USE OF DATA ANALYTICS TO PREDICT THE DEMAND OF BIKES

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Presentation transcript:

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

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

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

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.

Exploratory Data Analysis

Correlation

Multiple Linear Regression

Principal Component Analysis

Decision Trees R Square RMSE N Number of Splits AICc Training 0.841 R Square RMSE N Number of Splits AICc Training 0.841 762.41202 593 20 9599.51 Validation 0.842 793.3215 135

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.