"This repository contains implementations of Boosting method, aimed at improving predictive performance by combining multiple models. by using titanic database."
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Updated
Jun 4, 2024 - Jupyter Notebook
"This repository contains implementations of Boosting method, aimed at improving predictive performance by combining multiple models. by using titanic database."
👩💻This repository contains implementations of various machine learning algorithms, along with example datasets and Jupyter Notebook files for demonstration.
This repository contains my coursework (assignments, semester exams & project) for the Statistical Machine Learning course at IIIT Delhi in Winter 2024.
Developed and evaluated machine learning and deep learning models for detecting financial fraud.
A ML application(deployed on flask) to detect heart disease in patients based on medical features.
Analyze the data and come up with a predictive model to determine if a customer will leave the credit card services or not and the reason behind it
UArizona DataLab Workshops
learning python day 14
Work on combining Logit model with an information granulation method for better interpretability
This is a friend recommendation systems which are used on social media platforms (e.g. Facebook, Instagram, Twitter) to suggest friends/new connections based on common interests, workplace, common friends etc. using Graph Mining techniques. Here, we are given a social graph, i.e. a graph structure where nodes are individuals on social media plat…
Predictive Modeling and Clustering Insights for Success on Shark Tank
Code of the Stacking-Enhanced Bagging Ensemble Learning for Breast Cancer Classification with CNN on ICEEM 2023
"Heart disease and diabetes prediction accuracy through Bagging and AdaBoost ensemble methods for enhanced predictive performance."
A collection of fundamental Machine Learning Algorithms Implemented from scratch along-with their applications for various ML tasks like clustering, thresholding, data analysis, prediction, regression and image classification.
Predicting Colorado forest cover types using diverse ML models for classification. Baseline creation, feature selection, comparison, and tuning optimize accuracy in this University of Ottawa Master's Machine Learning course final project (2023).
Predictive Modeling of Credit Risk Faced by a P2P lending platform
Classification problem using multiple ML Algorithms
The project contains an implementation of Bagging Classifier from scratch without the use of any inbuilt libraries.
Machine Learning with Python
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