"Predict and enhance employee promotions through data-driven insights and a predictive model in the 'HR Classification for Promotion' project."

A machine learning model to predict patient survival rates at a hospital in Greenland, using a Random Forest classifier and patient data including diagnosed conditions, age, and previous medical history. Model is evaluated using the F1 Score.

This GitHub repository contains code for predicting the country destination of new Airbnb users using machine learning techniques on the "Airbnb New User Bookings" dataset from a Kaggle competition.

Unlock the potential of agricultural production with innovative optimization techniques. Explore strategies, technologies, and practices to enhance crop yields, improve efficiency, and sustainably increase output. Revolutionize farming practices and cultivate a thriving agricultural ecosystem

ICC World Cup 2023 Predictions: Unleash the power of Machine Learning to foresee cricket match outcomes. Analyze team performance and player stats for strategic insights. Your go-to resource at the intersection of sports and cutting-edge analytics.

This project focuses on predicting NYC taxi trip durations 🚖🗽 using machine learning techniques. By analyzing factors such as pickup and drop-off locations, timestamps, and traffic conditions, it aims to provide accurate duration estimates to enhance rider and driver experiences in New York City.

The "Churn Prediction" project analyzes customer data to identify factors leading to churn 📉🤔. Using machine learning algorithms, it predicts which customers are likely to leave, enabling businesses to implement targeted retention strategies and improve customer satisfaction.

Predictive modeling techniques for data-driven decision-making

This project predicts the Air Quality Index (AQI) using machine learning techniques. By analyzing historical air quality data and relevant environmental factors, it provides accurate forecasts to help raise awareness and promote healthier living conditions in urban areas.

this project develops a robust machine learning model to estimate house prices in the state.

This repository contains the data, code, and documentation for a project to analyze and predict churn in PowerCo's SME customer segment. The project includes data exploration, cleaning, and transformation, as well as the development and evaluation of a machine learning model to predict churn based on price sensitivity and other relevant factors.