in this project we train the different model using different classifiers( Logistic Regression Classifier , Decision Tree , Random Forest , A fully connected dense layer , and CNN) and we used the raw data , its derivative , its integration and all possible combination to determine which is better for each classiffier , and after determning which is best for each classiffier ,the models were validated and then tested
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in this project we trained our model using different classifiers( Logistic Regression Classifier , Decision Tree , Random Forest , A fully connected dense layer , and CNN) to predict the modulation type of a signal at different SNR values.
Ahmed-Elshoubashy/Modulation-Classification
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in this project we trained our model using different classifiers( Logistic Regression Classifier , Decision Tree , Random Forest , A fully connected dense layer , and CNN) to predict the modulation type of a signal at different SNR values.
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