This project demonstrates the implementation and evaluation of various Machine Learning classification models. It walks through the process of preparing and exploring data, engineering features, and building models to classify data effectively.
The workflow highlights practical skills in working with Machine Learning frameworks and understanding the strengths of different classifiers.
The dataset used in this project includes multiple features that describe entities for classification tasks, such as:
- Categorical Features: Text-based attributes converted to numerical values.
- Numerical Features: Normalized or standardized for better model performance.
- Target Variable: Categorized labels based on thresholds derived from the data.
- Load and preprocess the dataset.
- Handle missing values and inconsistencies.
- Conduct initial exploratory steps to understand data distribution.
- Use visualization techniques (scatter plots, heatmaps, etc.) to identify trends, correlations, and outliers.
- Extract key insights to guide feature selection and model design.
- Transform categorical features into numerical values.
- Generate additional features to improve prediction accuracy.
- Normalize or scale numerical features to suit Machine Learning models.
Train and evaluate the following classification models:
A simple yet powerful probabilistic classifier based on Bayes' theorem. It assumes independence between features, which makes it computationally efficient and effective for many applications, especially text classification.
A tree-structured model where decisions are made at each node based on feature values. It's intuitive and interpretable but prone to overfitting without proper regularization.
An ensemble method that combines multiple decision trees to improve accuracy and reduce overfitting. It achieves better generalization by averaging the predictions of individual trees.
A boosting algorithm that combines weak learners (e.g., shallow decision trees) iteratively to form a strong learner. It assigns higher weights to misclassified samples, focusing on them in subsequent iterations.
An advanced gradient boosting algorithm known for its speed and accuracy. It uses regularization techniques to prevent overfitting and is widely used in competitions like Kaggle.
To assess the performance of classification models, the following metrics are used:
- Confusion Matrix: A summary of prediction results, showing true positives, true negatives, false positives, and false negatives.
- Accuracy: The ratio of correctly predicted observations to the total observations.
- Precision: Measures the proportion of true positives among the predicted positives. High precision indicates fewer false positives.
- Recall (Sensitivity): Measures the proportion of true positives among the actual positives. High recall indicates fewer false negatives.
- F1-Score: The harmonic mean of precision and recall, providing a single metric that balances both.
- Weighted and Macro-Averaged Scores: Used to handle imbalanced datasets by giving appropriate weight to class sizes or averaging metrics across all classes.
These metrics provide a comprehensive view of model performance and are chosen based on the specific requirements of the task.
- Clone the repository:
git clone https://github.com/Amir-rfz/Machine_Learning_Classifiers.git
- Install the required libraries:
pip install -r requirements.txt
- Open the Jupyter Notebook:
jupyter notebook ml_models.ipynb
- Follow the steps in the notebook to run the data analysis and model evaluations.
The project provides detailed comparisons of different classification models, including their strengths, weaknesses, and scenarios where each is most effective. Visualizations and evaluation metrics are used to support the conclusions.
Contributions are welcome! Feel free to fork this repository, submit issues, or open pull requests for enhancements or bug fixes.