You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
This project involves predicting customer churn in a telecommunications company using machine learning techniques, exploring various features' impact, optimizing models, and identifying key factors influencing churn.
Using publicly available data for the national factors that impact supply and demand of homes in US, build a data science model to study the effect of these variables on home prices.
Create the Decision Tree classifier and visualize it graphically. The purpose is if we feed any new data to this classifier, it would be able to predict the right class accordingly.
Here I analysed data and made pipeline out of it making model making/training/testing/selecting and hyperparameters selecting more user friendly and visualised, like an application. I have worked for this project ~2 weeks.
Predicting the success of bank marketing campaigns using machine learning models (Random Forest, XGBoost) on customer and economic data. The project includes data preprocessing, model training, and evaluation with accuracy and ROC-AUC scores.
A machine learning foundation project to predict defaulting credit card clients. Topics covered are EDA, feature engineering and selection, model evaluation.
This repository explores machine learning models applied to predict online shoppers' purchase intentions based on a comprehensive dataset, showcasing various classification algorithms and their performance in the e-commerce domain.
Graduate Rotational Internship Program -TSF-The Spark Foundation(Data Science and Business Analysis Internship) #GRIPJULY21-Task#2:Prediction Using Unsupervised Machine Learning-In this task, we have to predict the optimum number of clusters from the iris dataset & represent it visually.