Predicting Bike Rental Count

A python data science project to predict the number of bike rents in next 6 months using previous data. Linear Regression model is used for prediction

Project - Bike Renting Prediction

Project-1(Bike Renting), Project-2(Santander customer transaction prediction)

A bike-sharing prediction project, aimed at estimating the total number of bikes rented in a bike-sharing system. Using machine learning techniques, we analyse various factors such as weather conditions and time of day to make accurate predictions.

The main objective of this project is the prediction of numbers of rented bikes on a daily basis. The project tries to predict the number of bikes which could be rented by assessing the environmental and seasonal settings.

December 2021 - Final 4th engineering year Project for the Python for Data Analysis module at ESILV | Blocks Classification & Seoul Bikes Rent Prediction

The objective of this project is the prediction of bike rental count daily based on the environmental and seasonal conditions. This aims at determining the future trends for a company so that they can plan accordingly on how they need to do the setup for renting bikes.

**Bike-Sharing Demand Prediction** is a machine learning project that forecasts the number of bikes that will be rented at a given time based on factors like weather, season, temperature, humidity, and day of the week. This helps bike rental companies optimize fleet management and improve user availability.

This repository contains the code and documentation for the Seoul Bikes Demand Prediction project, conducted by Beatriz Correia (Myself), Luís Pereira, Maria Pais, Sebastião Rosalino and André Novo. The project aims to predict the number of bikes rented per hour by the Seoul Bikes system, using historical rental data and weather conditions.

A machine learning project that predicts the number of bikes rented based on various factors like weather conditions, time of day, and season. The model is trained using Multiple Linear Regression, Random Forest, Decision Tree, XGBoost, and SVM to achieve high prediction accuracy.

This project compares three different regression models to predict the number of bikes rented for a bike sharing system in Washington DC. The models used to predict include a linear regressor, decision tree regressor and a random forest regressor. Comparing all three models showed that the random forest had the least amount of error on prediction.

In this project, we explore the capabilities of three machine learning algorithms to predict the total number of bikes people rented in a given hour in Washington D. C. USA. Here, the prediction accuracy of the models is determined by their corresponding MSE error. The model with the least MSE becomes the preferred model. the rental data for this project is available on the University of California, Irvine's website.