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Python Coding Homework Help are carried out by us in various domains such as Artificial Intelligence, Machine Learning, and Deep Learning, several projects have evolved, which are related to Python. If you are really facing difficulties then matlabprojects.org will be your trusted partner, get your work done with detailed explanation from our team. For assisting you to train and enhance your expertise, we recommend some important projects that specifically span from simple to moderate levels.

AI and Machine Learning Projects

  1. Linear Regression:
  • By means of NumPy, a basic linear regression model has to be applied from scratch.
  • In order to forecast a collection of certain data points, we employ the model.

import numpy as np

import matplotlib.pyplot as plt

# Generate some data

np.random.seed(0)

X = 2 * np.random.rand(100, 1)

y = 4 + 3 * X + np.random.randn(100, 1)

# Implement linear regression from scratch

X_b = np.c_[np.ones((100, 1)), X]  # Add bias term

theta_best = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y)

print(“Best fit parameters:”, theta_best)

# Plot the data and the regression line

plt.scatter(X, y)

plt.plot(X, X_b.dot(theta_best), color=’red’)

plt.xlabel(“X”)

plt.ylabel(“y”)

plt.title(“Linear Regression”)

plt.show()

  1. Logistic Regression:
  • In this project, employ NumPy to execute logistic regression from scratch.
  • To a binary categorization issue, the model must be employed.

import numpy as np

from sklearn.datasets import load_breast_cancer

from sklearn.preprocessing import StandardScaler

from sklearn.model_selection import train_test_split

# Load and prepare data

data = load_breast_cancer()

X = data.data

y = data.target.reshape(-1, 1)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

scaler = StandardScaler()

X_train = scaler.fit_transform(X_train)

X_test = scaler.transform(X_test)

# Sigmoid function

def sigmoid(z):

return 1 / (1 + np.exp(-z))

# Logistic regression function

def logistic_regression(X, y, lr=0.01, iterations=1000):

m, n = X.shape

theta = np.zeros((n, 1))

for i in range(iterations):

z = X.dot(theta)

h = sigmoid(z)

gradient = X.T.dot(h – y) / m

theta -= lr * gradient

return theta

# Train model

theta = logistic_regression(X_train, y_train)

# Predict and evaluate

def predict(X, theta):

return (sigmoid(X.dot(theta)) >= 0.5).astype(int)

y_pred = predict(X_test, theta)

accuracy = np.mean(y_pred == y_test)

print(“Test accuracy:”, accuracy)

  1. K-Nearest Neighbors (KNN):
  • From scratch, the KNN algorithm has to be applied.
  • With a particular dataset, carry out a categorization problem by implementing this algorithm.

import numpy as np

from sklearn.datasets import load_iris

from sklearn.model_selection import train_test_split

from collections import Counter

# Load and prepare data

iris = load_iris()

X = iris.data

y = iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# KNN algorithm

def knn(X_train, y_train, X_test, k=3):

distances = np.sqrt(((X_train – X_test[:, np.newaxis])**2).sum(axis=2))

knn_indices = np.argsort(distances, axis=1)[:, :k]

knn_labels = y_train[knn_indices]

most_common = [Counter(labels).most_common(1)[0][0] for labels in knn_labels]

return np.array(most_common)

# Predict and evaluate

y_pred = knn(X_train, y_train, X_test, k=3)

accuracy = np.mean(y_pred == y_test)

print(“Test accuracy:”, accuracy)

Deep Learning Projects

  1. Neural Network from Scratch:
  • Through NumPy, a basic feedforward neural network should be applied from scratch.
  • Consider the XOR problem to train it.

import numpy as np

# XOR dataset

X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])

y = np.array([[0], [1], [1], [0]])

# Sigmoid activation function

def sigmoid(x):

return 1 / (1 + np.exp(-x))

# Derivative of the sigmoid function

def sigmoid_derivative(x):

return x * (1 – x)

# Initialize weights and biases

np.random.seed(0)

input_size = 2

hidden_size = 2

output_size = 1

weights_input_hidden = np.random.rand(input_size, hidden_size)

weights_hidden_output = np.random.rand(hidden_size, output_size)

bias_hidden = np.random.rand(1, hidden_size)

bias_output = np.random.rand(1, output_size)

# Training parameters

learning_rate = 0.1

epochs = 10000

# Training loop

for epoch in range(epochs):

# Forward pass

hidden_layer_input = np.dot(X, weights_input_hidden) + bias_hidden

hidden_layer_output = sigmoid(hidden_layer_input)

output_layer_input = np.dot(hidden_layer_output, weights_hidden_output) + bias_output

output_layer_output = sigmoid(output_layer_input)

# Compute the error

error = y – output_layer_output

# Backpropagation

d_output = error * sigmoid_derivative(output_layer_output)

error_hidden_layer = d_output.dot(weights_hidden_output.T)

d_hidden = error_hidden_layer * sigmoid_derivative(hidden_layer_output)

