Suhail Parakkal Data Scientist

adxtyl@r.postjobfree.com +918********* Bangalore, India

https://www.linkedin.com/in/suhail767/ https://github.com/suhail767 https://www.hackerrank.com/suhail767 https://www.kaggle.com/suhailparakkal https://trailblazer.me/id/suhail767

PROFESSIONAL EXPERIENCE

Data Scientist

Capgemini

05-2021 – present Bangalore, India

•Developed, managed and analysed reports using Python and SQL, performed CRUD operations, Salesforce

Administration using Salesforce platform and

Dataloader.

•Automated MS Excel tasks using Python, Pandas and openpyxl, reducing task completion time by over 90%.

•Visualized and communicated insights from event

reports, enabling stakeholders and decision-makers to make data-driven decisions.

•Formulated market entry research and competitor

analysis by considering various datasets, such as search engine performance, sales numbers, market presence, online presence, social media, and eCommerce.

•Automated eCommerce product research using Python and Selenium to gather product data, analyzing search volume and interest for a specific category and

keyword.

EDUCATION

Manipal Academy of Higher Education

B.Tech Aeronautical Engineering (CGPA: 6.72)

08-2016 – 07-2020 Manipal, India

SKILLS

Programming Languages (Python, Java, C)

Data Engineering (MySQL, Snowflake, Pandas, Numpy, Matplotlib, Tableau, Data Visualisation, Statistics, Git, Docker) Machine Learning (Natural Language Processing, Computer Vision, CNN, Scikit-learn, Fastai, PyTorch, Tensorflow, Transfer Learning, Hugging face)

Web development (Flask, HTML, CSS, Django, Gradio) Salesforce (Dataloader, SOQL, Reports, Apex)

COURSES

Machine Learning

Stanford Online (Coursera)

Python & DevOps Automation Certification

GI&A Academy, Capgemini University

German - A2

Deutsche Welle

Data Engineering Zoomcamp

Datatalks.club

PROJECTS

Recommendation System

Similar Product Finder using Computer Vision and NLP

•Developed a Python-based Flask web application,

Similar Product Finder, for recommending similar

products based on user preferences.

•Leveraged Natural Language Processing (NLP)

techniques and a pre-trained VGG16 Convolutional

Neural Network (CNN) model for feature extraction

from product titles, tags, and images.

•Utilized K-means clustering to group similar products efficiently and improve recommendation accuracy.

•Integrated Celery and Redis to handle asynchronous task processing and ensure smooth performance.

•Considered product IDs, titles, tags, prices, and images as features for comparing and identifying similar

products.

•Programming Language: Python

•Machine Learning Models: VGG16, Word2Vec

•Algorithms and Techniques: NLP, CNN, K-Means

Clustering, Cosine Similarity

•Libraries and tools: Pandas, NumPy, Keras, Scikit-learn, Matplotlib, Gensim, NLTK, Requests, PIL, JSON, Git, GitHub.

•UI: Flask, HTML, CSS

Apple Classification using Deep Learning

•Developed a deep learning model to classify apples as fresh or rotten using images of the fruit.

•Used transfer learning to fine-tune a pre-trained convolutional neural network (ResNet18) on a dataset of Apple images after preprocessing.

•Implemented the model using Python and Fastai,

achieving an accuracy of 97.5%.

•Created a simple Gradle web application allowing users to upload images of apples and get a prediction of their freshness status.

•Programming Language: Python

•Machine Learning Models: ResNet-18

•Algorithms and Techniques: CNN

•Libraries and tools: Fastai, Hugging Face Transformers, Hugging Face Spaces, Pandas, NumPy

•Model Deployment: Hugging Face Model Hosting

•UI: Gradio

LANGUAGES

English German (A2) Arabic Hindi

INTERESTS

Manchester United, Formula 1, Mountains, Motorbike

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