This project demonstrates the use of machine learning techniques to analyze and classify data. A Decision Tree Classifier is applied to the dataset to predict specific outcomes based on the input features.
The purpose of this project is to apply machine learning algorithms to classify data. Specifically, we are using a Decision Tree Classifier to build a predictive model. The dataset used in this project is processed to extract relevant features and is split into training and testing sets.
The dataset is loaded from a CSV file containing information about various parameters:
- Date and Time
- Month
- Day Fraction
- Individual ID
- Altitude
The following Python libraries and technologies were utilized in the project:
- pandas: For data manipulation and analysis.
- numpy: For numerical operations.
- scikit-learn: For machine learning algorithms and model evaluation.
- Jupyter Notebook: For interactive coding and experimentation.
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Clone the repository to your local machine:
git clone https://github.com/your-username/ml-mini-project.git
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Navigate to the project directory:
cd ml-mini-project -
Install the required Python packages:
pip install -r requirements.txt
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Open the Jupyter Notebook:
jupyter notebook ML_Mini_project.ipynb
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Run the notebook to:
- Load and preprocess the dataset
- Split the data into training and testing sets
- Train the Decision Tree Classifier
- Evaluate the model performance
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The code for training the model and calculating accuracy can be found in the notebook.
The model's performance is evaluated using the accuracy score from the scikit-learn library. The accuracy indicates how well the model predicts the target variable based on the features.
Feel free to contribute to this project by submitting a pull request or opening an issue for discussion.
This project is licensed under the MIT License. See the LICENSE file for more details.