A deep learning Project for the Udacity course "Deep Learning Nanodegree".

A bike-sharing system is a service in which bikes are made available for shared use to individuals on a short term basis for a price or free. Many bike share systems allow people to borrow a bike from a "dock" which is usually computer-controlled wherein the user enters the payment information, and the system unlocks it.

Udacity Deep Learning nanodegree 'Bike sharing patterns' project

EDA, feature engineering, polynomial featurization, comparison of several machine learning algorithms and cross-validation.

This is a data analysis project from Dicoding to pass the Learning Data Analysis with Python class. This project aims to analyse and create a simple dashboard based on data from Capital Bikeshare.

Developing a business strategy to meet the demand levels and meet the customer's expectations.

Predictive (housing price) and prescriptive (bike sharing) analysis using Python (EDA, preprocessing) and Power BI dashboards for business insights.

Project focused on predicting bike rentals using machine learning models (linear regression, random forest, neural networks, etc.) in IBM SPSS Modeler. Dataset: Bike Sharing (UCI ML Repository).

A decision tree supervised learning algorithm trained to predict bike malfunctions, touristy routes, and other anomalies.

This data analytics project explores the bike-sharing service in Helsinki, analyzing factors such as weather, time of day, and station usage to gain insights into how people use the service and its impact on transportation in the city.

This Repository contains basic programs on Deep Learning and fewer parts of Computer Vision too.

Multiple Linear Regression | Bike Sharing Demand Prediction

(Archived) The bike-sharing system named BikeSims is built by the members of the group Lab 1_2d as an outcome of the team project for the course Programming and Systems Development in UofG.

ShareABike Flutter App "Flotte"

Analysis of the Bike-Sharing System for the following question: "How do annual members and casual riders use Cyclistic bikes differently?"

my first capstone project

This project utilizes RNN models with LSTM layers to predict bicycle availability at Bicing Barcelona stations in future time frames. Each time period is represented by a unique model, enhancing pattern recognition. The dataset, sourced from Bicing Barcelona

Read the README file for more details about the project.