ELM classifier for detecting tuberculosis in lung X-rays

Determining the total dropout rate of pupils at different levels of education based on various factors globally.

Amazon product review sentiment analysis using Logistic Regression (LR), Support Vector Machine (SVM), and Naive Bayes (NB) multiclass as classifier models, Synthetic Minority Oversampling Technique (SMOTE) as feature oversampler, and TF-IDF vectorization as feature, Synthetic Minority Oversampling Technique (SMOTE) as oversampler, and k-fold CV.

Practice model assessment and optimization on the HR dataset using validation and dimensionality reduction techniques

Based on customer visiting information to the site the customer sales revenue is predicted using machine learning models stacking

Label classification for three datasets: Face, Pose and Illumination. Bayes Classifier, KNN Classier, Kerner SVM and Boosted SVM algorithms are written from scratch in Python. The results were evaluated and compared to understand the effectr of dimentionality reduction techniques including PCA, LDA and MDA validation using K-fold cross validation.

The given dataset contains electricity consumer household information. This information has been used to predict the amount to be paid by the consumer with the help of regression model selection and validated with feature importance.

Classify whether a transaction is fraudulent or not

Predicting real state house price using Machine Learning Algorithms

Breast Cancer Data Analysis: Analyzes and classifies breast cancer data using a Naive Bayes classifier with preprocessing, label encoding, and k-fold cross-validation. Cars Dataset Analysis: Explores a cars dataset with data loading, statistics, and visualizations, including price distribution and correlation heatmap. Hayes-Roth Classification: C

Perform Linear Regression on the given dataset. Also perform K-Fold cross-validation

In this case study target variables are explored while using two ways of fitting a linear regression model to predict Boston housing prices. The the least squares method is used to estimate the coefficients.

Learning Machine Learning Through Data

K-fold cross-validation implemented from scratch to aid in analysis of the MNIST dataset

An implementation of the **k-Nearest Neighbours Classification Algorithm** using some popular demo datasets.

Linear Regression Models on Montesinho Forest Fire

Evaluating performances of CNN and ANN for Image Classification

Ames dataset: House Price prediction for kaggle competition (advanced regression, supervised ML)

A Two Stage Machine Learning Approach for 5G Mobile Network Augmentation through Dynamic Selection and Activation of UE_VBSs.