Instagram Reach Analysis using ML

Instagram Reach Analysis using ML

Pruthviraj Gajanan Agawane, Tanmay Ramesh Deshmukh, Siddhi Bhalchandra Talekar, Ms. Susmitha Mandava

Department of Computer Science & Design

New Horizon Institute of Technology and Management, Thane

Abstract— The growing importance of data-driven decision-making in social media marketing necessitates sophisticated analytical methodologies to optimize content performance. This study presents a Python-powered Instagram reach analysis framework designed to extract, preprocess, and visualize engagement metrics, enabling data-driven content strategies. Utilizing machine learning techniques and statistical modeling, the system identifies key factors influencing reach, such as hashtags, posting time, and content format. The framework leverages Python libraries like Pandas and NumPy for data manipulation, Matplotlib and Seaborn for visualization, and exploratory data analysis (EDA) techniques to uncover engagement trends. An intuitive graphical interface facilitates seamless data import from Instagram insights, offering real-time analytical capabilities for marketers, businesses, and content creators. By integrating automation and predictive analytics, this initiative provides actionable insights to refine posting strategies, enhance audience engagement, and drive content virality, ultimately fostering a strategic and measurable approach to Instagram marketing

Keywords— Social Media Analytics, Instagram Insights, Data Visualization, Machine Learning, Content Optimization, Engagement Metrics, Reach Analysis, Python Programming, Data Science, Predictive Analytics.