Explore the development of a logistic model aimed at predicting stock market crashes using comprehensive data analysis. Focus on key factors affecting stock prices, such as company performance, interest rates, and inflation. Assess the relevance and correlation of each factor, like the Price-to-Earnings Ratio and Earnings Per Share. The approach involves refining data selection to strengthen predictive accuracy, utilizing logistic regression for classifications. Evaluate model performance with testing data, achieving an accuracy of 70.45% for Apple's dataset, demonstrating a promising predictive tool for traders. Additionally, explore the practicality of deploying these models in real-time market testing for enhanced decision-making strategies.

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