ieee project

City wide bike usage prediction using machine learning is the aim of the project. Bike sharing demand is increasing nowadays. In this work, dataset from Capital Bikeshare system, Washington DC, USA is used.

Bike demand may affect or change due to various parameters such as weather, holiday or working day, temperature, humidity and more. These features are considered in this dataset.

Project methodology

  1. Data Pre-processing
  2. Data Visualization
  3. Training using Lasso and Ridge regression
  4. Demand forecasting

As the dataset considered is bike demand, which is a variable parameter according to each condition and attributes. Regression is applied on this work.

Experimental analysis showed accuracy of bike demand prediction is around 62%.

Data science is useful when the dataset is having multiple influencing parameters. Here we have visualized data and done Explanatory data analysis as shown below

Project implementation demo is given below, source code for bike usage demand prediction and forecast is written in Python

Similarly, forecasting is an interesting subject, it can be applied in given work check mode links

Stock Price prediction and Forecast

By admin

Leave a Reply

Your email address will not be published. Required fields are marked *