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Very good work and effort The Python homework was rewritten, and I am pleased with the work's overall quality as well.
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My Python homework was thoroughly reviewed by the expert. Based on the document, he made several really good points.
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Python, a widely utilized interactive and high-level object-oriented programming language, finds extensive application in web app development. Its approachable syntax ensures accessibility for a diverse range of users. It allows users to code efficiently. The portable programming languages are supported on various operating systems such as Windows, Unix, and Linux. It has a high-level data structure and allows dynamic typing and dynamic binding. With this, you can develop complicated apps. Python's versatility extends to making system calls on all operating systems and executing code written in C and C++. Its cross-system compatibility allows for diverse app development across various system architectures.
It is open-source, powerful, and makes programming fun. Python coders can type the variables dynamically without having to explain each one of them. The syntax is readable and often is used in analytics, machine learning, and web development. The interpreter used in this language will help you find bugs.
Python is a versatile programming language with applications spanning from machine learning to data science.
The IDE is the place where you edit and write the code. Following are the IDEs that allow you to write Python code and execute
Following are the Python libraries that you can use:
Python assignments can pose certain challenges, and here are some common questions students might encounter while working on them:
• Creating a 3D Space: To create a 3D space in Python, employ the Matplotlib library. Begin by importing the Axes3D module from Matplotlib, then utilize the axes method to generate a 3D plot. Incorporate the plot method to incorporate data into the plot.
• Calculating Series in Python: Python provides built-in libraries like NumPy and Pandas for series calculations. NumPy assists in mathematical operations on arrays, while Pandas helps in creating and manipulating dataframes for series calculations.
• Fitting Data to Gaussian Distribution: For fitting data to a Gaussian distribution in Python, rely on the SciPy library. Import the stats module from SciPy, create a normal distribution using the norm method, and use the fit method to align the distribution with your data.
• Returning an Integer Array: To return an integer array in Python, utilize the numpy library. Formulate an array and then apply slicing or indexing to extract a specific segment or element.
• Combining Bytes: To combine bytes in Python, employ the b'' syntax to form a byte array. Merge byte arrays using the + operator or the join() method. Convert the byte array to a mutable integer sequence using the bytearray() method.
• Obtaining Output as a List: For acquiring output as a list in Python, apply the list() function. Pass the output to this function to convert it into a list. Alternatively, leverage list comprehension to shape a list from the output.
• Plotting x/y/z Data: To plot x/y/z data, rely on the Matplotlib library. Establish a 3D plot using the Axes3D method and integrate your data using the plot_surface method.
• Saving Multiple Models: Save multiple models in Python using the joblib library. Import the dump() method from joblib to save models to a file. Use the load() method to reload models from the file.
• Creating a New Binary Variable: Forge a new binary variable in Python by creating a boolean variable. Convert the boolean variable to an integer using the astype() method, generating a binary variable where True equals 1 and False equals 0.
• Gaining Insights from Data: Extract insights from data with Python using analysis and visualization libraries like NumPy, Pandas, and Matplotlib. Employ exploratory data analysis for pattern identification and visualization techniques to communicate insights effectively.
• Displaying Clusters: To display clusters in Python, construct scatter plots with the Matplotlib library. Assign colors based on cluster membership and differentiate clusters using shapes or sizes.
• Obtaining an Image Matrix: Utilize the OpenCV library to obtain an image matrix in Python. Read the image using imread() and convert it to a matrix using the cvtColor() method. Manipulate the matrix as needed for analysis.
These questions touch on technical concepts often encountered in Python assignments, and mastering them can lead to successful completion of your tasks.
Students when assigned to write a program in Python based on the specifications given by the professors, end up struggling a lot. They spend sleepless nights, but could not get the desired output. However, we have a team of Python developers who have enough experience and a good amount of knowledge in writing Python homework. The programs or solutions done by our team will help you secure good grades in the examination.
Python serves as the foundation for data science, providing data analysts with a versatile language for executing intricate statistical computations, crafting compelling visual representations, formulating machine learning algorithms, conducting data analysis, and executing various data-related tasks. Python's capabilities encompass the creation of diverse data visualizations, ranging from fundamental line and bar graphs to intricate pie charts, histograms, and 3D plots. Additionally, the language boasts an array of libraries, including Keras and TensorFlow, which streamline the creation of efficient programs for machine learning and robust data analysis.
