ensemble-learning

The dataset has features of recording of vocal sounds of patients. These are being analysed in reference to presence or absence of a diagnosis of Parkinsons disease. Attribute Information:

• name - ASCII subject name and recording number
• MDVP:Fo(Hz) - Average vocal fundamental frequency
• MDVP:Fhi(Hz) - Maximum vocal fundamental frequency
• MDVP:Flo(Hz) - Minimum vocal fundamental frequency
• MDVP:Jitter(%),
• MDVP:Jitter(Abs),
• MDVP:RAP,
• MDVP:PPQ,
• Jitter:DDP - Several measures of variation in fundamental frequency
• MDVP:Shimmer,
• MDVP:Shimmer(dB),
• Shimmer:APQ3,
• Shimmer:APQ5,
• MDVP:APQ,
• Shimmer:DDA - Several measures of variation in amplitude
• NHR,HNR - Two measures of ratio of noise to tonal components in the voice status - Health status of the subject (one) - Parkinson's, (zero) - healthy RPDE,D2 - Two nonlinear dynamical complexity measures
• DFA - Signal fractal scaling exponent
• spread1,spread2,PPE - Three nonlinear measures of fundamental frequency variation
• car name: string (unique for each instance)

1. Load the dataset
2. It is always a good practice to eye-ball raw data to get a feel of the data in terms of number of records, structure of the file, number of attributes, Mention a few comments in this regard.
3. Using univariate & bivariate analysis to check the individual attributes for their basic statistics such as central values, spread, tails, relationships between variables etc. mention your observations
4. Splitthedatasetintotrainingandtestsetintheratioof70:30 (Training:Test)
5. Prepare the data for training - Scale the data if necessary, get rid of missing values (if any) etc
6. Trainatleast3standardclassificationalgorithms-LogisticRegression, Naive Bayes’, SVM, k-NN etc, and note down their accuracies on the test data
7. Train a meta-classifier and note the accuracy on test data
8. TrainatleastonestandardEnsemblemodel-Randomforest,Bagging, Boosting etc, and note the accuracy
9. Compare all the models (minimum 5) and pick the best one among them

Results

• First the algorithms of Logistic Regression, KNN Classifier, Decision Tree classifier, Support Vector Classifier and Naive Bayes are implemented individually.
• Thereafter they are stacked via StackModel with a final classifier of Logistic Regression and Support Vector Classifier.
• Random Forests is then implemented as an ensemble technique.
• Results are then compared.

Model	Accuracy	Recall	Precision	F1 Score	AUC
Logistic Regression	85	97	83	90	77.7
KNN	86	97	85	91	80.3
Naive Bayes	75	68	93	78	78.49
SVM	80	100	77	87	68.4
Stacking:SVM	90	97	89	93	85.59
Stacking:LG	90	100	87	93	84.21
Random Forest	93	100	92	96	83.3
Random Forest	95	100	93	91	92.11