1. Perform an Explorartory Data Analysis with visualization and
2. Use a liner regression model (OLS) to predict the number of bikes rented.

• Pandas
• Numpy
• Matplotlib
• Seaborn
• Time
• scikit-learn
• statsmodels
• Trello
• MIRO

London bike sharing dataset https://www.kaggle.com/hmavrodiev/london-bike-sharing-dataset

Train R^2: 0.628 - Train Adjusted R^2: 0.628

Test R^2: 0.655 - Test R^2: 0.582

• Difference in R^2 between train and test 2.07%
• Difference in R^2 between train and test is 4.6% which is less than 5%.

1. Design:

• I have created a MIRO board with the story mapping. https://miro.com/app/board/o9J_lRe3F5E=/
• I have created a Trello board with the epics of requirements needed to deliver. https://trello.com/b/S7aR17IA

2. Clean, manipulate and create the visualizations.

• Exploratory Data Analysis
• Create visualizations
• Create Dummies
• Recursive Feature Selection (RFE)

3. Create the linear regression model.

• Validate the assumptions (Linearity, Autocorrelation, Sub-Normality, Normality, Multicollinearity)

4. Analyze the model perforamnce
5. Creating a Story Telling presentation

To see the presentation, click in the below picture.