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https://github.com/7irelo/regressor-model
https://github.com/7irelo/regressor-model
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/7irelo/regressor-model
- Owner: 7irelo
- License: mit
- Created: 2024-08-19T11:08:15.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-08-19T15:28:29.000Z (5 months ago)
- Last Synced: 2024-08-20T13:17:34.757Z (5 months ago)
- Language: Jupyter Notebook
- Size: 1.2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Video Game Sales Prediction using Decision Tree Regression
This project implements a machine learning model using a Decision Tree Regressor to predict global video game sales based on various regional sales data and the release year. The dataset used is `vgsales.csv`, which contains information about video game sales across different regions, including North America, Europe, Japan, and others.
## Table of Contents
- [Installation](#installation)
- [Dataset](#dataset)
- [Model](#model)
- [Usage](#usage)
- [Model Evaluation](#model-evaluation)
- [Visualization](#visualization)
- [Saving and Loading the Model](#saving-and-loading-the-model)
- [Future Enhancements](#future-enhancements)
- [License](#license)
## Installation
To get started, clone this repository to your local machine:
```bash
git clone https://github.com/7irelo/regressor_model.git
```
Install the necessary Python libraries using pip:
```bash
pip install pandas numpy matplotlib scikit-learn joblib
```
## Dataset
The dataset used in this project is `vgsales.csv`. The dataset includes the following columns:
- **Year**: The release year of the video game.
- **NA_Sales**: Sales in North America (in millions).
- **EU_Sales**: Sales in Europe (in millions).
- **JP_Sales**: Sales in Japan (in millions).
- **Other_Sales**: Sales in other regions (in millions).
- **Global_Sales**: Total global sales (in millions).
Make sure the dataset is placed in the correct path as specified in the script.
## Model
The model is built using the `DecisionTreeRegressor` from scikit-learn. It predicts global video game sales based on the following features:
- `Year`
- `NA_Sales`
- `EU_Sales`
- `JP_Sales`
- `Other_Sales`
## Usage
1. **Preprocessing**: The dataset is preprocessed by dropping any rows with missing values.
2. **Feature Selection**: We select relevant features for training the model.
3. **Model Training**: The data is split into training and testing sets, and the `DecisionTreeRegressor` is trained on the training data.
4. **Model Evaluation**: The performance of the model is evaluated using Mean Squared Error (MSE).
5. **Visualization**: The trained decision tree is visualized using Matplotlib.
To run the code, simply execute the script:
```bash
python main.py
```
## Model Evaluation
The model's performance is evaluated using the Mean Squared Error (MSE) metric, which calculates the average squared difference between the predicted and actual values:
```python
Mean Squared Error: [MSE Value]
```
## Visualization
The trained decision tree is visualized using the `plot_tree` function from scikit-learn, which provides a graphical representation of the tree.
```python
plt.figure(figsize=(20,10))
tree.plot_tree(regressor, filled=True)
plt.show()
```
## Saving and Loading the Model
The trained model is saved using `joblib` for future use:
```python
joblib.dump(regressor, 'regressor_model.pkl')
```
You can load the saved model and use it for predictions:
```python
loaded_model = joblib.load('regressor_model.pkl')
new_predictions = loaded_model.predict(X_test)
```
## Future Enhancements
- Implement more advanced regression techniques such as Random Forest or Gradient Boosting.
- Perform hyperparameter tuning for better model performance.
- Explore feature engineering and scaling to improve the model's accuracy.
- Handle missing data more robustly, possibly using imputation techniques.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.
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