Whether it's to boost your fitness, health or bank balance, or as an environmental choice, taking up bicycle riding could be one of the best decisions you ever make. Remember the days of the bicycle built for two, when tourists rented bikes to explore island areas where cars either didn’t exist or were blessedly limited? Those days are still here, but the majority of bicycle rental businesses are clustered around heavily trafficked tourist spots. The objective of this case study is the prediction of bike rental count on daily based on the environmental and seasonal settings. The dataset contains 731 observations, 15 predictors and 1 target variable. The predictors are describing various environment factors and settings like season, humidity etc. We need to build a prediction model to predict estimated count or demand of bikes on a particular day based on the environmental factors.

Bike Rent Demand Prediction Model

Data Visualization using matplotlib and seaborn and prediction using different machine learning algorithms

Supervised Machine Learning- Regression

Bike renting demand prediction using Supervised ML

A prediction of the Bike Renting Demand in Seoul, Korea.

The Seoul Bike Sharing Demand Prediction data consists of 14 columns and 8760 rows. The dataset contains weather information (Temperature, Humidity, Windspeed, Visibility, Dew-point, Solar radiation, Snowfall, Rainfall), the count of bikes rented per hour and date information. I am supposed to predict the count of bikes rented per hour.

**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.

Bike sharing demand prediction is a common machine learning problem that involves predicting the number of bikes that will be rented in a bike sharing system at a given time. The task involves processing data from multiple sources, such as weather forecasts, historical rental data, and demographic information.

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.