Randomized-stock-prediction-with-julia

using python, jupyter and the new language of the field in data science called julia to do machine learning with a stock to produce results

by doing data analysis on the data, i can conclude that the stock would have a downtrend. time_series_analysis

more of the analysis that the opening, closing, low, high have positive correlation with each other therefore migh probably not be needed for analysis correlational_heatmap

after data cleaning process, most of the outliers have been removed Boxplot_closing_price

dataset_after_outlier_detection_and_removal

by conducting volatility analysis, i saw that the stock was volatile during its early periods and was less volatile after its IPO Volatility analysis

after training and prediction measurement via julia, the accuracy of the model via training set was 96.5% and testing set was 98.72% Actuals_vs_Predicted

in conclusion, the price has been volatile and will have a downtrend in the forseeable future

Downloads last month

-

Downloads are not tracked for this model. How to track
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support