Python help function serves to provide assistance regarding the object that is passed to it during invocation. It accepts an optional parameter and returns relevant help information. Get in touch with our huge expert team we come up with original project ideas tailored to your needs. Python is examined as an important programming language that supports several major functions. For different Deep Learning (DL), Machine Learning (ML), and Artificial Intelligence (AI) algorithms, we suggest a few sample functions related to Python:

AI: A* Search Algorithm

import heapq

def a_star_search(start, goal, graph):

def heuristic(a, b):

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

open_set = []

heapq.heappush(open_set, (0, start))

came_from = {}

g_score = {node: float(‘inf’) for node in graph}

g_score[start] = 0

f_score = {node: float(‘inf’) for node in graph}

f_score[start] = heuristic(start, goal)

while open_set:

current = heapq.heappop(open_set)[1]

if current == goal:

path = []

while current in came_from:

path.append(current)

current = came_from[current]

path.append(start)

return path[::-1]

for neighbor in graph[current]:

tentative_g_score = g_score[current] + graph[current][neighbor]

if tentative_g_score < g_score[neighbor]:

came_from[neighbor] = current

g_score[neighbor] = tentative_g_score

f_score[neighbor] = g_score[neighbor] + heuristic(neighbor, goal)

heapq.heappush(open_set, (f_score[neighbor], neighbor))

return None

# Example usage:

graph = {

(0, 0): {(1, 0): 1, (0, 1): 1},

(1, 0): {(1, 1): 1, (0, 0): 1},

(0, 1): {(1, 1): 1, (0, 0): 1},

(1, 1): {(1, 0): 1, (0, 1): 1}

}

start = (0, 0)

goal = (1, 1)

print(a_star_search(start, goal, graph))

ML: Linear Regression

from sklearn.linear_model import LinearRegression

import numpy as np

def linear_regression(X, y):

model = LinearRegression()

model.fit(X, y)

return model

# Example usage:

X = np.array([[1], [2], [3], [4]])

y = np.array([2, 3, 5, 7])

model = linear_regression(X, y)

print(model.predict(np.array([[5]])))  # Predicting for a new value

DL: Convolutional Neural Network (CNN)

import tensorflow as tf

from tensorflow.keras.models import Sequential

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

def create_cnn(input_shape, num_classes):

model = Sequential([

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

MaxPooling2D((2, 2)),

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

MaxPooling2D((2, 2)),

Flatten(),

Dense(64, activation=’relu’),

Dense(num_classes, activation=’softmax’)

])

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

return model

# Example usage:

input_shape = (28, 28, 1)  # Example for MNIST dataset

num_classes = 10

cnn_model = create_cnn(input_shape, num_classes)

NLP: Sentiment Analysis using NLTK

from nltk.sentiment.vader import SentimentIntensityAnalyzer

def analyze_sentiment(text):

sia = SentimentIntensityAnalyzer()

return sia.polarity_scores(text)

# Example usage:

text = “Python is such an amazing programming language!”

print(analyze_sentiment(text))

RL: Q-Learning

import numpy as np

def q_learning(env, num_episodes, alpha, gamma, epsilon):

q_table = np.zeros((env.observation_space.n, env.action_space.n))

for episode in range(num_episodes):

state = env.reset()

done = False

while not done:

if np.random.uniform(0, 1) < epsilon:

action = env.action_space.sample()

else:

action = np.argmax(q_table[state, :])

next_state, reward, done, _ = env.step(action)

q_table[state, action] = q_table[state, action] + alpha * (reward + gamma * np.max(q_table[next_state, :]) – q_table[state, action])

state = next_state

return q_table

# Example usage:

# import gym

# env = gym.make(‘FrozenLake-v0’)

# q_table = q_learning(env, 10000, 0.1, 0.99, 0.1)

Time Series Analysis: ARIMA

from statsmodels.tsa.arima_model import ARIMA

def arima_forecasting(series, order):

model = ARIMA(series, order=order)

model_fit = model.fit(disp=0)

return model_fit

# Example usage:

import pandas as pd

series = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])

order = (1, 1, 1)

model_fit = arima_forecasting(series, order)

print(model_fit.forecast(steps=5))

Python Thesis help

Data loading and preprocessing are considered as significant procedures that can be carried out with the aid of appropriate functions. In order to load and preprocess datasets across various domains such as computer vision, natural language processing, finance, healthcare, and others, we list out some important functions:

1. Healthcare: MIMIC-III

import pandas as pd

def load_mimiciii(file_path):

df = pd.read_csv(file_path)

