LinkedIn Post Analytics Project, which investigates the characteristics of successful LinkedIn posts. The project spans data cleaning, exploratory data analysis (EDA), and predictive modeling to understand what drives engagement on LinkedIn posts. Using Python, Tableau, and insights from a comprehensive dataset obtained from Kaggle, this project aims to provide actionable recommendations for creating high-engagement LinkedIn content.
LinkedIn_Data_Cleaning.ipynb: Jupyter Notebook for initial data cleaning and preprocessing. This notebook outlines the steps taken to prepare the dataset for analysis, including handling missing values and removing irrelevant columns.
LinkedIn_Posts-Cleaned.csv: The cleaned and processed dataset resulting from the initial data cleaning step. This file contains LinkedIn post data ready for analysis.
Dashboard.twb: A Tableau workbook file that contains visualizations of the LinkedIn post data. This dashboard provides insights into the engagement metrics of posts based on various factors such as media type, post timing, and author's follower count.
Project_Report.pdf: A detailed report of the project findings, including methodology, results, discussion, and conclusions. This document provides a comprehensive overview of the project's objectives, data description, analysis performed, and insights gained.
LinkedInPostAnalytics.pptx: A presentation summarizing the project's key findings and recommendations. This PowerPoint presentation is designed to communicate the results of the analysis to a non-technical audience.
The project uncovered several key insights into what makes a LinkedIn post successful:
Media Type Impact: Visual content, including images and videos, tends to receive more engagement compared to text-only posts.
Influencer Analysis: The distribution of followers among LinkedIn influencers is diverse, with top influencers garnering significantly more reactions, particularly on posts containing media.
Engagement Patterns: The analysis revealed that the timing of posts and the number of followers and connections of the author can influence the engagement a post receives.
Predictive Modeling: Using Python, predictive models were developed to forecast the number of reactions based on historical data, media type, and the author's network size.
Based on the analysis, the project recommends:
Incorporating Visuals: To enhance engagement, include images or videos in your posts.
Optimizing Post Timing: Publish content when your target audience is most active to maximize visibility and interaction.
Building a Network: Expand your follower and connection base to increase the reach and impact of your posts.
Analyzing Top Performers: Study the posts from top influencers to identify successful strategies and trends that could be applied to your content.