Faridabad, Haryana Able to work in a learning and challenging environment, utilizing my skills and knowledge to the best of my ability and contribute positively to my personal growth as well as growth of the organization. Work Experience Data Annotator KoiReader Technologies Pvt. Ltd. - Bengaluru, Karnataka October to September KoiReader Technologies Private Limited is a Private incorporated on 08 February . Achievements/Tasks &bull; Web scraping for gathering data for labeling. &bull; Labeling data for running Machine Learning models on tools like LabelStudio, LabelImg, LabelMe, Doccano etc. &bull; Testing and improving the performance of different Machine Learning models on the labeled data. &bull; Using coding and scripts to speed up the labeling process wherever possible. Contact : Alaap Dhall - Senior Machine Learning Engineer Education B. Tech. in Information Technology Guru Tegh Bahadur Institute of Technology - Delhi, Delhi September to August Skills / IT Skills &bull; Python (3 years) &bull; Microsoft Office (1 year) &bull; Machine Learning (1 year) &bull; Data Science (1 year) &bull; Data Annotation (1 year) Certifications and Licenses Python April to Present Machine Learning September to Present Projects / Papers Presented Sentimental Analysis on SEC EDGAR Financial Reports using Python November This project performs textual analysis on the financial reports to drive sentiment opinion, sentiment scores, readability etc. The project is categorized into 3 Parts: 1. Data Extraction 2. Data Parsing 3. Textual Analysis Data Extraction is done by extracting data from links containing SEC EDGAR financial reports. Data Parsing involves organizing the extracted data into easily readable text by removing unnecessary HTML tags, non-significant words in the report. Textual Analysis includes all the necessary steps taken to analyse the sentiment score of the text, whether it is positive, negative, or neutral, only on 3 different sections i.e. Management&rsquo;s Discussion and Analysis (MDA), Quantitative and Qualitative Disclosures about Market Risk (QQDMR), and Risk Factors (RF), in each report. Car Damage Detection and Classification using Keras July This project detects damages in images of car (if any) in namely 2 classes i.e. &quot;Scratch_or_spot&quot; and &quot;Dent&quot; using Faster-RCNN pretrained resnet50 weights on XML annotations. Test Accuracy: 79.4% - 3 runs. Custom VGG-16 model for Image Classification Task February Created a light model architecture based on VGG-16 for a basic problem like Dog-Cat Image classification, achieved best training accuracy: 95% and test accuracy: 91.2% - 9 runs. Additional Information TECHNICAL SKILLS: Languages: C, C++, Python. Web Technologies: HTML, CSS, Django (Python). Data bases: SQL Server Express , SSDT-BI Visual Studio Applications: MS OFFICE, Tableau, Anaconda Spyder Python IDE Operating System: Windows XP/7/8/8.1/10.