Instagram-Bot-and-Data-Analysis

This project implements an automated bot to interact with Instagram and perform various tasks, alongside content scraping and analysis for actionable insights. Built using Selenium, it offers both automation and data visualization features for social media data.

Features

Notebook 1: Instagram Automation

1. Login to Instagram: Automates logging into your Instagram account.
2. Search Functionality: Searches for specific keywords (e.g., "food") and displays related accounts.
3. Profile Management: Automates opening, following, and unfollowing profiles.
4. Post Interaction: Likes and unlikes posts programmatically.
5. Follower Analysis: Extracts follower lists from specific profiles.
6. Story Viewer: Checks and views stories of specific accounts, handling error scenarios.

Notebook 2: Instagram Data Analysis

1. Account Insights: Identifies top accounts with the highest followers from a search result.
2. Post Frequency Analysis: Counts the number of posts by accounts in the last three days.
3. Content Scraping: Extracts content from posts and identifies popular hashtags.
4. Data Visualization: Creates pie charts for hashtags and bar graphs for follower-to-like ratios.
5. Engagement Metrics: Calculates average likes and engagement ratios for profiles.

Technologies Used

1. Language: Python
2. Automation Framework: Selenium
3. Data Visualization: Matplotlib
4. Web Scraping: Selenium
5. Tools: WebDriver for browser automation

Usage

1. Open Notebook 1 (Insta_bot_1.ipynb) for automation tasks.
2. Open Notebook 2 (InstaBot_2.ipynb) for data scraping and analysis.
3. Run the cells sequentially, following the inline comments for guidance.

Outputs

1. Automation: Seamless interaction with Instagram tasks.
2. Data Analysis:

• CSV file with word frequency data.
• Pie chart for top hashtags.
• Bar graph for engagement metrics.

Conclusion

This project demonstrates the potential of automation and data analytics in leveraging social media platforms like Instagram. By combining Selenium for task automation with Python's data handling and visualization capabilities, it provides a powerful toolkit to automate repetitive tasks and derive insights from social media content.

From simplifying interactions such as logging in, following/unfollowing profiles, and liking posts, to advanced data scraping and analysis for hashtags and engagement metrics, the project highlights practical applications of programming in social media management. It sets the foundation for future enhancements, such as integrating AI-based analytics for sentiment analysis and improving scalability for large datasets.