Bike-shairing-demand-prediction

Supervised Machine Learning.

• Analyze the data related to bike sharing to identify patterns and trends in demand.
• This can help stakeholders to make informed decisions about where to deploy bikes, how many bikes to deploy, and in how much price bike sharing services.
• Using statistical techniques, machine learning algorithms, and data visualization tools to uncover insights and make predictions about future emand. Which will affect bike-sharing operations by up to 80%.

Libraries used :

Numpy
Pandas
Matpllotlib
Seaborn
sklearn
xgboost
warnings

Machine Learning Models Implimented:

Linear Regression
Lasso regression
Ridge regression
Elastic Net Regression
Polynomial Regression
Decision-tree model
Random forest regression model
Gradient Boosting