1. USE OF DATA ANALYTICS TO PREDICT THE DEMAND OF BIKES
BIT 5534: Applied Business Intelligence and Analytics
Project report by:
Surabhi Anurag, Trushit Vaishnav, Shikha Varkie
Group 2

2. Business Objective
To determine the demand for the bike rentals based upon the various parameters such as temperature, working day, humidity, weather, windspeed etc.
Temperature, working day or holiday, month, weather,humidity,windspeed etc

3. Data Source and Description
Kaggle.com : Bike Sharing Demand Dataset
Total attributes : 16
Total observations in dataset : 17380
Training set: 10886
Validation set: 6494

4. Data Analysis Process Define Business Objective
Data Collection / Selection
Exploratory Data Analysis
Data Preparation
Data Transformation
Data Modelling
Model Selection
Conclusion
Exploratory: To access data Quality for instance if we have any missing values, outliers,or if we need any data transforamtions.

5. Exploratory Data Analysis

6. Correlation

7. Multiple Linear Regression

8. Principal Component Analysis

9. Decision Trees R Square RMSE N Number of Splits AICc Training 0.841
R Square
RMSE N
Number of Splits
AICc
Training
0.841
593 20
Validation
0.842
135

10. Conclusion
All models including Multiple Linear Regression, Principal Component Analysis, Clustering, Decision Trees and Neural Networks performed well.
Our business objective is typical demand forecasting problem. Thus, we choose regression model as the final model because:
Model performs decently.
Business objective is supervised learning with dependent and independent variables clearly identified.