classfication-model

To build a classification methodology to predict whether a website is a phishing website on the basis of given set of predictors.

A ML project based on aqi data

This project classifies and detects malicious websites by analyzing various factors such as the URL of the website, IP address, the geographic location of where the website is hosted and other factors.

This system integrates front-end (HTML, CSS, JavaScript) with Flask backend, using a CNN (VGG-16) for breast cancer classification, and SQL for data management.

A simple program for classification of fruits on basis of color using KNN.

Explore my Kaggle competition solution repository for the year 2912. Join in to help rescue passengers trapped in an alternate dimension!

This project is designed to identify fraudulent transactions with high accuracy.

Classifying Wine Quality Data Based on Different Supervised Learning Methods - Logistic Regression, Decision Tree, Random Forest & Support Vector Machine.

Steam is a video game digital distribution service with a vast community of gamers globally. A lot of gamers write reviews at the game page and have an option of choosing whether they would recommend this game to others or not. However, determining this sentiment automatically from text can help Steam to automatically tag such reviews extracted …

Built a predictive ML model for employee attrition using Python & scikit-learn on a 10K-row HR dataset. Tackled class imbalance with balanced weights across Logistic Regression, Random Forest, and Gradient Boosting hitting 82% ROC-AUC while spotlighting top risks like low job satisfaction, long commutes, and stalled promotions.

Implemented machine learning algorithms to analyze historical weather data

Predicting Heart Disease using Machine Learning model

Learning ML through real-world loan approval prediction: preprocessing, feature engineering, model comparison, hyperparameter tuning, and understanding why tree models dominate tabular data.

Collaborated with Davies Biological Sciences Lab at the University of New Brunswick to automate the manual sorting of 45k shadowgraph images captured underwater in the Bay of Fundy for research purposes. Utilized computer vision and deep learning to develop an ensemble model that achieved a 94.42% accuracy rate, striking an optimal False Positive-F

📊 Predict employee attrition using data analysis to enhance workforce stability and improve retention strategies.

Built preprocessing pipelines and an interface for professionals to assess diabetes risk accurately. Accuracy of 77% for Logistic Regression model.

A supervised machine learning model using K-Nearest Neighbors (KNN) to classify Iris flowers, built with Python and Scikit-Learn.

Classify iris plants into three species in this classic dataset

A data-driven system to assist farmers in selecting the right crop for given conditions.