# Can One Use Computer Simulation Generated Data for Machine Learning Algorithms

Can One Use Computer Simulation Generated Data for Machine Learning Algorithms

Of course, we can utilize the computer simulation generated data for the machine learning algorithms. Let’s start this article with the list of most substantial machine learning algorithms.

Machine Learning Algorithms

• Naive bayes
• Random forest
• Logistic regression
• SVM

Python Libraries

As an additional note, we have addressed some python libraries based on the machine learning algorithms.

• PyTorch
• Keras
• Scikit-learn
• SciPy

Our technical people have illustrated the step by step process of loading computer simulation generated data into the machine learning algorithms using Python code.

• Importing libraries

import matplotlib.pyplot as plt

from sklearn.linear_model import LogisticRegression

from sklearn.model_selection import train_test_split

``` fileName =filedialog.askopenfilename(filetypes=[("TIFF",".tiff"),("JPG",".jpg"),("PNG",".png")]) img = mpimg.imread(fileName) imgplot = plt.imshow(img) plt.xticks([]), plt.yticks([]) • LeNet-4 network model based on load generated data mnist = tf.keras.datasets.mnist (x_train, y_train),(x_test, y_test) = mnist.load_data() x_train = x_train.reshape(x_train.shape, 28, 28, 1) x_test = x_test.reshape(x_test.shape, 28, 28, 1) model = Sequential() ```

If you have any issues while utilizing computer simulation generated data for ML algorithms then the research scholar can reach us at any time.

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