👋 Hi, I’m Princy Pappachan Iakov!

🌟 Passionate about Data Science, Deep Learning, Computer Vision, and Natural Language Processing, I strive to harness technology for positive impact and to make life better for everyone. I believe in leveraging innovation to drive meaningful change in the world.

💡 Let’s connect and explore how we can collaborate to create something extraordinary!

• 🌐 Visit my personal website: My personal Website
• 📫 Reach me at: [email protected]
• 🔗 Connect with me on LinkedIn: My LinkedIn

🌟 Customer Conversion : The bank wants to explore ways of converting its liability customers to personal loan customers (while retaining them as depositors). A campaign that the bank ran last year for liability customers showed a conversion rate of over 9% success. This has encouraged the retail marketing department to devise campaigns with better target marketing to increase the success ratio with minimal budget.

• You can find the project here
• LANGUAGE - Python
• CONTAINERISATION - Docker
• LIBRARIES IMPLEMENTED : pandas, streamlit, plotly, seaborn, matplotlib, flask, sklearn

🌟 Dayrize Health Check : Application to check the health of a data shared by user using Python.

• You can find the project here
• LANGUAGE - Python
• CONTAINERISATION - Docker
• LIBRARIES IMPLEMENTED : pandas, streamlit, plotly

🌟 My NLP contributions for Giskard : Sentiment Analysis for twitter Data

• You can find the notebook for the project here
• LANGUAGE : Python
• LIBRARIES IMPLEMENTED : transformers, tweepy, datasets, torch and giskard
• MODELS EXPLORED : DistillBERT

🌟 Chronic Kidney Disease Progression

• You can find the project here
• LANGUAGE : Python
• LIBRARIES IMPLEMENTED : lifelines, pandas, seaborn, plotly, matplotlib
• MODELS EXPLORED : KaplanMeierFitter, KNN, Random Forest, Logistic Regression

🌟 Fraud Detection in Blockchain transactions

• You can find the notebook for the project here
• LANGUAGE : Python
• LIBRARIES IMPLEMENTED : pandas, seaborn, plotly, matplotlib
• MODELS EXPLORED : Random Forest, XGBoost, Logistic Regression

🌟 Personal Drone Programming and Computer Vision : Facial Recognition to help recognise registered missing people and self flying drone

• A project close to my heart to help recognise missing children or adults who are reigtered
• You can find the code here
• LANGUAGE : Python
• DATABASE : PostgreSQL
• PYTORCH implementation
• Facial Recognition using FaceNet (MTCNN and InceptionResnetV1)
• Registration of missing people using a simple front end Flask Implementation

🌟 My NLP contributions for Giskard : Email Classification

• You can find the notebook for the project here
• LANGUAGE : Python
• LIBRARIES IMPLEMENTED : torch, nltk, transformers, sklearn and giskard
• MODELS EXPLORED : Hugging Face BERT, Logistic Regression

🌟 My NLP contributions for Giskard : Text Classificcation using Tensorflow

• You can find the notebook for the project here
• LANGUAGE : Python
• LIBRARIES IMPLEMENTED : tensorflow, pandas and giskard
• MODELS EXPLORED : simple binary classifier

🌟 My contributions for Giskard : House Pricing Regression

• You can find the notebook for the project here
• LANGUAGE : Python
• LIBRARIES IMPLEMENTED : sklearn, pandas, numpy and giskard
• MODELS EXPLORED : Random Forest, Catboost

🌟 Personal Project - Classification for Tabular Data Project : Credit Card Default project

• You can find the notebook for the project here
• LANGUAGE : Python
• LIBRARIES IMPLEMENTED : sklearn, seaborn, matplotlib, plotly, imblearn
• MODELS EXPLORED : XGBoost, Random Forest, Decision Tree, KNN
• I have performed EDA and visualization using Matlabplot lib and Plotly
• Feature Engineering and Feature Selection
• Explored various Sampling techniques
• Will be implementing a front end for the application and creating an image on docker

🌟 First Docker Implementation : Video Process

• In my first attempt at docker implementation, I reinitiated an existing code of correcting a corrrupted video and packaged into a python library and created a docker image
• You can find the code here
• LANGUAGE : Python

🌟 Amazon Web Services(AWS) : Jump Box implementation

• You can find the implementation here