This project is part of my experience at Prodigy InfoTech, where I focused on analyzing and visualizing sentiment patterns in public feedback related to various games. The goal was to understand public opinion and attitudes toward different gaming titles by leveraging Python's powerful data analysis libraries.
The analysis was conducted using the following libraries:
- Pandas: For data manipulation and cleaning.
- Seaborn & Matplotlib: For data visualization.
- NumPy: For numerical operations and handling missing data.
- Data Cleaning: Filled null values to ensure a robust analysis.
- Sentiment Analysis: Identified and categorized public feedback into positive and negative sentiments.
- Pattern Extraction: Analyzed overall sentiment trends, focusing on games with the most positive and negative feedback.
- Visualization: Created visual representations of the sentiment patterns to easily convey insights.
- Most Positive Sentiments: Analyzed to determine which games are favored by the public.
- Most Negative Sentiments: Identified "MaddenNFL" as the game with the highest negative sentiment, providing key insights into public dissatisfaction.
- Python
- Pandas
- Seaborn
- Matplotlib
- NumPy
- Clone this repository.
- Install the required libraries
- Run the Jupyter notebook to explore the analysis and visualize the results.
The results offer a comprehensive look at public sentiment across various games, highlighting key trends and insights that can be useful for game developers, marketers, and researchers.
Feel free to explore the notebook, and let me know if you have any questions or suggestions!