- 👋 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