a DUMMY for the SHAB theme wich will be there
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Updated
Dec 8, 2022 - CSS
a DUMMY for the SHAB theme wich will be there
Explainable prediction of next year GDP Growth using the Kaggle World Development Indicators
Built an ensemble model on the Spotify dataset to determine the popularity of songs and study feature importance using SHAP.
This project explores the Framingham Heart disease dataset with the objective to predict its risk in 10 years. Various methods for handling missing values and outliers are explored as iterations. After analysing the dataset, important and necessary features are selected. Seven ML models are implemented, with evaluation on the basis of Test Recall.
A project in which a model has been developed that predicts which passengers of the Titanic have the greatest opportunity to survive after the disaster
We've developed a powerful binary dog and cat image classifier, driven by advanced deep learning techniques, and enhanced its transparency using Local Interpretable Model-agnostic Explanations (LIME). Witness the magic as the model accurately predicts dog and cat images while LIME reveals the intricate decision-making process behind each result.
This repository contains the code for machine learning models designed to predict the outcomes of horse races, with SHAP (SHapley Additive exPlanations) interpretation incorporated for enhanced model interpretability.
A take on highly imbalanced fraud classification using permutation importance to select top features and explaining the model using SHAP.
Final Year Project KCL
Exploration of SHAP visualisations with Keras Multi Layered Perceptron (MLP), Classifiers, and Regresors using seaborn datasets.
Through exploratory data analysis, predictive analytics and explainable AI, this project aims to provide valuable feedback regarding the reasons that customers churn, thus providing useful insight for the company to minimize customer churn.
Streamlit dashboard frontend (user interface) to deploy a machine learning model to the web
Car dealership web application that is enhanced with online machine learning and interpretable machine learning.
This a classic Credit Card Default Prediction project where based on customer profile we want to predict whether the borrower is likely to default in the next 2 years or not having a delinquency of more than 3 months.
Replicate Power BI Key Influencer visual in python
This project aims to analyze and model the information retrieved by a real-world IoT system in order to understand which are the relationships in the dataset chosen between the dependent variables and the selected target feature. To help the understanding of the inner working of the models adopted, several machine learning interpretability techn…
Feature assessment and importance of Machine Learning Models using SHAP and CXPlain libraries
Deployed a predictive model using decision tree regressor
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