Olympic Medal Prediction with Machine Learning

📌 About the Project

This project aims to predict the number of medals a country will win in the Olympics using machine learning techniques. The model is trained on historical Olympic data, considering factors such as economic indicators, population, and past performances.

📊 Technologies Used

• Python – Core programming language
• Pandas & NumPy – Data preprocessing and manipulation
• Scikit-Learn – Machine learning model development
• Matplotlib & Seaborn – Data visualization
• Jupyter Notebook – Experimentation and analysis

🔍 Project Features

This project includes the following steps:

• Data Collection & Preprocessing: Cleaning and structuring historical Olympic datasets
• Exploratory Data Analysis (EDA): Understanding the relationship between different factors and medal counts
• Feature Engineering: Selecting and transforming relevant features for model training
• Model Training & Evaluation: Implementing various machine learning models to predict medal counts
• Hyperparameter Tuning: Optimizing the performance of selected models
• Prediction & Insights: Analyzing model outputs and key influencing factors

📂 Project Structure

• data/ → Contains raw and processed datasets
• notebooks/ → Jupyter Notebooks for analysis and experimentation
• models/ → Saved trained models
• scripts/ → Python scripts for data processing and training
• README.md → Project documentation and description

🚀 Installation & Usage

1. Clone the Repository:

   git clone https://github.com/Utkuersy/Predict_Olyimpics_Medal_With_Machine_Learning.git

2. Install Dependencies:

   pip install -r requirements.txt

3. Run Jupyter Notebook (for exploration and training):

   jupyter notebook

4. Execute Python Scripts (for automated processing and training):

   python scripts/train_model.py