IN THIS WE HAVE TO PREDICT THE NEXT NUMBER OF GIVEN SEQUENCES USING MACHINE LEARNING MODELS

1. ECKOVATION
MACHINE LEARNING PROJECT
Integer Sequences
Submitted by:
•INA SINGHAL
•VISHESH NAHATA
•VAIBHAV TAYAL
•KARAN TANWAR

2. OBJECTIVE
To predict the next number
of a given sequence

3. DESCRIPTION
 This dataset contains the majority of the integer
sequences from the OEIS.
 It is split into a training set, where you are given
the full sequence, and a test set, where we have
removed the last number from the sequence. The
task is to predict this removed integer.
 This problem challenges to create a machine
learning algorithm capable of guessing the next
number in an integer sequence

4. DATA VISUALIZATION

6. IMPORTING AND READING FILES:

7. APPROACH:

8. SUPPORT VECTOR REGRESSOR(SVR)
 support vector
machines are supervised
learning models with associated
learning algorithms that analyze
data used
for classification and regression
analysis.
 In addition to performing linear
classification, SVMs can
efficiently perform a non-linear
classification using what is
called the kernel trick, implicitly
mapping their inputs into high-
dimensional feature spaces.

9. MODEL USED:

10. PREDICTION

11. RANDOM FOREST
 Random forest algorithm is a supervised classification
algorithm. As the name suggest, this algorithm creates the
forest with a number of trees.
 In general, the more trees in the forest the more robust
the forest looks like. In the same way in the random forest
classifier, the higher the number of trees in the forest
gives the high accuracy results.

12. MODEL USED:

13. CONCLUSION
Efficiencies:
1. SVC ~ -0.17%
2. RANDOM FOREST ~ 2.49%

14. BIBLIOGRAPHY
 www.analyticsvidhya.com
 www.stackoverflow.com
 www.kaggle.com
 www.eckovation.com
 www.wikipedia.com
 www.oeis.com