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Website Prediksi Penyakit jantung dengan 5 fitur menggunakan metode KNN,bagging classifier,random forest
This project predicts wind turbine failure using numerous sensor data by applying classification based ML models that improves prediction by tuning model hyperparameters and addressing class imbalance through over and under sampling data. Final model is productionized using a data pipeline
Customer Churn Prediction using Machine Learning and Deep learning. With Integration of MLFlow
R | Classification Project
The project is to analyze the flight booking dataset obtained from a platform which is used to book flight tickets.
Drug consumption prediction models are like crystal balls for public health. By analyzing vast amounts of data, these models can identify individuals or communities at higher risk of drug use. They consider factors like demographics, social media activity, prescription history, and even economic indicators.
Bagging is the term from "Bootstrap Aggregation Algorithm", That is for Low Bias & Low Variance
Analyze the data of Visa applicants, build a predictive model to facilitate the process of visa approvals, and based on important factors that significantly influence the Visa status recommend a suitable profile for the applicants for whom the visa should be certified or denied.
Comparative Analysis of Decision Tree Algorithms in Number Classification: Bagging vs. Random Forest vs. Gradient Boosting Decision Tree Classifiers
I applied the bagging and boosting methods using the decision tree as the base predictor on the sklearn’s breast cancer data set. I experiment with different parameters and report the results obtained.
A machine learning application, deployed using Flask, is designed to identify the presence of heart disease in patients by analyzing various medical features.
Our group project aimed to evaluate three predictive machine learning classification models to anticipate whether website visitors engage in transactions. This is done by analysing different attributes of website visitors including duration spent on different web pages, click rates, and bounce rates.
Predictive Analysis for Musculoskeletal Injury Risk using Machine Learning and Flask
The project contains an implementation of Bagging Classifier from scratch without the use of any inbuilt libraries.
In this repository, I will share the materials related to machine learning algorithms, as I enrich my knowledge in this field.
The Office of Foreign Labor Certification is facing a dramatic increase in work visa applications, but is hampered by a sluggish review system. It needs to improve the process by developing a way to quickly, accurately identify applications likely to be accepted or rejected so their processing may be prioritized.
Project on course "Data Mining 2"
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
Advanced Machine Learning
Data Mining Machine Learning | Assignments | JAN-APR 2023 | CMI
In simple, a Loan (borrowing money from a bank) is the sum of money that you borrow from the bank or lending financial institution in order to meet needs. These needs could result from planned or unplanned events, and by borrowing, you incur a debt that you have to pay within the agreed duration on your contract.
The sinking of the RMS Titanic is one of the most infamous shipwrecks in world history. In this model, need to analyse what sorts of people were likely to survive. We also need to apply the tools of machine learning to predict which passengers survived in this tragedy.
Titanic survivors binary classification
Model to Predict if a customer will purchase a Travel Package
Analyze the data of ABC consulting company, build a predictive model based on the parameters like age, salary, work experience and predict the preferred mode of transport.
Visa approval process by leveraging machine learning on OFLC's extensive dataset, aiming to recommend suitable candidate profiles for certification or denial based on crucial drivers.
Our project utilizes machine learning models to predict cardiovascular diseases (CVDs) by analyzing diverse datasets and exploring 14 different algorithms. The aim is to enable early detection, personalized interventions, and improved healthcare outcomes.
This mini-project involves experimenting with a variety of classification and regression models, exploring different techniques to understand their behaviors and applications in predictive analytics.
Machine Learning
Some process on Shatel dataset.