Data Scientist with over 5 years of experience in extracting valuable insights from complex data to drive business growth and solve intricate problems. Expertise in collecting, cleaning, analyzing, and modeling data usin…
Data Scientist with over 5 years of experience in extracting valuable insights from complex data to drive business growth and solve intricate problems. Expertise in collecting, cleaning, analyzing, and modeling data using Python, machine learning, deep learning, and natural language processing. Proven ability to collaborate with cross-functional teams to identify and solve complex business problems.
Data Scientist with over 5 years of experience in extracting valuable insights from complex data to drive
business growth and solve intricate problems. Expertise in collecting, cleaning, analyzing, and modeling data
using Python, machine learning, deep learning, and natural language processing. Proven ability to collaborate
with cross-functional teams to identify and solve complex business problems.
Experience in development of an AI based copy editing product using Spacy library and Grobid to extract the business requirement related to Numbers.
Identified Longform and Short form abbreviation with the help of Spacy and Schwartz-hearst algorithm.
Created API using flask framework.
Utilized Spacy library in order to identify Numbers as first level classification.
Utilized Grobid to extract units and unfound numbers with unit.
Extracted as second level classification.
Integrated the extraction data to get the output.
Adding business logic rules in Python typically involves writing code that reflects the specific rules and requirements.
Employed LSTM algorithm in creating a text classification model for Frontmatter Parastyle and Character style.
Ensuring custom tokenizer to split every Designator, punctuations and numbers.
Split and trained few models and Integrated to get output.
Created a text classification using Spacy to identify the Reference Parastyle and Character style.
Used Spacy and Schwartz-Hearst algorithm to find the abbreviation.
Business rule for capitalized unfound abbreviated word.
Integrated to get output.
Project: Loan Approval Prediction.
Domain: Banking.
Problem Statement: Prediction of approval rate of a loan by using multiple labels.
Topics: Data Analysis, Data preprocessing, Cleaning Ops, Data Visualization.
Performing Data Preprocessing.
Building a model and applying PCA.
Building a Naïve Bayes model on the training dataset.
Prediction of values after performing analysis.
Project: Analyzing the Naming Pattern Using Python.
Problem Statement: How to analyze the trends and the most popular baby names.
Analyzing data using Pandas Library.
Deploying Data Frame Manipulation.
Bar and box plots with Matplotlib.
Project: Customer Churn Classification.
نظام التوظيف التشغيلي المعتمد على الذكاء الاصطناعي أولاً، الذي يوحّد ويؤتمت دورة التوظيف بالكامل للمؤسسات الحديثة حول العالم.
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