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.

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.

In this repository, I will share the materials related to machine learning algorithms, as I enrich my knowledge in this field.

Model to Predict if a customer will purchase a Travel Package

Machine Learning

Predictive Analysis for Musculoskeletal Injury Risk using Machine Learning and Flask

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.

A machine learning application, deployed using Flask, is designed to identify the presence of heart disease in patients by analyzing various medical features.

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

Comparative Analysis of Decision Tree Algorithms in Number Classification: Bagging vs. Random Forest vs. Gradient Boosting Decision Tree Classifiers

The project contains an implementation of Bagging Classifier from scratch without the use of any inbuilt libraries.

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.

Some process on Shatel dataset.

Customer Churn Prediction using Machine Learning and Deep learning. With Integration of MLFlow

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.

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.