The document discusses a term project on bitcoin price detection using the Random Forest algorithm in the context of big data analysis. It outlines cryptocurrency data such as open, high, low, close prices, and volumes, as well as the methodology employed in building the Random Forest model, including training and prediction processes. The results illustrate the use of the model, highlighting the importance of understanding past performance in predicting future outcomes.

1. BITCOIN PRICE DETECTION
WITH RANDOM FOREST
CS 556 - BIG DATA ANALYSIS TERM PROJECT -
YAKUP GORUR - S017327

2. OUTLINE
• MOTIVATION
• CRYPTOCURRENCY DATA
• RANDOM FOREST
• RESULT

3. MOTIVATION

4. BITCOIN
CRYPTOCURRENCY DATA
• OPEN
• HIGH
• LOW
• CLOSE
• Volume (BTC)
• Volume (Currency)
• Weighted Price

5. CANDLE STICK CHART
CRYPTOCURRENCY DATA
• OPEN PRICE:
The open represents the first price traded
during the candlestick.
• HIGH PRICE:
The high is the highest price traded during
the candlestick
• LOW:
The low shows the lowest price traded
during the candlestick
• CLOSE:
The close is the last price traded during the
candlestick
Image via Wikipedia
The color of the candle body indicates whether the closing price was
higher than the opening price
green bar, called an ‘up-bar’
red bar, called a ‘down-bar’

6. CANDLE STICK CHART
CRYPTOCURRENCY DATA
• Volume (BTC):
Volume, in BTC traded in the stock
market during a given measurement
interval.
• Volume (Currency):
Volume, in USD, traded on stock market
during a given measurement interval.
• Weighted Price:
Measure of the average price

7. CRYPTOCURRENCY DATA
HOW BIG
WEEKLY DATA
DAILY DATA
HOURLY DATA
PER MINUTE DATA
x 7
x 24
x 60
2009 - 2018 for BITCOIN
~468 weeks
~3,285 days
~78,840 hours
~4,730,400 minutes
EVERY DAY OR EVERY WEEK
EVERY DAY
EVERY HOUR
EVERY MINUTE
PREDICTION
REAL TIME
Number of Market
~ 27000
Number of Coins
~ 5000
~12,636,000 weeks
~88 M days
~2 B hours
~120 B minutes
~60 B weeks
~440 B days
~10 T hours
~ 600 T minutes

8. RANDOM FOREST

9. • classification and regression tasks
• decision tree, the basic building
block of a random forest
• fast to build
• Fully parallelizable
RANDOM FOREST

10. • Each tree in a Random Forest is
trained independently, multiple trees
can be trained in parallel.
• Random Forests use a different
subsample of the data to train each
tree. Instead of replicating data
explicitly, we save memory by using
a TreePoint structure which stores
the number of replicas of each
instance in each subsample.
RANDOM FOREST IN SPARK

11. 1. Read train data from a CSV file.
2. For n Trees call n mappers
3. Create 90% subset of the training data with replacement
4. When mapper is running it takes these data
5. After receiving data, each mapper start to build tree and
produce prediction for test dataset.
6. Pass the test data and label as key and value to Reducer
7. Reducer counts the majority label according to key.
8. Write results to output file.
RANDOM FOREST ON MAPREDUCE
BASIC MAPREDUCE STRUCTURE

12. DATA REPRESENTATION
RESULT
Window Size=7

13. RESULT
DATA TRAIN/TEST

14. RESULT
DATA PREDICTION
RMSE: 180

15. RESULT
DATA PREDICTION
“past performance is not an indicator for future outcomes”.*Finance world:

16. THANK YOU
- Yakup Görür