AI & Machine Learning: Detection and Classification of Network Traffic Anomalies based on IoT23 Dataset

Assumptions of Logistic Regression, Clearly Explained

Recognition of Persomnality Types from Facebook status using Machine Learning

To predict the the Prostate cancer is Benign or Malignant

Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets and understand your voice commands, it is likely that a neural network is helping to understand your speech; when you cash a check, the machines that automatically read the digits also use neural networks.

Loan Prediction using Classification Techniques

Build and evaluate various machine learning classification models using Python.

Logistic regression and dynamic feature selection based android malware detection approach

Classification Modeling: Probability of Default

LDA(Linear Discriminant Analysis) for Seed Dataset

This project involves predicting wine quality using logistic regression in Jupyter Notebook. Wine quality prediction is an important task in the field of wine production and quality control, as it helps assess the overall quality of wines based on various chemical properties.

Predicting Hepatocellular Carcinoma through Supervised Machine Learning

First Assignment in 'NLP - Natural Languages Processing' course by Prof. Yoav Goldberg, Prof. Ido Dagan and Prof. Reut Tsarfaty at Bar-Ilan University

4 classifier models viz. k-NN classifier, Naive Bayes classifier, Decision Tree and Logistic Regression classifier predicts the outcome of Loan Appliction Status.

This is an exoplanet classifier for the final project of the AIN212 data science course. Publicly accessible data from NASA's exoplanet archive was used in the training and testing of this classification model.

Training of ML models with CIC dataset of malicious DoH traffic

Credit card Fraud Detection using AutoEncoders Neural network to encode complete dataset. Training the Genuine Transaction Data alone and create Anomaly Detecting Classification Model using the simple Logistic Regression Classifier and XGBoost to predict the Fraud transaction from the Encoded dataset.

CS289A | Supervised ML on MBTI types