MattOwings/Facial-Recognition-Sklearn

Using the Anaconda Spyder IDE, I created a program that prints out accuracies using the Sklearn Dataset fetch_lfw_people. I applied six different classifiers, parameters such as PCA, and manipulated the number of samples that appeared in the final results and edited the train test split ratio to improve accuracy artifically.

Facial-Recognition-Sklearn

Using Python and the Anaconda Spyder IDE, I created a program that prints out accuracies using the Sklearn Dataset fetch_lfw_people. I applied six different classifiers, parameters such as PCA, and manipulated the number of samples that appeared in the final results and edited the train test split ratio to improve accuracy artifically.

MLProject.py --> the code that imports the dataset, applies the classifiers, and prints out the accuricies to the console. FacialRecognitionResearchPaper.docx --> research paper and final project report for my Machine Learning course.

Classifiers used: K-Nearest_Neighbors KNN Random Forests RF Naive Bayes NB Decision Trees DT Multi-Layer Perceptron MLP (Neural Network) Support Vector / Classifier SVC

Parameters: PCA

Code and paper written alongside my group partner, named in the research paper.