Python Assignment Expert

Python Assignment Experts at phdservices.org will provide you with full support and assistance that are required for a successful completion of your project. Across various domains such as Artificial Intelligence, Machine Learning, and Deep Learning, Python is employed in an extensive manner. By encompassing simple to moderate topics, we list out some interesting projects. To interpret and implement significant theories in these domains, these projects assist students in an efficient manner.

Artificial Intelligence (AI)

Project 1: Tic-Tac-Toe Game with Minimax Algorithm

Goal: A Tic-Tac-Toe game has to be carried out, in which the computer plays in an ideal manner by utilizing the Minimax algorithm.

Missions:

  1. A Tic-Tac-Toe board must be developed. Then, initiate the game by enabling two players.
  2. In order to take ideal steps, the Minimax algorithm should be applied for the computer.
  3. To play in opposition to the computer, we have to combine the Minimax algorithm with the game.

import numpy as np

def print_board(board):

for row in board:

print(” “.join(row))

def check_winner(board):

for row in board:

if row[0] == row[1] == row[2] != ” “:

return row[0]

for col in range(3):

if board[0][col] == board[1][col] == board[2][col] != ” “:

return board[0][col]

if board[0][0] == board[1][1] == board[2][2] != ” “:

return board[0][0]

if board[0][2] == board[1][1] == board[2][0] != ” “:

return board[0][2]

return None

def minimax(board, depth, is_maximizing):

winner = check_winner(board)

if winner == “X”:

return 1

elif winner == “O”:

return -1

elif ” ” not in board.flatten():

return 0

if is_maximizing:

best_score = -np.inf

for i in range(3):

for j in range(3):

if board[i][j] == ” “:

board[i][j] = “X”

score = minimax(board, depth + 1, False)

board[i][j] = ” ”

best_score = max(score, best_score)

return best_score

else:

best_score = np.inf

for i in range(3):

for j in range(3):

if board[i][j] == ” “:

board[i][j] = “O”

score = minimax(board, depth + 1, True)

board[i][j] = ” ”

best_score = min(score, best_score)

return best_score

def best_move(board):

best_score = -np.inf

move = (0, 0)

for i in range(3):

for j in range(3):

if board[i][j] == ” “:

board[i][j] = “X”

score = minimax(board, 0, False)

board[i][j] = ” ”

if score > best_score:

best_score = score

move = (i, j)

return move

def play_game():

board = np.array([[” ” for _ in range(3)] for _ in range(3)])

while True:

print_board(board)

row = int(input(“Enter row (0, 1, 2): “))

col = int(input(“Enter col (0, 1, 2): “))

if board[row][col] == ” “:

board[row][col] = “O”

else:

print(“Invalid move. Try again.”)

continue

if check_winner(board) or ” ” not in board.flatten():

break

move = best_move(board)

board[move] = “X”

if check_winner(board) or ” ” not in board.flatten():

break

print_board(board)

winner = check_winner(board)

if winner:

print(f”{winner} wins!”)

else:

print(“It’s a tie!”)

play_game()

Project 2: A* Search Algorithm

Goal: As a means to identify the shortest route in a grid, the A* search algorithm must be applied.

Missions:

  1. Including barriers, we need to develop a grid.
  2. Then, the A* search algorithm has to be employed.
  3. The route should be visualized, which is detected by the algorithm.

import heapq

def heuristic(a, b):

return abs(a[0] – b[0]) + abs(a[1] – b[1])

def astar_search(grid, start, goal):

neighbors = [(0, 1), (0, -1), (1, 0), (-1, 0)]

close_set = set()

came_from = {}

gscore = {start: 0}

fscore = {start: heuristic(start, goal)}

oheap = []

heapq.heappush(oheap, (fscore[start], start))

while oheap:

current = heapq.heappop(oheap)[1]

if current == goal:

data = []

while current in came_from:

data.append(current)

current = came_from[current]

return data

close_set.add(current)

for i, j in neighbors:

neighbor = current[0] + i, current[1] + j

tentative_g_score = gscore[current] + 1

if 0 <= neighbor[0] < grid.shape[0]:

if 0 <= neighbor[1] < grid.shape[1]:

if grid[neighbor[0]][neighbor[1]] == 1:

continue

else:

continue

else:

continue

if neighbor in close_set and tentative_g_score >= gscore.get(neighbor, 0):

continue

if tentative_g_score < gscore.get(neighbor, 0) or neighbor not in [i[1] for i in oheap]:

came_from[neighbor] = current

gscore[neighbor] = tentative_g_score

fscore[neighbor] = tentative_g_score + heuristic(neighbor, goal)

heapq.heappush(oheap, (fscore[neighbor], neighbor))

return False

grid = np.array([[0, 1, 0, 0, 0, 0],

[0, 1, 0, 1, 1, 0],

[0, 0, 0, 0, 1, 0],

[0, 1, 1, 0, 0, 0],

[0, 0, 1, 0, 1, 0],

[0, 0, 0, 0, 0, 0]])

start = (0, 0)

goal = (5, 5)

path = astar_search(grid, start, goal)

if path:

for step in path:

grid[step] = 2

grid[start] = 3

grid[goal] = 4

print(“Path found:”)

print(grid)

else:

print(“No path found”)

Machine Learning (ML)

Project 3: Predicting House Prices with Linear Regression

Goal: In order to forecast house prices, our project utilizes a linear regression model.

