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.

Prediction of the number of bicycles rented in an hour of a day in the Seoul Bike Sharing System based on different weather conditions and holiday information in Seoul from 1/12/2017 to 30/11/2018

Bike Rent Demand Prediction Model

Predicting Bike Rental Count

Created Bike Renting Prediction analysis for the Predication of bike rental count on daily based on the environmental and seasonal settings.

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

Prediction and Projection for a problem statement by BoomBikes to predict the factors affecting their bike rental count post covid and deploying a model predicting the number of bikes that can be rented for a particular day.

Project - Bike Renting Prediction

bike rent prediction

prediction for bike renting agencie

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rented bike count prediction

Rented Bike Count Prediction

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The objective of this Case is to Predication of bike rental count on daily based on the environmental and seasonal settings.

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Production-ready ML pipeline for bike demand forecasting with data validation, Dockerized training & CI/CD automation.

A model which can predict the bike rental count based on different environmental and seasonal settings

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Problem Statement ::-->The objective of this Case is the Predication of bike rental count on daily based on the environmental and seasonal settings. We are provided with a dataset of bike rental based on environmental conditions and seasons for two year i.e. 2010 and 2011. We are supposed to predict what could be the count of bike rental for a given season, month and year in accordance with the environmental condition and prevailing seasons.

Bike renting demand prediction using Supervised ML

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

Bike rent prediction - Kaggle Competitions

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Supervised Machine Learning- Regression

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

Prediction of Bike Renting count using Regression in Spark