Refactor README formatting and content

Updated formatting and removed emojis for consistency.

Author: gnevercodes

README.md

@@ -1,29 +1,29 @@
-# 🎬 Personalized Movie Recommendation System using PySpark & Collaborative Filtering
+#  Personalized Movie Recommendation System using PySpark & Collaborative Filtering
 
-> 📌 Project 1 of 6 | Pushed as part of my academic + real-world ML portfolio 🚀
+>  Project 1 of 6 | Pushed as part of my academic + real-world ML portfolio 
 
-## 🧠 Overview
+##  Overview
 In the ever-growing jungle of streaming content, users often get lost in endless scrolls and mediocre suggestions. Our project dives into solving this problem by building a **personalized movie recommendation system** powered by **collaborative filtering** and **Apache Spark**, capable of processing massive datasets and giving spot-on suggestions based on user behavior.
 
-## 📈 Key Features
-- 💡 Personalized suggestions based on **user-item interaction**
-- ⚡ Built with **PySpark** on **Apache Spark** for large-scale performance
-- 🧪 Evaluated using **RMSE**, **precision**, and **recall**
-- 🤝 Scalable, fast, and adaptable to various streaming platforms
-- 🔒 Acknowledges **bias and privacy** issues in recommender systems
+##  Key Features
+-  Personalized suggestions based on **user-item interaction**
+-  Built with **PySpark** on **Apache Spark** for large-scale performance
+-  Evaluated using **RMSE**, **precision**, and **recall**
+-  Scalable, fast, and adaptable to various streaming platforms
+-  Acknowledges **bias and privacy** issues in recommender systems
 
-## 🛠️ Tech Stack
+##  Tech Stack
 - **Language**: Python
 - **Frameworks**: PySpark, Apache Hadoop (HDFS)
 - **Tools**: MLlib, Jupyter, VS Code
 - **Algorithm**: User-based Collaborative Filtering
 
-## 📂 Dataset
+##  Dataset
 - Contains over **8,000+** user interactions and movie ratings
 - Publicly sourced, includes diverse genres, languages, and release years
 - Preprocessing steps include handling nulls, normalization, and outlier removal
 
-## 📊 Dataset
+##  Dataset
 
 This project uses the ([https://www.kaggle.com/datasets/grouplens/movielens-20m-dataset](https://www.kaggle.com/datasets/arzubesiroglu/netflix-titles)) which contains millions of user-movie interactions, ratings, and metadata.
 
@@ -35,7 +35,7 @@ To use the full dataset:
 3. Place it in the root directory or update the path in the code accordingly
 
 
-## 📊 Results
+##  Results
 - Achieved **RMSE = 3.7725** on our baseline implementation
 - Compared with benchmark paper achieving **RMSE = 1.0742**
 - Insights into how **parameter tuning** (lambda, iterations, rank) affects performance
@@ -49,29 +49,29 @@ We’ve drawn inspiration and technical strategies from key works including:
 
 _For the full IEEE-style paper, check the documenation folder in this repo :) 
 
-## 🧠 Authors & Credits
-Built with ❤️ by a team of graduate students as part of our coursework under the guidance of our incredible supervisor (see acknowledgments in paper). Shoutout to all contributors and cited researchers!
+##  Authors & Credits
+Built by a team of graduate students as part of our coursework under the guidance of our incredible supervisor (see acknowledgments in paper). Shoutout to all contributors and cited researchers!
 
-## 📌 Future Work
-- 🧠 Incorporating **hybrid models** (content + collaborative)
-- 🔒 Introducing **privacy-preserving mechanisms**
-- 🎯 Deploying the system on a cloud platform for live inference
+##  Future Work
+-  Incorporating **hybrid models** (content + collaborative)
+-  Introducing **privacy-preserving mechanisms**
+-  Deploying the system on a cloud platform for live inference
 
-## 📎 License
+##  License
 feel free to fork, star, and remix with credit!
 
 ## 📁 Project Structure
 
-📦 PySparkFlicks_MovieRecommender/
+ PySparkFlicks_MovieRecommender/
 ```
-|---🧠 code/                  → PySpark code and scripts
-├── 📒 notebooks/             → Jupyter Notebooks for exploration
-├── 📊 data/                  → Sample Netflix dataset
-├── 📄 documentation/         → IEEE paper, diagrams, and references
-├── ⚙️ .github/workflows/     → CI/CD workflows (Python)
-├── 📦 requirements.txt       → Python dependencies
-├── 🛠️ setup.py               → Installable package setup (optional)
-├── 📘 README.md              → This very file
-└── 🧾 LICENSE                → Open-source license
+|--- code/                  → PySpark code and scripts
+├──  notebooks/             → Jupyter Notebooks for exploration
+├──  data/                  → Sample Netflix dataset
+├──  documentation/         → IEEE paper, diagrams, and references
+├──  .github/workflows/     → CI/CD workflows (Python)
+├──  requirements.txt       → Python dependencies
+├──  setup.py               → Installable package setup (optional)
+├──  README.md              → This very file
+└──  LICENSE                → Open-source license
 ```