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Presidential-Election-Predictor

Conceptual Complexity is to measure an individual’s ability to project their ability to perceive a different perspective of a certain subject. Historically, lt has been seen to be extraneous and time-consuming to manually score large sets of text. Therefore, there is a demand to automate scoring to significantly reduce time and expense. This notebook suggests that using modern machine learning algorithms is a better predictor than traditional methods specifically the efficacy of a presidential speech.

Findings - Most Frequent 1000 Words Predictions

Algorithms Configuration Accuracy
Support Vector Machine kernel="rbf",C=13, gamma='scale',97 Component PCA 92.32%
Random Forest Gini Index 88.6%
XG boosting gamma=0, max_tree_depth=3, n_estimatror=100 94.1%

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A machine learning model to predict candidates based on presidential speeches

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