Machine Learning

1. BIKE SHARING PREDICTION

2. BIKE SHARING:
 Bike sharing systems are new generation of traditional
bike rentals where whole process from membership,
rental and return back has become automatic.
 Through these systems, user is able to easily rent a bike
from a particular position and return back at another
position.
 Currently, there are about over 500 bike-sharing
programs around the world which is composed of over
500 thousands bicycles.
 Today, there exists great interest in these systems due to
their important role in traffic, environmental, social,
public, eco and health issues.

3. Fig: Bike Sharing
Stand

4. Problem Statement:
 Please make an explorative data analysis and build a
model for the hourly utilization “cnt” of the
 given data set:
 Report the mean absolute deviations.

5. Data Set:
Hour CSV
 This dataset contains the hourly and daily count of rental bikes between years 2011 and 2012 in
Capital bike share system with the corresponding weather and seasonal information.
Attribute Information:
1. Instant
2. hr
3. Season
4. weathersit
5. Temp
6. Hum
7. windspeed
8. Casual
9. atemp
10. Count
11. Registered

6. Fig: Box Plot On Count Across Month.

7. Fig: Box Plot On Count Across Weather.

8. Fig: Box Plot On Count Across Hour Of Day.

9. Fig: Box Plot On Count Across
Temperature.

10. Fig: Weekday wise hourly distribution of counts.

11. Fig: ViolinPlot

12. Fig: Correlation Analysis.

13. Model Mean Squared
Error
Score
SGDRegressor 24145.81 0.25
Lasso 19842.91 0.38
ElasticNet 24172.61 0.25
Ridge 19836.38 0.38
SVR 21536.58 0.33
SVR 13836.69 0.57
BaggingRegressor 2135.96 0.93
BaggingRegressor 12718.40 0.60
NuSVR 14086.19 0.56
RandomForestRegres
sor
1809.67 0.94
AdaBoostRegressor 2030.11 0.94
Fig: Different Algorithm Metrics.

14. RANDOM FOREST METRICS
TABLE:
Model Dataset MSE MAE RMSLE Score
RandomFor
estRegress
or
training 372.28 11.32 0.18 0.99
RandomFor
estRegress
or
validation 1817.10 25.58 0.36 0.94

16. SOURCES:
 Github
 Kaggle

17. Problem’s Faced:
 Selecting an algorithm.
 Training model.

19. Thank you!