# Update weights and biases

weights_hidden_output += hidden_layer_output.T.dot(d_output) * learning_rate

bias_output += np.sum(d_output, axis=0, keepdims=True) * learning_rate

weights_input_hidden += X.T.dot(d_hidden) * learning_rate

bias_hidden += np.sum(d_hidden, axis=0, keepdims=True) * learning_rate

# Test the network

hidden_layer_input = np.dot(X, weights_input_hidden) + bias_hidden

hidden_layer_output = sigmoid(hidden_layer_input)

output_layer_input = np.dot(hidden_layer_output, weights_hidden_output) + bias_output

output_layer_output = sigmoid(output_layer_input)

print(“Predicted output:\n”, output_layer_output)

  1. Convolutional Neural Network (CNN) with Keras:
  • By means of Keras, we develop a basic CNN. Using the MNIST dataset, the CNN must be trained.

import tensorflow as tf

from tensorflow.keras import layers, models

import matplotlib.pyplot as plt

# Load and prepare the MNIST dataset

(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()

X_train = X_train.reshape((X_train.shape[0], 28, 28, 1)).astype(‘float32’) / 255

X_test = X_test.reshape((X_test.shape[0], 28, 28, 1)).astype(‘float32′) / 255

# Build the CNN model

model = models.Sequential([

layers.Conv2D(32, (3, 3), activation=’relu’, input_shape=(28, 28, 1)),

layers.MaxPooling2D((2, 2)),

layers.Conv2D(64, (3, 3), activation=’relu’),

layers.MaxPooling2D((2, 2)),

layers.Conv2D(64, (3, 3), activation=’relu’),

layers.Flatten(),

layers.Dense(64, activation=’relu’),

layers.Dense(10, activation=’softmax’)

])

# Compile the model

model.compile(optimizer=’adam’,

loss=’sparse_categorical_crossentropy’,

metrics=[‘accuracy’])

# Train the model

history = model.fit(X_train, y_train, epochs=5, validation_data=(X_test, y_test))

# Evaluate the model

test_loss, test_acc = model.evaluate(X_test, y_test)

print(“Test accuracy:”, test_acc)

# Plot training history

plt.plot(history.history[‘accuracy’], label=’accuracy’)

plt.plot(history.history[‘val_accuracy’], label=’val_accuracy’)

plt.xlabel(‘Epoch’)

plt.ylabel(‘Accuracy’)

plt.legend(loc=’lower right’)

plt.show()

  1. Recurrent Neural Network (RNN) for Sequence Prediction:
  • As a means to forecast the subsequent value in a basic series, an RNN has to be created and trained with Keras.

import numpy as np

import tensorflow as tf

from tensorflow.keras import layers, models

# Generate a simple sequence dataset

def create_sequence(n_samples=1000, n_timesteps=10):

X = np.array([i for i in range(n_samples)])

y = np.array([i + 1 for i in range(n_samples)])

X = X.reshape((n_samples, 1))

y = y.reshape((n_samples, 1))

X_seq = []

y_seq = []

for i in range(len(X) – n_timesteps):

X_seq.append(X[i:i + n_timesteps])

y_seq.append(y[i + n_timesteps])

return np.array(X_seq), np.array(y_seq)

n_timesteps = 10

X, y = create_sequence(n_timesteps=n_timesteps)

X_train, X_test = X[:800], X[800:]

y_train, y_test = y[:800], y[800:]

# Build the RNN model

model = models.Sequential([

layers.SimpleRNN(50, activation=’relu’, input_shape=(n_timesteps, 1)),

layers.Dense(1)

])

# Compile the model

model.compile(optimizer=’adam’, loss=’mse’)

# Train the model

history = model.fit(X_train, y_train, epochs=20, validation_data=(X_test, y_test))

# Predict the next value

y_pred = model.predict(X_test)

# Plot the results

import matplotlib.pyplot as plt

plt.plot(y_test, label=’True’)

plt.plot(y_pred, label=’Predicted’)

plt.xlabel(‘Time step’)

plt.ylabel(‘Value’)

plt.legend()

plt.show()

Python coding homework services

Appropriate for different fields such as Computer Science and Engineering (CSE), Electronics and Communication Engineering (ECE), Information Technology (IT), Mechanical Engineering (MECH), and Electrical and Electronics Engineering (EEE), we list out a few Python coding homework projects. For supporting students to implement Python programming to their particular domain, these projects are specifically modeled.

Computer Science and Engineering (CSE) / Information Technology (IT)

  1. Data Structures – Linked List Implementation:
  • Including simple processes like traversal, insertion, and deletion, a singly linked list must be applied.

class Node:

def __init__(self, data):

self.data = data

self.next = None

class LinkedList:

def __init__(self):

self.head = None

def insert(self, data):

new_node = Node(data)

new_node.next = self.head

self.head = new_node

def delete(self, key):

temp = self.head

if temp is not None:

if temp.data == key:

self.head = temp.next

temp = None

return

while temp is not None:

if temp.data == key:

break

prev = temp

temp = temp.next

if temp == None:

return

prev.next = temp.next

temp = None

def print_list(self):

temp = self.head

while temp:

print(temp.data, end=’ ‘)

temp = temp.next

# Example usage

ll = LinkedList()

ll.insert(3)

ll.insert(2)

ll.insert(1)

ll.print_list()  # Output: 1 2 3

ll.delete(2)

ll.print_list()  # Output: 1 3

  1. Database Management – SQLite Integration:
  • A Python script has to be developed, which carries out CRUD processes through communicating with an SQLite database.

