Update README.md

Author: Sarathnadiminti

README.md

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-🎥 Top 100 Movies Analysis (Python Project)
-This project dives into an analysis of the top 100 highest-rated movies from the past decade, utilizing Python to uncover interesting insights into movies, actors, voters, ratings, and box-office collections. The analysis is performed in a Jupyter Notebook, making it easy to follow along with the code, visualizations, and findings.
-
-🚀 Project Overview
-This project is a compulsory individual assignment aimed at analyzing movie data to:
-
-Gain insights into movie ratings, collections, and actor performances.
-Explore voter demographics and their impact on ratings.
-Identify patterns and trends using Python libraries.
-🛠️ Key Features
-Data Exploration:
-
-Loading and inspecting the dataset.
-Understanding the data dictionary for clear column meanings.
-Data Cleaning & Preparation:
-
-Handling missing values.
-Correcting data inconsistencies.
-Preparing the dataset for analysis.
-Data Analysis & Insights:
-
-Analyzing the distribution of ratings and collections.
-Finding top-rated actors and movies.
-Investigating voter demographics and their preferences.
-Visualization:
-
-Creating meaningful plots for better interpretation of results.
-Using subplots for comparison where needed.
-Inferences:
-
-Drawing actionable conclusions based on the analysis.
-Highlighting interesting patterns and trends.
-📊 Tech Stack
-Programming Language: Python 🐍
-Libraries Used:
-Pandas for data manipulation.
-Matplotlib and Seaborn for data visualization.
-NumPy for numerical operations.
+# 🎥 Top 100 Movies Analysis (Python Project)
+
+## 🚀 Project Overview
+This project analyzes the top 100 highest-rated movies of the past decade using Python to uncover valuable insights about movies, actors, voters, ratings, and box office collections. The project is performed in a Jupyter Notebook for easy readability, providing code, visualizations, and key findings. 
+
+## 🛠️ Key Features
+
+### Data Exploration:
+- Load and inspect the dataset.
+- Understand the data dictionary for column meanings.
+
+### Data Cleaning & Preparation:
+- Handle missing values.
+- Correct data inconsistencies.
+- Prepare the dataset for analysis.
+
+### Data Analysis & Insights:
+- Analyze the distribution of ratings and collections.
+- Find the top-rated actors and movies.
+- Investigate voter demographics and their preferences.
+
+### Visualization:
+- Create meaningful plots for interpreting the results.
+- Use subplots for effective comparison where needed.
+
+### Inferences:
+- Draw actionable conclusions from the analysis.
+- Highlight interesting patterns and trends in the data.
+
+## 📊 Tech Stack
+- **Programming Language**: Python 🐍
+- **Libraries Used**:
+  - **Pandas** for data manipulation
+  - **Matplotlib** and **Seaborn** for data visualization
+  - **NumPy** for numerical operations
+
+## 📁 Repository Contents
+- **Movie Assignment.ipynb**: The Jupyter Notebook containing the Python code, visualizations, and inferences.
+- **movies_dataset.csv**: The dataset used for analysis (if allowed to include).
+- **README.md**: Documentation for the repository.
+
+## 📈 Insights from the Project
+- Identified trends in ratings and collections over the past decade.
+- Highlighted the most influential actors and their performances.
+- Explored how voter demographics influence movie ratings.
+- Uncovered correlations between box office collections and ratings.
+
+## 🎯 Conclusion
+This project provides an in-depth analysis of the top 100 rated movies over the past decade, uncovering key insights into the movie industry, its ratings, and the influence of actors and voters. Through data exploration, cleaning, and visualization, the project delivers actionable insights and meaningful trends that can guide future decisions in the movie industry.
+
+Feel free to explore the notebook and run the code to dive deeper into the analysis!