This Python script processes text data and generates a word cloud visualization of the most frequent words from a cleaned dataset. It begins by importing necessary libraries: pandas for data handling, matplotlib.pyplot for plotting, and WordCloud for creating the word cloud. The script loads a cleaned CSV file (cleaned_data.csv) into a pandas DataFrame and combines all the text from the cleaned_text column into a single string. This string is then used to generate the word cloud, which is displayed with a white background and customized dimensions. To use the script, ensure you have the required libraries (pandas, matplotlib, and wordcloud) installed, place the CSV file in the correct directory, and run the script. The word cloud will be displayed, showing the most common words in the dataset.

This Python script processes and visualizes textual data by generating a word cloud from a cleaned dataset. It starts by importing essential libraries: pandas for data manipulation, matplotlib.pyplot for plotting the word cloud, and WordCloud for creating the visualization. The script loads the cleaned CSV file (cleaned_data.csv) into a pandas DataFrame, which contains a column named cleaned_text with preprocessed text data. All the text from this column is concatenated into one large string, which is then used to generate the word cloud, highlighting the most frequently occurring words. The word cloud is displayed with a white background and set dimensions of 800x400 pixels. Users can modify parameters like word cloud size, background color, and other visual settings. To run the script, ensure that the necessary libraries (pandas, matplotlib, and wordcloud) are installed, the CSV file is correctly placed in the expected directory, and then execute the script. The resulting word cloud provides a visual representation of the most common terms in the dataset, making it a useful tool for text analysis and pattern recognition in large text-based datasets.