Python develops the backend of the website and application which users do not see. The role of Python in developing a web app or website is to send and receive data from the server, process data and communicate with the database. Various frameworks are offered by this language for web development and the main ones include Flask and Django.
Some of the popular topics in Python on which our programming assignment & homework experts work on a daily basis are listed below:
| Machine Learning | Object-Oriented Programming |
| Data Science | Python Shell |
| Data Analysis | GUI Programming in Python (TkInter) |
| Operators in Python | Working with RESTful APIs |
| String Manipulation | SQLite |
| Loops in Python | Network Programming in Python |
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Code for: the setosa vs. versicolor learning problem,
Solution:
import numpy as np
class Perceptron(object):
"""Perceptron
Parameters
------------
eta : float
Learning rate (between 0.0 and 1.0)
n_iter : int
Passes over the training dataset.
random_state : int
Random weight initialization seed for a random number generator.
Attributes
-----------
w : 1d-array
Weights after fitting.
errors : list
Number of updates (misclassifications) during each epoch.
"""
def __init__(self, eta=0.01, n_iter=50, random_state=1):
self.eta = eta
self.n_iter = n_iter
self.random_state = random_state
def fit(self, X, y):
"""Fit training data.
Parameters
----------
X : {array-like}, shape = [n_samples, n_features]
Training vectors, with n_samples denoting the sample count and
n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
Returns
-------
self : object
"""
rgen = np.random.RandomState(self.random_state)
self.w = rgen.normal(loc=0.0, scale=0.01, size=1 + X.shape[1])
self.errors = []
for i in range(self.n_iter):
errors = 0
for xi, target in zip(X, y):
update = self.eta * (target - self.predict(xi))
self.w[1:] += update * xi
self.w[0] += update
errors += int(update != 0.0)
self.errors.append(errors)
return self
def net_input(self, X):
"""Calculate net input"""
return np.dot(X, self.w[1:]) + self.w[0]
def predict(self, X):
"""Return class label after unit step"""
return np.where(self.net_input(X) >= 0.0, 1, -1)
# ### Reading-in the Iris data
import pandas as pd
pd.options.mode.chained_assignment = None # default='warn'
df = pd.read_csv('https://archive.ics.uci.edu/ml/'
'machine-learning-databases/iris/iris.data', header=None)
# We are selecting first 100 samples only
(as both Versicolor and Sentosa categories are present in these 100 samples only)
df2 = df.head(100)
# Representing Sentosa as 1 and Versicolor as -1
y = np.array([1]*50 + [-1]*50)
# Drop the last column
(which represents target and store the remaining which are the features)
X_all = df2.drop([4],axis=1)
print('\nFeature-1 is left out\n')
X = X_all[[1,2,3]].to_numpy()
# Fitting the model with X as input and y as labels and only 4 iterations
model = Perceptron(n_iter=4)
model.fit(X,y)
# Calculating the sum of the errors
sum_of_errors = sum(model.errors)
print('Sum of errors:',sum_of_errors)
print('\nFeature-2 is left out\n')
X = X_all[[0,2,3]].to_numpy()
# Fitting the model with X as input and y as labels and only 4 iterations
model = Perceptron(n_iter=4)
model.fit(X,y)
# Calculating the sum of the errors
sum_of_errors = sum(model.errors)
print('Sum of errors:',sum_of_errors)
print('\nFeature-3 is left out\n')
X = X_all[[0,1,3]].to_numpy()
# Fitting the model with X as input and y as labels and only 4 iterations
model = Perceptron(n_iter=4)
model.fit(X,y)
# Calculating the sum of the errors
sum_of_errors = sum(model.errors)
print('Sum of errors:',sum_of_errors)
print('\nFeature-4 is left out\n')
X = X_all[[0,1,2]].to_numpy()
# Fitting the model with X as input and y as labels and only 4 iterations
model = Perceptron(n_iter=4)
model.fit(X,y)
# Calculating the sum of the errors
sum_of_errors = sum(model.errors)
print('Sum of errors:',sum_of_errors)
"""####
When we train the model for setosa and versicolor, we can see this,
the sum of the errors after training the model is minimum in case when we exclude either
feature-1 or feature-4 (both have same sum of errors = 5).
So we can exclude any one feature. We will exclude feature-4 after 4 iterations!"""
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