Exploratory Data Analysis (EDA) using SQL — Instagram Dataset

Project Overview

This project demonstrates Exploratory Data Analysis (EDA) using PostgreSQL (pgAdmin4) on a real-world Instagram engagement dataset.
The goal is to uncover insights about post performance, audience engagement, and growth metrics — all by leveraging pure SQL queries instead of Python or visualization tools.

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Objectives

• Perform data exploration and cleaning in PostgreSQL.
• Analyze engagement metrics such as likes, saves, impressions, and follows.
• Detect trends and outliers to understand user interaction patterns.
• Strengthen SQL-based analytical thinking for data-driven storytelling.

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Dataset Description

File name: Instagram-data-cleaned.csv

Column Name	Description
Date	Date when the post was published
Impressions	Number of times the post was seen
From_Home	Impressions generated from home feed
From_Hashtags	Impressions generated from hashtags
From_Explore	Impressions from the Explore section
From_Other	Other impressions (e.g., shares, tags)
Saves	Number of times the post was saved
Comments	Number of comments on the post
Shares	Number of times the post was shared
Likes	Total likes received
Profile_Visits	Number of profile visits from the post
Follows	Number of followers gained from the post
Conversion_Rate	Follows as a percentage of profile visits
Caption	Text caption of the post
Hashtags	Hashtags used in the post

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Database Setup

Create the table

CREATE TABLE instagram_data (
    Date DATE,
    Impressions INT,
    From_Home INT,
    From_Hashtags INT,
    From_Explore INT,
    From_Other INT,
    Saves INT,
    Comments INT,
    Shares INT,
    Likes INT,
    Profile_Visits INT,
    Follows INT,
    Conversion_Rate FLOAT,
    Caption TEXT,
    Hashtags TEXT
);