Python Projects Source Code

Certainly! I can provide you with a brief overview of each project and the source code for both. However, please note that due to space limitations, I'll provide a simplified version of the code along with explanations. You may need to adapt and expand upon it according to your specific requirements. ### Project 1: Web Scraping Script **Description:** This Python script is designed for web scraping. It uses the BeautifulSoup library to parse HTML and extract specific data from websites. The scraped data is then stored in a structured format, such as a CSV file. **Dependencies:** - BeautifulSoup: Used for parsing HTML. - requests: Used for making HTTP requests to fetch web pages. Here's the code for the web scraping script: ```python import requests from bs4 import BeautifulSoup # Define the URL of the website you want to scrape url = 'https://example.com' # Send an HTTP GET request to the URL response = requests.get(url) # Check if the request was successful if response.status_code == 200: # Parse the HTML content of the page soup = BeautifulSoup(response.text, 'html.parser') # Extract specific data from the page # Example: Scraping all the links on the page links = [] for link in soup.find_all('a'): links.append(link.get('href')) # Store the scraped data in a structured format (e.g., CSV) with open('scraped_data.csv', 'w') as csv_file: for link in links: csv_file.write(link + '\n') else: print('Failed to retrieve the web page.') ``` **Explanation:** 1. We import the necessary libraries. 2. Define the URL of the website you want to scrape. 3. Send an HTTP GET request to the URL. 4. Check if the request was successful (HTTP status code 200). 5. Parse the HTML content of the page using BeautifulSoup. 6. Extract specific data from the page (in this case, we scrape all the links). 7. Store the scraped data in a structured format (e.g., CSV). ### Project 2: Sentiment Analysis with Machine Learning **Description:** This Python project involves building a sentiment analysis model using machine learning. The model analyzes text data to determine the sentiment of the text as positive, negative, or neutral. **Dependencies:** - scikit-learn: Used for machine learning tasks. - pandas: Used for data manipulation and analysis. Here's a simplified code for sentiment analysis: ```python import pandas as pd from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.naive_bayes import MultinomialNB from sklearn.metrics import accuracy_score # Load a dataset with text and corresponding sentiment labels (positive, negative, neutral) # Replace 'dataset.csv' with your dataset data = pd.read_csv('dataset.csv') # Split the dataset into training and testing sets X_train, X_test, y_train, y_test = train_test_split(data['text'], data['sentiment'], test_size=0.2, random_state=42) # Create a TF-IDF vectorizer to convert text data to numerical features tfidf_vectorizer = TfidfVectorizer() X_train_tfidf = tfidf_vectorizer.fit_transform(X_train) X_test_tfidf = tfidf_vectorizer.transform(X_test) # Train a machine learning model (e.g., Naive Bayes) model = MultinomialNB() model.fit(X_train_tfidf, y_train) # Make predictions on the test data y_pred = model.predict(X_test_tfidf) # Evaluate the model's accuracy accuracy = accuracy_score(y_test, y_pred) print(f'Accuracy: {accuracy * 100:.2f}%') ``` **Explanation:** 1. Import the necessary libraries. 2. Load a dataset containing text data and corresponding sentiment labels. 3. Split the dataset into training and testing sets. 4. Create a TF-IDF vectorizer to convert text data into numerical features. 5. Train a machine learning model (Naive Bayes in this example) on the TF-IDF transformed training data. 6. Make predictions on the test data. 7. Evaluate the model's accuracy. Please replace 'dataset.csv' with your actual dataset containing text and sentiment labels. You can further enhance the model by using more advanced techniques and hyperparameter tuning based on your specific needs.