Kaggle Problem Bike Sharing Demand, Akshay Patil

1. Bike Sharing Demand
Prediction
PRESENTED BY:-
AKSHAY PATIL
14MCB1031
MAIL@AKSHAY.IM
RESEARCH FACILITATOR:
PROF. BVANSS PRABHAKAR RAO
M.TECH 1ST YEAR RBL FIRST REVIEW PRESENTATION
VIT-CHENNAI.

2. Objective
 Primary Objective:
To build a superior statistical model to predict the number of
bicycles that can be rented with availability of data.
 Secondary Objectives:
1)To learn how real time data is represented in datasets.
2)To understand how to pre-process such data.
3)To study comparison of results achieved by various
Machine Learning techniques such as Regression, Decision
Trees, RandomForests and SVM’s.

3. Research Scope
 Introduction to Bike Sharing Systems.
 Use of Data Analysis in such Systems.

4. Literature Survey
 Regression:
Package used: lm
 Decision Trees:
Package Used: rpart, ctree
 RandomForests:
Package Used: randomForest
 SVM:
Package Used: e1071

5. Proposed Methodology
Fetch &
Analyze
Data
Clean
Data
Partition
Data
Remove
Missing Data
Clean
Data
Create
New Factors
PreProcessing
Building a
Prediction
Model
Validate
the
Model
Predict
Values for
Test Data

6. About Data:
 The training set is comprised of the first 19 days of each month, while the
test set is the 20th to the end of the month of year 2011 and 2012.
 Training Data: 10866 observations of 12 variables.
 Test Data: 6493 observations of 9 variables.

7. Dataset Description

8. Implementation Tools
 R
 Weka

9. Work Done:
 Understanding Data
 Factorize training set and test set
 Create time column by stripping out timestamp
 Create new timestamp column
 Create day of week column
 Create and factorize Sunday variable

10. Factorized Data:

11. Timeline
 Till 20th January: Finalizing RBL topic
 20th January – 5th February: Understanding dataset and gaining domain
knowledge
 6th February – 20th February: Literature Survey and methods.
 21st February – 20th March: Implementation
 21st March- 10th April: Testing and improving model
 11th April – 30th April: Writing Paper

12. Stats:
 “In the world of data analysis, Analysts require only 20% of the total project
time in building the actual models, about 60% of the period is spent in
understanding and pre-processing the data”
- Mat McHogan,
Data Scientist,
SVDS.com

13. References
 1] Bike Sharing Demand: http://www.kaggle.com/c/bike-sharing-demand
 2] Fanaee-T, Hadi, and Gama, Joao, Event labeling combining ensemble
detectors and background knowledge, Progress in Artificial Intelligence
(2013): pp. 1-15, Springer Berlin Heidelberg.
 3]Decision Tree Learning: http://www.cs.cmu.edu/afs/cs/project/theo-
20/www/mlbook/ch3.pdf
4]A Tour of Machine Learning Algorithms:
http://machinelearningmastery.com/a-tour-of-machine-learning-
algorithms/

14. Any
Suggestions?

15. Thank You