Abhinav S.

Abhinav S.

Data Scientist with 12.9 Years of IT industry experience and 5 years of Data science experience

Gurugram , India

Experience: 13 Years

Abhinav

Gurugram , India

Data Scientist with 12.9 Years of IT industry experience and 5 years of Data science experience

36000 USD / Year

  • Immediate: Available

13 Years

Now you can Instantly Chat with Abhinav!

About Me

  • 12.9 years of IT industry experience including 5+ years of experience in the data science.
  • Perform data cleaning and data preparation in data science projects.
  • Pre-processed data by feature s...
  • Pre-processed data by feature selection, imputation, handled class imbalance, developed classification models in Python, understanding correlation between variable.
  • Performed feature engineering including feature normalize and OHE & label encoding.
  • Analyzed sentiments for upcoming product based on review given by customer.
  • Improved accuracy of classifier using cross-validation and hyper-parameter tuning.
  • Processed data to identify outliers,  inconsistencies and conducted exploratory data analysis to see the insights of data and validate each feature.
  • I have worked on ASP.NET, ADO.NET, C#, SQL server, Angular.js, Node.js. Hive, Mongodb also

Show More

Portfolio Projects

Credit Risk Analysis

Company

Credit Risk Analysis

Description

Based on details provided by customer, we have to predict that customer is eligible for loan or not.

I was involved in functional analysis of dataset and understanding of data.

Involved in Feature engineering and feature importance based on which feature selection was done.

Involved in choosing best AUC score for model and parameter tuning

Involved in saving model and contineous training.

Tech Used: Python, skLearn, matplotlib, seaborn, gc

Show More Show Less

Tools

Numpy Pandas

Customer Segmentation

Company

Customer Segmentation

Description

By collecting data for one year of purchase made by customers, I had to anticipate the purchases that will be made by a new customer, during the following year and current one, from its first purchase.

I was involved in functional analysis of dataset and understanding of data.

Involved in Feature engineering and feature importance based on which feature selection was done.

Involved in choosing best AUC score for model and parameter tuning

Involved in saving model and contineous training.

Tech Used: Python, skLearn, matplotlib, seaborn, gc

Show More Show Less

Tools

Numpy Pandas

Customer churn analysis

Company

Customer churn analysis

Description

Based on the provided information, I have to predict those customers who are likely to churn out of the bank in coming year.

I was involved in functional analysis of dataset and understanding of data.

Involved in Feature engineering and feature importance based on which feature selection was done.

Involved in choosing best AUC score for model and parameter tuning

Involved in saving model and contineous training.

Tech Used: Python, ScikitLearn

Show More Show Less

Skills

Python Sklearn

Tools

Numpy Pandas

Employee churn analysis

Company

Employee churn analysis

Description

Based on the provided information i had to predict those employees tending to churn the company in coming year.

Involved in Feature engineering and feature importance based on which feature selection was done.

Involved in choosing best AUC score for model and parameter tuning

Involved in saving model and contineous training.

Show More Show Less

Skills

Python Sklearn

Tools

Numpy Pandas

Text classification while selling appliances

Company

Text classification while selling appliances

Description

Analyzed communication between sales representatives and customers to predict whether a protection agreement was sold to the customer or not during the sale.

I was involved in word embedding and text cleaning

Involved in Parameter tuning and pipeline

Model deployment

Tech Used: Python, matplotlib, seaborn, nltk, ScikitLearn

Show More Show Less

Tools

Numpy Pandas

Text classification of comments

Company

Text classification of comments

Description

Classify the comments retrieved from website as positive, negative and normal.

I was involved in text cleaning

Involved in Parameter tuning and pipeline

Model deployment

Tech Used: Python, matplotlib, seaborn, nltk, SkLearn

Show More Show Less

Tools

Numpy Pandas

Opinion mining Sentiment analysis

Company

Opinion mining Sentiment analysis

Description

Opinion mining from twitter, analyzed given number of tweets for the product released by company provided by customer.

Tech Used: Sklearn, NLTK, Python, tweepy, TextBlob, wordCloud, seabron, matplotlib, gensim, keras

Show More Show Less

Tools

Numpy Pandas
Share:

Verifications

  • Profile Verified

  • Phone Verified

Preferred Language

  • English - Conversational

Available Timezones

  • Eastern Daylight [UTC -4]

  • Central Daylight [UTC -5]

  • Mountain Daylight [UTC -6]

  • Pacific Daylight [UTC -7]

  • Eastern European [UTC +2]

  • Eastern EST [UTC +3]

  • Greenwich Mean [UTC ±0]

  • Further EET [UTC +3]

  • Australian EDT [UTC +11]

  • Australian CDT [UTC +10:30]

  • Dubai [UTC +4]

  • New Delhi [UTC +5]

  • China (West) [UTC +6]

  • Singapore [UTC +7]

  • Hong Kong (East China) [UTC +8]