🎬 Oscar Awards Analysis (1927–2025)

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📌 About the Project

This project analyzes 98 years of Academy Awards (Oscars) data to uncover trends, biases, and patterns in the film industry. It includes:

• Data Cleaning: Handling missing values, duplicates, and inconsistencies.
• Exploratory Data Analysis (EDA): Visualizations to explore trends in categories, years, and films.
• Machine Learning: Predicting award winners based on nominations, categories, and release years.

Perfect for data analysis portfolios or showcasing skills in Python, Pandas, and Scikit-learn.

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📂 Project Structure

oscar-analysis/
├── data/
│   ├── the_oscar_award.csv      # Raw dataset from Kaggle
│   └── oscar_cleaned.csv        # Cleaned dataset (auto-generated)
├── notebooks/
│   └── oscar_analysis.ipynb     # Jupyter Notebook with full analysis
└── README.md                    # This file

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📊 Dataset

• Source: Kaggle - The Oscar Award (1927–2025)
• Key Columns:
  • year_film: Year the film was released.
  • year_ceremony: Year the ceremony was held.
  • category: Award category (e.g., Best Picture, Best Actor).
  • film: Title of the film.
  • winner: Whether the nominee won (1 = Yes, 0 = No).

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🛠️ Tools & Libraries

Tool	Purpose
Python 3.10+	Programming language
Pandas	Data cleaning and manipulation
NumPy	Numerical operations
Matplotlib	Static visualizations
Seaborn	Statistical visualizations
Scikit-learn	Machine learning models
Jupyter	Interactive notebook environment

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🚀 How to Run the Project

1. Set Up Your Environment

Option A: Using Anaconda (Recommended)

1. Install Anaconda.
2. Open Anaconda Prompt and run:

 conda create -n oscar_env python=3.10 -y
 conda activate oscar_env
 conda install numpy=1.23.5 pandas=1.5.3 matplotlib seaborn scikit-learn jupyter -y

Option B: Using pip

1. Install Python 3.10+ from python.org.
2. Open a terminal and run:

 pip install numpy==1.23.5 pandas==1.5.3 matplotlib seaborn scikit-learn jupyter

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2. Download the Dataset

1. Download the_oscar_award.csv from Kaggle.
2. Save it in the data/ folder of your project.

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3. Run the Jupyter Notebook

1. Navigate to your project directory:

 cd path/to/oscar-analysis

2. Launch Jupyter Notebook:

 jupyter notebook

3. Open notebooks/oscar_analysis.ipynb and run the cells in order.

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📈 Key Findings

Here’s what the analysis reveals:

1. Most Awarded Categories:

• Best Picture, Best Actor, and Best Actress dominate the awards.

2. Trends Over Time:

• The number of awards per year has grown, reflecting the expansion of the film industry.

3. Winner Distribution:

• Only a small percentage of nominees win, highlighting the competitiveness of the Oscars.

4. Predictive Model:

• A Random Forest Classifier predicts winners with ~68% accuracy (replace with your actual result) based on nominations, category, and release year.

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📁 File Descriptions

File/Folder	Description
data/	Contains raw and cleaned datasets.
notebooks/	Jupyter Notebook with all analysis code.
README.md	Project documentation (this file).

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🔧 Troubleshooting

Issue	Solution
FileNotFoundError	Ensure the_oscar_award.csv is in the data/ folder.
Binary Incompatibility	Use compatible versions: numpy=1.23.5, pandas=1.5.3.
Missing Columns	Update the notebook to use the correct column names (e.g., year_film instead of year).
Jupyter Notebook Won’t Open	Ensure Jupyter is installed (pip install jupyter or conda install jupyter).

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📜 License

This project is open-source under the MIT License.

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🙏 Acknowledgments

• Data provided by Kaggle.
• Inspired by the global community of data analysts and film enthusiasts.