# Perform necessary preprocessing here, such as handling missing values, data type conversions, etc.

df.dropna(inplace=True)

return df

# Example usage:

# df = load_mimiciii(‘path/to/MIMIC-III.csv’)

2. Finance: Yahoo Finance API

import yfinance as yf

def load_yahoo_finance(ticker, start_date, end_date):

stock_data = yf.download(ticker, start=start_date, end=end_date)

# Perform necessary preprocessing here

stock_data.fillna(method=’ffill’, inplace=True)

return stock_data

# Example usage:

# df = load_yahoo_finance(‘AAPL’, ‘2020-01-01’, ‘2021-01-01’)

3. Natural Language Processing: IMDb Movie Reviews

import pandas as pd

def load_imdb_reviews(file_path):

df = pd.read_csv(file_path, delimiter=’\t’, quoting=3)  # TSV file with no quotes

# Perform necessary preprocessing here, such as removing HTML tags, converting to lowercase, etc.

df[‘review’] = df[‘review’].str.replace(‘<[^<]+?>’, ”)  # Remove HTML tags

df[‘review’] = df[‘review’].str.lower()  # Convert to lowercase

return df

# Example usage:

# df = load_imdb_reviews(‘path/to/IMDb_Reviews.csv’)

4. Computer Vision: CIFAR-10

from tensorflow.keras.datasets import cifar10

def load_cifar10():

(train_images, train_labels), (test_images, test_labels) = cifar10.load_data()

# Perform necessary preprocessing here, such as normalization

train_images, test_images = train_images / 255.0, test_images / 255.0

return (train_images, train_labels), (test_images, test_labels)

# Example usage:

# (train_images, train_labels), (test_images, test_labels) = load_cifar10()

5. Time Series: UCI Machine Learning Repository – Air Quality Dataset

import pandas as pd

def load_air_quality(file_path):

df = pd.read_csv(file_path, sep=’;’, decimal=’,’)

# Perform necessary preprocessing here, such as handling missing values, converting data types, etc.

df.dropna(inplace=True)

return df

# Example usage:

# df = load_air_quality(‘path/to/AirQualityUCI.csv’)

6. Genomics: 1000 Genomes Project

import pandas as pd

def load_genomics_data(file_path):

df = pd.read_csv(file_path, sep=’\t’)

# Perform necessary preprocessing here, such as handling missing values, data transformation, etc.

df.dropna(inplace=True)

return df

# Example usage:

# df = load_genomics_data(‘path/to/1000Genomes.tsv’)

7. Image Processing: CelebA Dataset

import pandas as pd

import os

from PIL import Image

import numpy as np

def load_celeba(images_path, attributes_path):

attributes = pd.read_csv(attributes_path, delim_whitespace=True)

image_files = os.listdir(images_path)

images = []

for img_file in image_files:

img_path = os.path.join(images_path, img_file)

image = Image.open(img_path)

image = np.array(image)

images.append(image)

images = np.array(images)

return images, attributes

# Example usage:

# images, attributes = load_celeba(‘path/to/images/’, ‘path/to/list_attr_celeba.txt’)

8. Reinforcement Learning: OpenAI Gym (e.g., CartPole)

import gym

def load_cartpole_env():

env = gym.make(‘CartPole-v1’)

return env

# Example usage:

# env = load_cartpole_env()

# env.reset()

# for _ in range(1000):

#     env.render()

#     action = env.action_space.sample()

#     env.step(action)

# env.close()

9. Sentiment Analysis: Sentiment140

import pandas as pd

def load_sentiment140(file_path):

df = pd.read_csv(file_path, encoding=’latin1′, header=None)

df.columns = [‘polarity’, ‘id’, ‘date’, ‘query’, ‘user’, ‘text’]

# Perform necessary preprocessing here, such as removing unnecessary columns, handling text, etc.

df = df[[‘polarity’, ‘text’]]

return df

# Example usage:

# df = load_sentiment140(‘path/to/Sentiment140.csv’)

10. Environmental Data: NOAA Climate Data

import pandas as pd

def load_noaa_climate_data(file_path):

df = pd.read_csv(file_path)

# Perform necessary preprocessing here, such as handling missing values, data transformation, etc.

df.dropna(inplace=True)

return df

# Example usage:

# df = load_noaa_climate_data(‘path/to/NOAA_Climate_Data.csv’)

Appropriate for diverse DL, ML, and AI algorithms, several important functions are listed out by us, along with sample codes. To carry out data loading and preprocessing procedures in various domains, we recommended different functions.

We are the perfect ally for all your python assignment requirements. Our commitment is to offer round-the-clock support to address any academic uncertainties you may have. Our team consists of over 100+  highly regarded Python specialists, selected through rigorous research and quality assessment.Drop us a mail for more details.