Created AdaBoost Model to predict the whether the income is <=50K or >50K of an individual. In order to find potential donors.

A basic implementation of AdaBoost algorithm from Scratch.

Goal Using the data collected from existing customers, build a model that will help the marketing team identify potential customers who are relatively more likely to subscribe term deposit and thus increase their hit ratio

Algorithms from scratch to know how the algorithms work.

ADABOOST - MULTICLASS CLASSIFICATION - MACHINE LEARNING - PYTHON : Predict category of problem solved as part of Piramal Hackathon

Classification problem to predict flight delay using: Logistic Regression, Decision Tree, Random Forest, AdaBoost, Neural Network

In this project I intend to predict customer churn on bank data.

Employee Churn Prediction for a Kaggle Data Contest

Bagging-boosting-stacking

Program to predict whether a person will suffer a stroke in the near future or not with help of python

Application to predict future occurrence of heart attack in patients on the basis of test reports

Employ different techniques to train and evaluate models with unbalanced classes. Evaluate the performance of these models and make recommendations on their suitability to predict credit risk.

Practicum by Yandex Project 7: In this supervised learning project, we are to use customer data and develop a model that would predict whether a customer will leave a bank soon. One of the objective is to build a machine learning model with the maximum possible F1 score of atleast 0.59 or higher.

Recent times the E-commerce websites took a rapid increase in their user stats, this project is a small help for all the people out there to have a confirmation on their delivery date to avoid any issues or problems while receiving the package.

This repository contains solved assignments of regression and classification algorithms.

This repository contains the dataset and code for Absenteeism analysis. Many algorithms like RandomForestClassifier, SVC, AdaBoost etc.. are employed. The final model is selected based on accuracy obtained after hyper-parameter tuning the models