As a data analyst, I conducted an in-depth analysis of top influencers on Instagram. Through rigorous data cleaning processes and the use of advanced analysis matrices, I was able to study their strategies and present my findings in a comprehensive dashboard. This project showcases my expertise in data analysis and my ability to derive valuable insights from complex data sets

Instagram is a photo and video sharing social networking service founded in 2010 by Kevin Systrom and Mike Krieger, and later acquired by American company Facebook Inc. The app allows users to upload media that can be edited with filters and organized by hashtags and geographical tagging. Posts can be shared publicly or with preapproved followers. Users can browse other users' content by tag and location, view trending content, like photos, and follow other users to add their content to a personal feed.

Instagram was originally distinguished by allowing content to be framed only in a square (1:1) aspect ratio of 640 pixels to match the display width of the iPhone at the time. In 2015, this restrictions was eased with an increase to 1080 pixels. It also added messaging features, the ability to include multiple images or videos in a single post, and a Stories feature—similar to its main competitor Snapchat—which allowed users to post their content to a sequential feed, with each post accessible to others for 24 hours. As of January 2019, Stories is used by 500 million people daily.

This dataset comprises of 200 top influencers profile data of instagram

The Top Instagram Accounts Dataset is a collection of 200 rows of data that provides valuable insights into the most popular Instagram accounts across different categories. The dataset contains several columns that provide comprehensive information on each account's performance, engagement rate, and audience size.

1. The "rank": column lists the accounts in order of their popularity on Instagram, starting from the most followed account.

2. The "name": column displays the Instagram handle of the account, which can be used to locate and follow the account on Instagram.

3. The "channel_info": column provides a brief description of the account, such as the type of content it features or the products and services it offers.

4. The "Category": column categorizes the account based on its primary theme or subject matter, such as fashion, sports, entertainment, or food.

5. The "posts": column displays the total number of posts on the account. This column helps to understand the account's level of activity and the amount of content it has produced over time.

6. The "followers": column indicates the number of people who follow the account on Instagram.

7. The "avg likes": column displays the average number of likes that the account's posts receive per post.

8. The "eng rate": column calculates the account's engagement rate by dividing the total number of likes and comments received by the total number of followers, expressed as a percentage.

How you can use this Dataset?

The Top Instagram Accounts Dataset can be used in a variety of ways to gain insights into the performance and engagement levels of popular Instagram accounts. Here are a few examples of what you can do with this dataset:

1. Conduct category analysis: The dataset provides information on the category of each Instagram account. You can use this information to conduct a category analysis and identify the most popular categories on Instagram.

2. Identify top influencers: The dataset ranks Instagram accounts based on their follower count. You can use this information to identify the top influencers in different categories and use them for influencer marketing campaigns.

3. Analyze engagement levels: The dataset includes columns such as "avg likes" and "eng rate" that provide insights into the engagement levels of Instagram accounts. You can use this information to understand what type of content resonates with Instagram users and create more engaging content for your own account.

This is a Data analysis Project. I used Excel to clean up the messy raw data about the "Top 100 Instagram Influencers" and turn it into a neat, easy-to-read table. Then, I dove into the data on the Top 100 Instagram influencers, using Excel to uncover interesting insights about which countries were leading in terms of engagement and reach. I made sense of it all by setting up Pivot tables to connect the dots and bring together the most important information. To make sure everyone could understand the findings, I got creative and visualized the data, creating a dynamic dashboard that made it easy to see what was going on at a glance.

Instagram[a] is a photo and video sharing social networking service founded in 2010 by Kevin Systrom and Mike Krieger, and later acquired by American company Facebook Inc. The app allows users to upload media that can be edited with filters and organized by hashtags and geographical tagging. Posts can be shared publicly or with preapproved followers. Users can browse other users' content by tag and location, view trending content, like photos, and follow other users to add their content to a personal feed.

Instagram was originally distinguished by allowing content to be framed only in a square (1:1) aspect ratio of 640 pixels to match the display width of the iPhone at the time. In 2015, this restrictions was eased with an increase to 1080 pixels. It also added messaging features, the ability to include multiple images or videos in a single post, and a Stories feature—similar to its main competitor Snapchat—which allowed users to post their content to a sequential feed, with each post accessible to others for 24 hours. As of January 2019, Stories is used by 500 million people daily.

This dataset comprises of the details of top 1000 influencers in instagram

This dataset captures the pulse of viral social media trends across TikTok, Instagram, Twitter, and YouTube. It provides insights into the most popular hashtags, content types, and user engagement levels, offering a comprehensive view of how trends unfold across platforms. With regional data and influencer-driven content, this dataset is perfect for:

• Trend analysis 🔍
• Sentiment modeling 💭
• Understanding influencer marketing 📈

Dive in to explore what makes content go viral, the behaviors that drive engagement, and how trends evolve on a global scale! 🌍

Description: The Marketing Campaign Performance Dataset provides valuable insights into the effectiveness of various marketing campaigns. This dataset captures the performance metrics, target audience, duration, channels used, and other essential factors that contribute to the success of marketing initiatives. With 200000 unique rows of data spanning two years, this dataset offers a comprehensive view of campaign performance across diverse companies and customer segments.

Columns: Company: The company responsible for the campaign, representing a mix of fictional brands. Campaign_Type: The type of campaign employed, including email, social media, influencer, display, or search. Target_Audience: The specific audience segment targeted by the campaign, such as women aged 25-34, men aged 18-24, or all age groups. Duration: The duration of the campaign, expressed in days. Channels_Used: The channels utilized to promote the campaign, which may include email, social media platforms, YouTube, websites, or Google Ads. Conversion_Rate: The percentage of leads or impressions that converted into desired actions, indicating campaign effectiveness. Acquisition_Cost: The cost incurred by the company to acquire customers, presented in monetary format. ROI: Return on Investment, representing the profitability and success of the campaign. Location: The geographical location where the campaign was conducted, encompassing major cities like New York, Los Angeles, Chicago, Houston, or Miami. Language: The language used in the campaign communication, including English, Spanish, French, German, or Mandarin. Clicks: The number of clicks generated by the campaign, indicating user engagement. Impressions: The total number of times the campaign was displayed or viewed by the target audience. Engagement_Score: A score ranging from 1 to 10 that measures the level of engagement generated by the campaign. Customer_Segment: The specific customer segment or audience category that the campaign was tailored for, such as tech enthusiasts, fashionistas, health and wellness enthusiasts, foodies, or outdoor adventurers. Date: The date on which the campaign occurred, providing a chronological perspective to analyze trends and patterns.

Scope: By leveraging this dataset, marketers and data analysts can uncover valuable insights regarding campaign performance, audience preferences, channel effectiveness, and ROI. This dataset serves as a valuable resource for market research, campaign optimization, and data-driven decision-making, enabling businesses to refine their marketing strategies and drive targeted growth.

**Note:** This is a fictional dataset.