Integer sequence
- 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
- 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