import sqlite3

# Connect to SQLite database

conn = sqlite3.connect(‘example.db’)

cursor = conn.cursor()

# Create table

cursor.execute(”’CREATE TABLE IF NOT EXISTS students

(id INTEGER PRIMARY KEY, name TEXT, age INTEGER)”’)

# Insert data

cursor.execute(“INSERT INTO students (name, age) VALUES (‘Alice’, 21)”)

cursor.execute(“INSERT INTO students (name, age) VALUES (‘Bob’, 22)”)

# Read data

cursor.execute(“SELECT * FROM students”)

print(cursor.fetchall())

# Update data

cursor.execute(“UPDATE students SET age = 23 WHERE name = ‘Bob'”)

# Delete data

cursor.execute(“DELETE FROM students WHERE name = ‘Alice'”)

conn.commit()

conn.close()

Electronics and Communication Engineering (ECE)

  1. Signal Processing – Fourier Transform:
  • In this project, we focus on applying the Discrete Fourier Transform (DFT). Then, the frequency elements of a signal have to be visualized.

import numpy as np

import matplotlib.pyplot as plt

# Generate a sample signal

t = np.linspace(0, 1, 500)

signal = np.sin(2 * np.pi * 5 * t) + np.sin(2 * np.pi * 20 * t)

# Compute DFT

def DFT(signal):

N = len(signal)

dft = np.zeros(N, dtype=complex)

for k in range(N):

for n in range(N):

dft[k] += signal[n] * np.exp(-2j * np.pi * k * n / N)

return dft

dft_signal = DFT(signal)

# Plot the signal and its frequency components

plt.subplot(2, 1, 1)

plt.plot(t, signal)

plt.title(‘Original Signal’)

plt.subplot(2, 1, 2)

plt.stem(np.abs(dft_signal))

plt.title(‘DFT of Signal’)

plt.show()

Electrical and Electronics Engineering (EEE)

  1. Circuit Analysis – Ohm’s Law Simulation:
  • To simulate Ohm’s Law, a Python program should be drafted. By offering any two elements such as resistance, current, or voltage, estimate the other.

def ohms_law(voltage=None, current=None, resistance=None):

if voltage is None:

return current * resistance

elif current is None:

return voltage / resistance

elif resistance is None:

return voltage / current

# Example usage

voltage = ohms_law(current=2, resistance=5)

print(f”Voltage: {voltage} V”)  # Output: Voltage: 10 V

current = ohms_law(voltage=10, resistance=5)

print(f”Current: {current} A”)  # Output: Current: 2 A

resistance = ohms_law(voltage=10, current=2)

print(f”Resistance: {resistance} Ohms”)  # Output: Resistance: 5 Ohms

Mechanical Engineering (MECH)

  1. Thermodynamics – Ideal Gas Law Simulation:
  • A Python program has to be developed, which provides any two properties like volume, pressure, or temperature of a gas to estimate the other with Ideal Gas Law.

def ideal_gas_law(P=None, V=None, T=None, n=1, R=8.314):

if P is None:

return (n * R * T) / V

elif V is None:

return (n * R * T) / P

elif T is None:

return (P * V) / (n * R)

# Example usage

pressure = ideal_gas_law(V=0.1, T=300)

print(f”Pressure: {pressure} Pa”)  # Output: Pressure: 2494.2 Pa

volume = ideal_gas_law(P=101325, T=300)

print(f”Volume: {volume} m^3″)  # Output: Volume: 0.0246 m^3

temperature = ideal_gas_law(P=101325, V=0.0246)

print(f”Temperature: {temperature} K”)  # Output: Temperature: 300.0 K

  1. Finite Element Analysis (FEA) – 1D Truss Analysis:
  • In a truss structure, we assess the pressures and shifts by carrying out a basic 1D truss analysis.

import numpy as np

# Stiffness matrix for a 2-node 1D truss element

def stiffness_matrix(E, A, L):

return (E * A / L) * np.array([[1, -1], [-1, 1]])

# Global stiffness matrix assembly for a truss structure

def global_stiffness_matrix(elements, nodes, E, A):

n = len(nodes)

K = np.zeros((n, n))

for element in elements:

node1, node2, L = element

k = stiffness_matrix(E, A, L)

K[node1:node2+1, node1:node2+1] += k

return K

# Example truss structure

nodes = [0, 1, 2]

elements = [(0, 1, 1.0), (1, 2, 1.0)]

E = 210e9  # Young’s modulus in Pascals

A = 0.01  # Cross-sectional area in square meters

K = global_stiffness_matrix(elements, nodes, E, A)

print(“Global Stiffness Matrix:\n”, K)

For the purposes of training and knowledge enhancement, numerous intriguing projects are suggested by us. Related to various domains like CSE, ECE, IT, MECH, and EEE, we proposed several Python coding homework projects, along with concise explanations.

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