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https://github.com/simon2k/stock-price-prediction-evaluation
This project is indented to present a small evaluation of different types of regression models for predicting stock prices for AAPL.
https://github.com/simon2k/stock-price-prediction-evaluation
evaluation machine-learning numpy pandas predicting-stock-prices scikit-learn
Last synced: 6 days ago
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This project is indented to present a small evaluation of different types of regression models for predicting stock prices for AAPL.
- Host: GitHub
- URL: https://github.com/simon2k/stock-price-prediction-evaluation
- Owner: simon2k
- Created: 2019-09-08T21:03:43.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2022-06-21T22:43:04.000Z (over 2 years ago)
- Last Synced: 2023-04-05T23:40:34.309Z (over 1 year ago)
- Topics: evaluation, machine-learning, numpy, pandas, predicting-stock-prices, scikit-learn
- Language: Python
- Homepage:
- Size: 1.29 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Stock Price Prediction Evaluation
This project is indented to present a small evaluation of different types of regression models for predicting stock prices for [AAPL](https://finance.yahoo.com/quote/AAPL/history?p=AAPL).
## Setup
Steps:
- Download the repository
- Install packages: `pip install -r requirements.txt`
- Run: `python comparison.py`
## Evaluation
### Comparison
#### Models sorted by confidence
| Method | Confidence | Mean Absolute Error | Mean Squared Error | Root Mean Squared Error |
| :---: | ---: | ---: | ---: | ---: |
| Orthogonal Matching Pursuit | 99.9644% | 12.944 | 251.349 | 3.598 |
| Orthogonal Matching Pursuit CV | 99.9644% | 12.944 | 251.362 | 3.598 |
| Linear | 99.9641% | 12.898 | 249.516 | 3.591 |
| Bayesian Ridge | 99.9641% | 12.898 | 249.513 | 3.591 |
| Ridge CV | 99.9641% | 12.897 | 249.494 | 3.591 |
| Ridge | 99.9641% | 12.892 | 249.295 | 3.591 |
| Elastic Net CV | 99.9639% | 12.876 | 248.647 | 3.588 |
| Lasso | 99.9634% | 13.116 | 259.209 | 3.622 |
| KNN | 99.7029% | 13.293 | 254.76 | 3.646 |
| Elastic Net | 99.4877% | 12.879 | 218.512 | 3.589 |
| Kernel Ridge | 71.8265% | 40.886 | 1778.924 | 6.394 |
#### Models sorted by RMSE
| Method | Confidence | Mean Absolute Error | Mean Squared Error | Root Mean Squared Error |
| :---: | ---: | ---: | ---: | ---: |
| Elastic Net CV | 99.9639% | 12.876 | 248.647 | 3.588268 |
| Elastic Net | 99.4877% | 12.879 | 218.512 | 3.588714 |
| Ridge | 99.9641% | 12.892 | 249.295 | 3.590572 |
| Ridge CV | 99.9641% | 12.897 | 249.494 | 3.591279 |
| Bayesian Ridge | 99.9641% | 12.898 | 249.513 | 3.591345 |
| Linear | 99.9641% | 12.898 | 249.516 | 3.591357 |
| Orthogonal Matching Pursuit CV | 99.9644% | 12.944 | 251.362 | 3.597757 |
| Orthogonal Matching Pursuit | 99.9644% | 12.944 | 251.349 | 3.597776 |
| Lasso | 99.9634% | 13.116 | 259.209 | 3.621619 |
| KNN | 99.7029% | 13.293 | 254.76 | 3.646022 |
| Kernel Ridge | 71.8265% | 40.886 | 1778.924 | 6.394188 |
#### Moving Average for Adj Close
In the next paragraphs you can find predictions using selected methods.
Settings:
- Alpha: 0.1
- Number of iterations: 1000
- Alphas search: 0.0001, 0.0001, 0.001, 0.1
- Number of neighbors: 2
