Quickly learn new skills and apply them to daily tasks for improving efficiency and productivity.
Data Extraction from different file formats like PDFs, Images using tools like ICR and OCR. Also having expertise in parsing text files for data extraction. Also, extractions were mainly done with the help of Python Data Structures.
Extensive use of Regex for data processing after data extraction.
Created a Linux service for automating the restart of the server when it goes down for some reason.
Created a Python script for preparing an automated report data coming from a mail Id. The mail id was used for tracking purposes. So my script basically automated this tracking work. The work was mainly done with the help of Python Pandas.
Extracting locations from text using Spacy’s NER (Named Entity Recognition).
Extracting Tariffs using Camelot, tabula/Excalibur,pdfminer
To run all the txt files in a single attempt I have made Automation Script So it will give Fast Results and time consumption.
Mostly Working On debugging When raises bugs those within the period I can resolve the problems
Interact with customers for Project updations, and I have good skills in client handling in meanwhile presenting demos .
I m good in latex .
Experienced Data Science Associate with a demonstrated history of working in the information technology and services industry. Skilled in Python (Programming Language), Analytical Skills, Data Science, Data Analytics, and Predictive Modeling and Deep learning.NLP, Different Algorithms of Machine Learning,.
Data Visualization using different libraries, Data extraction from APIs, Web Scraping.
Also passionate about working with large and complex data sets and converting the data into information, information to insights, insights to business decisions. I have a keen interest in the field of data visualization and data analysis and I am fascinated by the power to compress complex data sets into approachable graphics.
Experience in Machine learning algorithms like Linear and Logistic Regression, KNN, Support Vector Machines (SVM), Decision tree, Random Forest, Adaptive Boosting (ADA Boost), Extreme Gradient Boosting (XG Boost), K-Means Clustering.
Skilled in libraries like Numpy, Pandas, Matplotlib, Seaborn, Scikit learn Keras, Tensor flow, by tesseract, and OpenCV.
Strong Mathematical foundation and good in Statistics.
Feature engineering in Python – Missing value treatment and outlier handling, transforming variables, and reshaping data using python packages like Numpy, Pandas, and Scikit Learn.
Good Knowledge of Deep Learning (DL) and ample hands-on with Neural Networks, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks(RNN).
Having good knowledge of YOLO v4 architecture.
Basic Understanding of Computer Vision techniques like Image pre-processing, Image Segmentation, Object detection, Object recognition, etc.