predict the winning horse with supervised machine learning models (lucky to have 100% accuracy on small test data)

Classification of Customers for bank marketing

Introducing the Spam Detector Web App! This application is designed to detect spam comments using a K-Nearest Neighbors (KNN) classifier with TF-IDF vectorization. The web app is built using Django and HTMX, and it allows users to register, create comments, and detect spam within those comments.

This project predicts the survival of passengers on the Titanic by using Kaggle Titanic Disaster Dataset. The dataset contains information related to passengers, such as age, gender, and class. Different machine learning algorithms have been applied for this predictive model to accomplish an accurate prediction that will define the survival chances

This repository holds the final project for the Natural Language Processing project for the Intel AI Learning and Certification Program.

Some exercises codes on basics of machine learning built from scratch with basic libraries like numpy ,math.

Machine learning project focused on predicting the success of Kickstarter campaigns. Implemented KNN, Logistic Regression, and Random Forest algorithms to analyze campaign features and forecast outcomes. Developed during a data science bootcamp, showcasing practical experience in model selection, feature engineering, and predictive modeling.

A small demonstration of my knowledge of machine learning algorithms.

This Repo. contains all my Python based Machine Learning projects using numerous algorithms for the purpose of Classification, Clustering, Recommendation, Prediction and Anomaly Detection.

knn implementation in numpy

Binary classification task of determining whether a red wine is 'good' or 'bad'

Machine Learning course, Spring 2024, University of Tehran, Dr. M Tavassolipour and Dr. MRA Dehaqani

Water potability classifier using simple logistic regression to advanced gradient boosting algorithms

In our Titanic dataset project, we predicted passenger survival using machine learning. We cleaned the data, handled missing values, and engineered features like FamilySize. After exploratory analysis, we trained models, with Random Forests achieving 80% accuracy, highlighting key survival factors.

my first projectWelcome to my second DS project also my first notebook on kaggle. In this notebook, I explore the Breast Cancer dataset and develop an RF model to try classifying suspected cells as Benign or Malignant. after using K-fold cross-validation with logistic regression, RF, SVM, and KNN to check the best model for my dataset.