Update README.md

README.md

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----
-license: mit
----
+---
+license: apache-2.0
+language:
+- en
+tags:
+- social
+- analytic
+- x-analytics
+- engagement-prediction
+- twitter
+pretty_name: The AI Thread Engagement Predictor
+size_categories:
+- n<1K
+datasets:
+  - ai-thread-engagement-rate
+---
+
+# AI Thread Engagement Rate Predictor Dataset
+
+This dataset contains a real-world, manually collected sample of **14 threads** posted on X (formerly Twitter) under [this account](https://x.com/PulkitSahu89/status/1833014886776832314) between **September 2024 and January 2025**.
+
+Despite its small size, it is an authentic dataset with real engagement metrics, making it ideal for small-scale experiments, educational purposes, and exploratory analysis of how post features influence engagement.
+
+---
+
+## 📌 Purpose
+
+The dataset is designed to help answer:
+
+**Can we predict a thread's engagement rate based on its content, structure, and other posting attributes?**
+
+**Engagement Rate** is defined by X as:
+
+> The total number of times a user has interacted with a post. This includes all clicks (hashtags, links, usernames, post expansions), reposts, replies, follows, and likes.
+
+---
+
+## 🛠️ Collection Methodology
+
+- **Data Source:**  
+  Metrics were collected using **X Post Analytics**, tracking user engagement, impressions, and other relevant metrics.
+  
+- **Readability Analysis:**  
+  **Grammarly's data** was used to compute the Flesch Reading Ease score and other textual analysis metrics.
+
+---
+
+## 📊 Features Captured
+
+The dataset includes the following columns:
+
+| Column                | Description                                                                  |
+|----------------------|------------------------------------------------------------------------------|
+| **id**                | Unique identifier for each thread                                            |
+| **word_count**        | Total number of words in each thread                                         |
+| **reading_time(s)**   | Estimated reading time (in seconds)                                          |
+| **readability_score** | Flesch Reading Ease score (higher = easier to read)                          |
+| **posts_per_thread**  | Number of posts within each thread                                           |
+| **topic_complexity**  | Subjective rating of the thread’s topic complexity                           |
+| **media_count**       | Number of media elements (images, videos, quizzes) per thread                |
+| **posting_time**      | Time when the thread was posted (in IST)                                     |
+| **post_frequency**    | Number of posts made by the account in a week                                |
+| **impressions**       | Number of times the thread was viewed                                        |
+| **emojis**            | Number of emojis used within the thread                                      |
+| **engagements**       | Total user engagements (likes, comments, reposts, follows, etc.)             |
+
+**CSV Header Row:**
+id word_count reading_time(s) readability_score posts_per_thread topic_complexity media_count posting_time post_frequency impressions emojis engagements
+
+
+---
+
+## 🔄 Data Cleaning & Transformation
+
+- Basic data cleaning steps were applied.
+- Consistency checks ensured no missing or corrupted values.
+- Readability scores were normalized, numeric features standardized where necessary.
+
+---
+
+## 📓 Additional Resources
+
+A **Jupyter Notebook** is available demonstrating:
+- Exploratory data analysis (EDA)
+- A simple neural network model built to predict engagement rate.
+
+👉 **[Kaggle Notebook Link](https://www.kaggle.com/code/pulkitsahu89/simple-neural-network)**
+
+---
+
+## 🔍 Potential Use Cases
+
+- Investigate the relationship between post characteristics (e.g., content length, readability, media usage) and engagement.
+- Build machine learning models to predict engagement rate.
+- Study how readability, timing, and media inclusion affect post performance.
+- Experiment with small, real-world datasets for educational purposes.
+
+---
+
+## 📄 License
+
+- **License:** Apache 2.0
+- **Usage:** Publicly available for research and educational purposes.
+- **Commercial Use:** Not permitted unless explicitly allowed under the license terms.
+
+---
+
+## 📢 Source
+
+- **Data Source:** X Analytics
+- **Account:** [PulkitSahu89](https://x.com/PulkitSahu89)
+
+---
+
+