Real Life Applications of Python Programming

If you’ve begun your coding journey and determined that Python is the best place to start, you may be wondering about some of the most useful real-life applications. Here are a few that should be on your hotlist for Python programming.

Machine Learning Through Logistic Regression

Logistic regression in Python is vital for machine learning. The confusion matrix poses a challenge due to categorical variables, so a set of independent variables, like a train set, having at least one independent variable and one dependent variable, is needed. Missing data points regarding false negatives, false positives, true negatives, and null values can distort both your location data and training data.

A logistic regression model is superior to a linear regression model in this situation. This is because the output of your linear model will not match your input. Therefore, a proper machine learning model capitalizes on logistic regression in order to process the test data.

Law Enforcement Through License Plate Recognition

Now that you have used your programming skills to implement machine learning through logistic regression, you can use this skill to help law enforcement officers and local law enforcement agencies. This is because law enforcement agencies in the United States have to manage a large database, and machine learning is needed to manage the vast datasets within that database.

Take automatic license plate reader systems (ALPR systems), for example. Police departments rely heavily on artificial intelligence to navigate the massive database that contains their myriad of license plate numbers and license plate data in real-time. With ALPR systems, gone are the days when police officers and court officials have to stare through grainy video images of license plates. Instead, through the use of an automatic license plate reader, the ALPR data is digitized within a law enforcement database, and thus license plates don’t need to be interpreted with the naked eye. Federal agencies would also find ALPR technology useful for their criminal investigations and missing person cases. The importance of using reasonable security procedures to intercept criminal activity without misusing our personal information and violating privacy policies is of vital importance to various watchdog groups like the American Civil Liberties Union (ACLU).

Value Stream Mapping

Value stream mapping is on the hot list of the manufacturing process nowadays, and it’s vital for any vendor within a private company valuing metrics to learn about in order to improve supply chains, logistics, product development, customer service, etc. Numpy arrays within datasets are required to scale the sizable amount of data elements, sample data, and historical data involved.

This is especially true if the registered owner of the company in question has data scientists import all the input data from the database, such as a Pandas library, through the selection import train. The regularization of the accuracy of the model depends on the frequency of modification within the classification algorithm. This can have a chilling effect if the Pandas data frame doesn’t exhibit stronger regularization as you would normally have when you run a training dataset. You would learn to do in any tutorial in a model training, as you would see, for example, in receiving operator characteristic curve (ROC curve).

Pondering Python

When considering the applications of Python, it’s important to also consider the implications of machine learning. Logistic regression, license plate recognition, and NumPy arrange are closely connected to Python and machine learning. Data scientists and experts within a Python environment may be police officers, ALPR operators, federal agencies and local law enforcement agencies from various jurisdictions, manufacturers, customers, and the American Civil Liberties Union (ACLU), which safeguards our personal information and privacy policies through the appropriate data retention policy.