ml-solutions 0.0.3

A Python Pacakage for Ready Made Machine Learning Solutions.

Ml-Solutions

A Python Pacakage for Ready Made Machine Learning Solutions.

Installation

To install this package, you have to run this command on your terminal

pip install ml-solutions

Quick Start Guide

from ml_solutions import solutions



# Spam Message Detector



spam_message_detector = solutions.SpamMessageDetector()

spam_message_detector.train()

detected = spam_message_detector.detect_spam('Please subscribe my youtube channel for 100 years of good luck!')



if detected == True:

    print("Message is Spam!")

else:

    print("The message is not Spam!")



# House Price Prediction



house_price_predictor = solutions.HousePricePrediction()

house_price_predictor.train()

Price = house_price_predictor.get_price(rooms=2, bathrooms=1, landsize=230, lattidute=-346.876, longtitude=347.378)

print(Price)



# Image Classification



# Training and Saving The Model

image_classifier = solutions.ImageClassification()

image_classifier.train()

image_classifier.save_model('image_classifier')



# After that you will see your training of the model has been started!

# When training is complete, then remove all the code and write:



# Loading the Model

image_classifier = solutions.ImageClassification()

image_classifier.load_model('image_classifier')

# For image classification, you need a 32*32 px image, to convert your image into a 32*32 px image, go into this website: https://www.privatedaddy.com/resize-to-32x32

# After you converted your image, give the path to the 32*32 image

recognized = image_classifier.recognize_image('image.jpg')

print(recognized)



# You will see the name of the thing in the image :)

# This supports with 'Plane', 'Car', 'Bird', 'Cat', 'Deer', 'Dog', 'Frog', 'Horse', 'Ship', 'Truck'



# And we have more Ml Solutions :)

ML Algorithms

from ml_solutions import algorithms



# HLRegression (HyperLinearRegression)

X_train = np.array(X_train)

Y_train = np.array(Y_train)

X_test = np.array(X_test)



model = algorithms.HLRegression()

model.train_model(X_train, Y_train)

Prediction = model.predict(X_test)

print(Prediction)



# We will make more algorithms :)