Bike Sharing Demand Prediction PRESENTED BY:- AKSHAY PATIL 14MCB1031 RESEARCH FACILITATOR: PROF. BVANSS PRABHAKAR RAO M.TECH 1 ST YEAR RBL FIRST REVIEW PRESENTATION VIT-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 Clean Data Create New Factors PreProcessing Building a Prediction Model Validate the 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  Training Data: 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 20 th January: Finalizing RBL topic  20 th January – 5 th February: Understanding dataset and gaining domain knowledge  6 th February – 20 th February: Literature Survey and methods.  21 st February – 20 th March: Implementation  21 st March- 10 th April: Testing and improving model  11 th April – 30 th 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:  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: 20/www/mlbook/ch3.pdf 4]A Tour of Machine Learning Algorithms: algorithms/ 20/www/mlbook/ch3.pdf

Any Suggestions?

Thank You