MacLearn

Installation - How to install

The easiest way to install maclearn is using pip. Open your teminal and enter -
pip install maclearn

Instruction - How to use maclearn

Using maclearn in your project is simple enough. Currently, you'll find only 2 algorithms in this module. We are working to develop more.

Reading Excel Files (.csv/.xlsx)

Reading excel files using maclearn is quite easy.
Let's say we want to read this file (PizzaPrice.csv) -

Size	Price
6	350
8	775
12	1150
14	1395
17	1648
18	1675

The file may be in .xlsx or .csv format but the extension doesn't matter. Here's how to read it -
For .csv format -


        import maclearn # importing the module
        
        filename = "PizzaPrice.csv"  # Initialize the directory
        file = maclearn.csv(filename)  # Create a csv object
        data = file.data()  # Read the data and store it as a list
        print(data)  # Print the data

            Result - [[6, 8, 12, 14, 17, 18], [350, 775, 1150, 1395, 1648, 1675]]

For .xlsx format -

            import maclearn # importing the module
            
            filename = "PizzaPrice.xlsx"  # Initialize the directory
            file = maclearn.xlsx(filename)  # Create a xlsx object
            data = file.data()  # Read the data and store it as a list
            print(data)  # Print the data

            Result - [[6, 8, 12, 14, 17, 18], [350, 775, 1150, 1395, 1648, 1675]]

If you want, you can put file.data() in the fit method of GaussianNaiveBayes or LinearRegression in order to fit the data to your model from an excel file

Gaussian Naive Bayes Classifier

First create the object GaussianNaiveBayes()...
Then call the fit() method to fit the data to your model.
Note: Your data must be in this format - [Feature1, Feature2, Feature3, Label]...
Here, you can use as many features as you want...
After that, simply call the predict(*Features) method to predict the label....
You can calculate the F1 Measure for your model by simply calling the f1_measure() method.
You can also calculate the specificity for your model by simply calling the specificity() method.
Example:


        import maclearn # Importing the module
        # Features
        Outlook = ["Sunny", "Sunny", "Cloudy", "Sunny", "Cloudy", "Cloudy", "Sunny"]
        Temperature = ["Cold", "Warm", "Warm", "Warm", "Cold", "Cold", "Cold"]
        Routine = ["Indoor", "Outdoor", "Indoor", "Indoor", "Indoor", "Outdoor", "Outdoor", "Outdoor"]
        WearCoat = ["No", "No", "No", "No", "Yes", "Yes", "Yes"] # Labels
        
        # You can also import the data from an excel file
        Features = [Outlook, Temperature, Routine, WearCoat] # Putting all features in a list
        
        model = maclearn.GaussianNaiveBayes()  # Creating the object
        model.fit(Features) # Fit the data into the model

        # Should we wear coat, if our feature set is cloudy, warm and outdoor?
        print(model.predict("Cloudy", "Warm", "Outdoor")) # Predict the label
        f1_value = model.f1_measure() # Calculate the F1 measure value for the model
        specificity = model.specificity() # Calculate the specificity for the model
        print(f1_value * 100, "%") # print F1 measure value in percentage
        print(specificity * 100, "%") # print specificity in percentage

Linear Regression with numerical analysis

First create the object - LinearRegression()...
Then call the fit(x, y) method to fit the data to your model.
This model is for single variable linear regression. So your data must be in this format - X=[2,3,4], Y=[5,6,7]
Then, just call the predict(x) method with the argument of x value to predict the y value...
You can also calculate the R-Squared value by simply calling r_squared() method.
Example:


        import maclearn # Importing the module

        # You can also import the data from an excel file
        Size = [6, 8, 12, 14, 18] # Size of Pizza in inches (Independent Variable)
        Price = [350, 775, 1150, 1395, 1675] # Price of Pizza in Taka (Dependent Variable)
                    
        model = maclearn.LinearRegression()  # Creating the object
        model.fit(Size, Price) # Fit the data into the model

        predicted = model.predict(17) # Predicted the price of 17 inches pizza
        accuracy = model.r_squared() # Calculate the R-Squared value for the model

        print(predicted) # Print the predicted value
        print(accuracy * 100, "%") # Print the R-Squared value in percenntage

Lisence

This module is completely free and open source. You can use and modify to improve the module if you want ;)
Any suggestion will be highly appriciated. Gmail - neural.gen.official@gmail.com

Created by Sajedur Rahman Fiad

Release files for MacLearn 1.0.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for MacLearn 1.0.1
File	Size	Uploaded
MacLearn-1.0.1.tar.gz	5.5 kB	Oct 18, 2021	Details

Release files / MacLearn-1.0.1.tar.gz

Download URL	MacLearn-1.0.1.tar.gz
Size	5.5 kB
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