Missions:

  1. Initially, the Boston housing dataset should be imported.
  2. A linear regression model must be applied and trained.
  3. Then, we have to assess the functionality of the model.

import numpy as np

import pandas as pd

from sklearn.datasets import load_boston

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LinearRegression

from sklearn.metrics import mean_squared_error

# Load the dataset

boston = load_boston()

data = pd.DataFrame(boston.data, columns=boston.feature_names)

data[‘PRICE’] = boston.target

# Split the data

X = data.drop(‘PRICE’, axis=1)

y = data[‘PRICE’]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train the model

model = LinearRegression()

model.fit(X_train, y_train)

# Predict and evaluate

y_pred = model.predict(X_test)

mse = mean_squared_error(y_test, y_pred)

print(f”Mean Squared Error: {mse:.2f}”)

# Print model coefficients

print(“Model Coefficients:”)

print(pd.Series(model.coef_, index=X.columns))

Project 4: Iris Flower Classification with k-NN

Goal: With the aim of categorizing iris flowers, we apply a k-nearest neighbors (k-NN) classifier.

Missions:

  1. The Iris dataset must be imported.
  2. Then, a k-NN classifier has to be employed and trained.
  3. Focus on assessing the functionality of the model.

import numpy as np

import pandas as pd

from sklearn.datasets import load_iris

from sklearn.model_selection import train_test_split

from sklearn.neighbors import KNeighborsClassifier

from sklearn.metrics import accuracy_score

# Load the dataset

iris = load_iris()

data = pd.DataFrame(iris.data, columns=iris.feature_names)

data[‘species’] = iris.target

# Split the data

X = data.drop(‘species’, axis=1)

y = data[‘species’]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train the model

knn = KNeighborsClassifier(n_neighbors=3)

knn.fit(X_train, y_train)

# Predict and evaluate

y_pred = knn.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)

print(f”Accuracy: {accuracy:.2f}”)

# Print classification report

from sklearn.metrics import classification_report

print(classification_report(y_test, y_pred, target_names=iris.target_names))

Deep Learning

Project 5: Handwritten Digit Recognition with CNN

Goal: From the MNIST dataset, handwritten digits have to be categorized by utilizing a convolutional neural network (CNN).

Missions:

  1. To carry out this project, we should import the MNIST dataset.
  2. A CNN must be applied and trained.
  3. Then, the model’s functionality has to be assessed.

import numpy as np

import tensorflow as tf

from tensorflow.keras.datasets import mnist

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

from tensorflow.keras.utils import to_categorical

# Load the dataset

(X_train, y_train), (X_test, y_test) = mnist.load_data()

X_train = X_train.reshape(-1, 28, 28, 1) / 255.0

X_test = X_test.reshape(-1, 28, 28, 1) / 255.0

y_train = to_categorical(y_train, 10)

y_test = to_categorical(y_test, 10)

# Build the model

model = Sequential([

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

MaxPooling2D((2, 2)),

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

MaxPooling2D((2, 2)),

Flatten(),

Dense(64, activation=’relu’),

Dense(10, activation=’softmax’)

])

# Compile the model

model.compile(optimizer=’adam’, loss=’categorical_crossentropy’, metrics=[‘accuracy’])

# Train the model

model.fit(X_train, y_train, epochs=5, batch_size=32, validation_split=0.2)

# Evaluate the model

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

print(f”Test Accuracy: {test_acc:.2f}”)

Project 6: Text Sentiment Analysis with RNN

Goal: On text data, the sentiment analysis process has to be carried out through applying a recurrent neural network (RNN).

Missions:

  1. A text-based sentiment dataset should be imported.
  2. An RNN must be employed and trained.
  3. Then, we need to assess the functionality of the model.

import numpy as np

import pandas as pd

import tensorflow as tf

from tensorflow.keras.preprocessing.text import Tokenizer

from tensorflow.keras.preprocessing.sequence import pad_sequences

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Embedding, LSTM, Dense

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score

# Load the dataset

url = ‘https://raw.githubusercontent.com/laxmimerit/IMDB-Movie-Reviews-LSTM/master/train.tsv’

df = pd.read_csv(url, sep=’\t’)

sentences = df[‘Phrase’].values

labels = df[‘Sentiment’].values

# Tokenize the text

tokenizer = Tokenizer(num_words=10000)

tokenizer.fit_on_texts(sentences)

X = tokenizer.texts_to_sequences(sentences)

X = pad_sequences(X, maxlen=100)

y = pd.get_dummies(labels).values

# Split the data

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Build the model

model = Sequential([

Embedding(10000, 64, input_length=100),

LSTM(64, return_sequences=True),

LSTM(64),

Dense(5, activation=’softmax’)

])

# Compile the model

model.compile(optimizer=’adam’, loss=’categorical_crossentropy’, metrics=[‘accuracy’])

# Train the model

model.fit(X_train, y_train, epochs=5, batch_size=32, validation_split=0.2)

# Evaluate the model

y_pred = model.predict(X_test)

accuracy = accuracy_score(y_test.argmax(axis=1), y_pred.argmax(axis=1))

print(f”Accuracy: {accuracy:.2f}”)

Relevant to different domains such as artificial intelligence, machine learning, and deep learning, we proposed numerous intriguing projects that span from simple to moderate topics. In addition to that, explicit goals and missions are suggested by us, along with an instance of code.

Python assignment assistance is provided by our developers across all domains. If you find yourself in need of clarification, do not hesitate to contact us. Phdservices.org offers Python assignment support not only for students embarking on their programming journey but also for developers in search of additional guidance. Our online Python specialists are available to address your inquiries and assist you in discovering solutions.

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