• 👋 Hi, I’m Princy Pappachan Iakov
• 👀 I’m interested in Data Science, Deep Learning, Computer Vision, Natural Language Processing and all things which can bring a positive influence in this world and make life better for every individual !
• 📫 You can reach me at [email protected]

🌟 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