (K-NN, K-Means, Naive Bayes, SVM).
Proficiency in Python for machine learning tasks, utilizing libraries like Scikit-learn, NLTK, and Keras.
Experience with statistical analysis using R, as shown in the Titanic-Sink-Analysis project.
Practical application of ML concepts on publicly available datasets and participation in a HackerEarth challenge.

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's projects are exclusively personal and academic in nature, focusing heavily on machine learning and data science. While this aligns with the 'Data Scientist' target role, the lack of team-based projects, professional experience, or diverse domain exposure limits the assessment of cultural fit. The projects demonstrate individual initiative and a strong interest in the field.

Soft Skills & Operational Fit

The provided data is insufficient to assess soft skills or operational fit. Project descriptions indicate a focus on technical implementation and problem-solving.

AI is analyzing your overall score…

Identifying your key strengths…

Evaluating your skill match against the job requirements…

Assessing your cultural and operational fit

Projects

hello-world

October 3, 2019 – March 12, 2020

hello-world — GitHub repository

View Project

ChatBot

December 19, 2018 – December 19, 2018

This ChatBot is based on Python with NLTK. Its a basic chatbot.

View Project

index

June 17, 2018 – June 17, 2018

My_Site

View Project

Language-Detection-From-Text---Bi-gram-based

May 6, 2018 – May 6, 2018

Language-Detection-From-Text---Bi-gram-based It uses Bi-gram language model and bi-gram frequency addition classifier for language identification task. Trained over 6 languages namely German, English, Spanish, French, Italian and Dutch. The original source of the text corpus is wortschatz leipzig corpora. Both the train and test corpus were taken from this corpora. The training corpus consists of 30000 sentences from news/web domain. Test corpus 10000 unseen sentences from news/web domain. Also, the chosen six languages were such that the same languages are present in the LIGA twitter dataset which consists of 9066 tweets. Note : Directory path used for train and test corpus in code language-test.py, language-train.py and liga_test.py needs to be properly set accordingly.

View Project

Predict-the-Happiness-HackerEarth-Challenge

May 6, 2018 – May 6, 2018

It uses 2-layered fully connected/Dense Neural network model to predict whether the hotel reviews at TripAdvisor site are positive sentiment or negative sentiment. It is a python implementation utilizing Keras library for DNN. This problem statement came from a HackerEarth challenge: "Predict the Happiness" The accuracy score achieved was 88% when prediction file (sample_submisson.csv) is uploaded to their portal. The link for corpus/dataset download is given in blog-post.

View Project

Object-recognition

May 6, 2018 – May 6, 2018

In this blog-post, we will demonstrate how to achieve 90% accuracy in object recognition task on CIFAR-10 dataset with help of following concepts: 1. Deep Network Architecture 2. Data Augmentation 3. Regularization

View Project

Sentiment-Analysis-using-tf-idf---Polarity-dataset

May 6, 2018 – May 6, 2018

It uses machine learning models to do sentiment polarity analysis on movie reviews. In other words, to classify opinions expressed in a text review (document) in order to determine whether the reviewer’s sentiment towards the movie is positive or negative.

View Project

Mail-Spam-Filtering

May 6, 2018 – May 6, 2018

Mail-Spam-Filtering It uses machine learning models to predict whether the email is spam or ligitimate. Best thing would be to follow my blog-post for implementation. The description about the steps to build a spam filter from scratch can be read from my blog: https://appliedmachinelearning.wordpress.com/2017/01/23/nlp-blog-post/ It is a python implementation using Naive Bayes Classifier and Support Vector Machines from Scikit-learn ML library. The results has been shown on two publicly open corpus. Ling-spam corpus Euron-spam corpus The link for corpus/dataset download is given in blog-post. Note : Directory path used for training and testing models in lingspam_filter.py and euron-spamfilter.py needs to be properly set accordingly.

View Project

Text-classification-and-clustering

May 6, 2018 – May 6, 2018

It demonstrates the example of text classification and text clustering using K-NN and K-Means models based on tf-idf features.

View Project

Titanic-Sink-Analysis

April 9, 2018 – April 9, 2018

The project is based on statistical analysis with R, which provides the survival prediction based on age,sex ratio,tickets,male,female,children etc.

View Project

Key Strengths

Demonstrated experience in various machine learning applications including NLP (Spam Filtering, Chatbot, Language Detection, Sentiment Analysis), deep learning (DNN for sentiment, Object Recognition with CNNs), and traditional ML (K-NN, K-Means, Naive Bayes, SVM).
Proficiency in Python for machine learning tasks, utilizing libraries like Scikit-learn, NLTK, and Keras.
Experience with statistical analysis using R, as shown in the Titanic-Sink-Analysis project.
Practical application of ML concepts on publicly available datasets and participation in a HackerEarth challenge.

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit

The provided data is insufficient to assess soft skills or operational fit. Project descriptions indicate a focus on technical implementation and problem-solving.

Surendra Kumar Shringi

Key Strengths

Cultural & Operational Fit

Top Skills

Skills

Projects

Key Strengths

Cultural & Operational Fit