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making it more interpretable. This contrasts with
Linear Regression, which might consider all
variables, potentially overcomplicating the model.
2. Feature Selection: LASSO inherently performs
feature selection. By introducing a penalty to the
absolute values of model coefficients, it reduces
some of them to zero, effectively removing less
important features, which might be beneficial in
stock price predictions where numerous variables
can influence the outcome.
3. Robustness Against Overfitting: LASSO tends to
be more resilient against overfitting compared to
traditional Linear Regression, especially in
scenarios where there's a risk of fitting the model
too closely to the training data. This is crucial in
stock price predictions, given the volatile nature of
stock markets.
4. Statistical Significance: The p-value of 0.0461
from the independent sample t-test is crucial as it
indicates a statistically significant difference
between the results of the two algorithms. This
adds weight to the conclusion that one method
might be superior to the other.
5. Future Applications: The results suggest that
LASSO might be a more suitable technique for
other stock price predictions or in scenarios with
vast amounts of data and numerous features. The
adaptability of LASSO to other complex
predictive scenarios warrants further exploration.
6. External Influences: It's crucial to understand that
while the LASSO algorithm shows higher
accuracy in this study, stock price prediction is
influenced by numerous external factors. Market
conditions, economic shifts, and company
performance, among other variables, will always
introduce an element of unpredictability.
In conclusion, while the mean accuracy of the
LASSO algorithm stands at 85.31% in predicting
Amazon's stock price, the Linear Regression
approach lags slightly with 77.44%. It becomes
evident that, in this context, LASSO seems to offer a
more accurate prediction model. The independent
sample t-test, backed by a p-value of 0.0461, further
solidifies the significance of this difference. Given
the insights, it's compelling to lean towards models
like LASSO when faced with multifaceted prediction
tasks such as stock price forecasting.
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