Bike Sharing Demand Prediction

PRESENTED BY:-AKSHAY PATIL14MCB1031

MAIL@AKSHAY.IM

RESEARCH FACILITATOR:PROF. BVANSS PRABHAKAR RAO

M.TECH 1ST YEAR RBL FIRST REVIEW PRESENTATIONVIT-CHENNAI.

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.

Research Scope

Introduction to Bike Sharing Systems.

Use of Data Analysis in such Systems.

Literature Survey

Regression:Package used: lm

Decision Trees:Package Used: rpart, ctree

RandomForests:Package Used: randomForest

SVM:Package Used: e1071

Proposed Methodology

Fetch &

Analyze Data

Clean Data

Partition Data

Remove Missing

Data

CleanData

CreateNew

Factors

PreProcessing

Building aPrediction

Model

Validatethe

Model

Predict Values for Test Data

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.

Dataset Description

Implementation Tools

R Weka

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

Factorized Data:

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

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

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.pdf4]A Tour of Machine Learning Algorithms: http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/

AnySuggestions?

Thank You