Instagram Reach Analysis

Objective:The goal of this project is to analyze Instagram engagement metrics to identify trends, detect anomalies, and derive actionable insights that can improve social media performance. Dataset Description:The dataset consists of 119 Instagram posts with 13 columns containing various engagement metrics, such as: Impressions (Total reach of a post) From Explore (Views from the Explore page) Follows (New followers gained from a post) Likes, Comments, Shares, Saves (User interactions) Captions and Hashtags (Text-based engagement factors) Key Challenges & Solutions: Outliers Detection & Correction: Identified outliers in numerical metrics using the IQR method. Applied capping to bring extreme values within a reasonable range to ensure fair analysis. Data Cleaning & Preprocessing: Checked and handled missing values. Standardized numerical variables for better comparisons. Exploratory Data Analysis (EDA): Boxplots to visualize outliers before and after correction. Histograms and KDE plots to analyze the distribution of engagement metrics. Correlation heatmaps to understand relationships between different metrics. Feature Engineering: Created an Engagement Rate metric using interactions and impressions. Identified the impact of captions and hashtags on engagement. Predictive Analysis & Insights: Used regression models to predict post reach based on engagement metrics. Built visualizations to identify key trends in high-performing posts. Expected Outcomes: Understanding which factors contribute most to post engagement. Recommendations for content optimization based on data-driven insights. Improved strategy for increasing reach and interactions on Instagram posts.