This project aims to build a model to predict the truth of an article, hoax or non-hoax. Apart from that, this project also wants to identify the percentage of hoax and non-hoax articles.

This project was made as a final exam report for a Data Mining course at Universitas Indonesia.

This project consists of Two models that classifies the potability of water .

ML based Smart Crop Recommendation System with Disease Identification, utilizing CNNs. It aids farmers in selecting crops, managing diseases, and boosts productivity by integrating weather and geolocation APIs.

A Time-Series Machine Learning project aimed at accurate predicting curreny pair exchange rates using ARIMA, SARIMA and LSTM.

Spotify Music Classifier with Machine Learning Using Spotify API

Machine Learning Algorithms

This project on placement prediction integrates machine learning with database management using MySQL for user authentication. The project involves data preprocessing, feature engineering, and the implementation of supervised learning techniques to train the model.

Using various machine learning models (Logistic Regression, Gaussian Naïve Bayes, KNN, Gradient Boosting Classifier, Decision Tree Classifier, Random Forest Classifier.) to predict whether a company will go bankrupt in the following years, based on financial attributes of the company; Addressed the issue of imbalanced classes, different importance

Navie Base Classifier for classifiing imdb comments

The goal of this project is to create a classifier and see how accurately it can predict song genres. Taking a dataset from Spotify [Pandya, 2022], which is al- ready using machine learning algorithms for these purposes, can help assess if the resulting model can be considered apt for a large-scale business.

Develop machine learning models that detect diseases from blood test data. Utilizing machine learning, it analyzes patterns and predicts potential health conditions, offering users early insights for proactive care.

In this study, after detailed Exploratory Data Analysis, 5 different machine learning models were tested on Titanic Data to answer the question "What sorts of people were more likely to survive?" and the best model for survival prediction was determined.

R package (R6 class) based on a Gaussian Naive Bayes for supervised classification

This repository contains a machine learning project aimed at predicting diabetes using various algorithms such as Decision Tree Regression, Support Vector Regression (SVR), and Gaussian Naive Bayes (GaussianNB). Additionally, it provides a web deployment mechanism for the trained models, enabling easy access and utilization.

A Machine Learning Processing with SMS Data to predict whether the SMS is Spam/Ham with various ML Algorithms like MultinomialNB, LogisticRegression, SVC, DecisionTreeClassifier, RandomForestClassifier, KNeighborsClassifier, AdaBoostClassifier, BaggingClassifier, ExtraTreesClassifier, GradientBoostingClassifier, XGBClassifier to compare accuracy an

This project focuses on using Gaussian Naive Bayes, a probabilistic algorithm, to filter spam emails effectively. By leveraging its simplicity and efficiency, the goal is to enhance email security by accurately classifying emails as either spam or legitimate.

Data Science - Naive Bayes Work

Simple machine learning model using scikit-learn