Project: Machine Learning Models

This project contains implementations of various machine learning models, focusing on regression and classification tasks. Below is an overview of the files included and their specific purposes.

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Project Structure

1. linearRegression.ipynb

• Description: Implements Linear Regression.
• Key Features:
  • Uses sklearn.linear_model.LinearRegression to model linear relationships.
  • Evaluates performance using R-squared and Mean Squared Error.

• Use Case: Best for regression tasks with linear relationships between variables.”

2. logisticRegression.ipynb

• Description: Implements Logistic Regression.
• Key Features:
  • Uses sklearn.linear_model.LogisticRegression for binary classification.
  • Includes methods for model evaluation such as accuracy, confusion matrix, and ROC curve.

• Use Case: Ideal for classification tasks, especially binary classification (e.g., spam detection, disease prediction).

3. decisiontree.ipynb

• Description: Implements Decision Tree Classification.
• Key Features:
  • Uses sklearn.tree.DecisionTreeClassifier.
  • Demonstrates plotting of decision trees.
  • Includes methods for splitting datasets and visualizing results.
• Use Case: Best suited for classification problems.

4. lasso.ipynb

• Description: Demonstrates Lasso Regression.
• Key Features:
  • Utilizes sklearn.linear_model.Lasso for regression with feature selection.
  • Evaluates model using metrics like R-squared and Mean Squared Error.
• Use Case: Suitable for regression tasks requiring feature regularization.

5. randomforest.ipynb

• Description: Implements Random Forest models.
• Key Features:
  • Covers Random Forest Classification and Regression using sklearn.ensemble.
  • Works with synthetic datasets generated using sklearn.datasets.
• Use Case: Ideal for both classification and regression problems requiring ensemble techniques.

6. ridge.ipynb

• Description: Demonstrates Ridge Regression.
• Key Features:
  • Uses sklearn.linear_model.Ridge for regression with L2 regularization.
  • Analyzes model performance with suitable metrics.
• Use Case: Best for regression problems with multicollinearity.

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Comparison of Models

Model	Strengths	Weaknesses	Best For
Linear Regression	Simple to implement, interpretable results.	Assumes linear relationships, sensitive to outliers.	Regression tasks with linear relationships between variables.
Logistic Regression	Provides probabilities for classification, interpretable.	Assumes linear decision boundary, struggles with large datasets.	Binary classification tasks, e.g., predicting success/failure.
Decision Tree	Easy to interpret and visualize, works for non-linear data.	Prone to overfitting, especially with deep trees.	Classification tasks with clear decision boundaries.
Lasso Regression	Feature selection by reducing irrelevant coefficients.	Can overshrink coefficients, removing useful features.	Regression tasks with high dimensional data.
Random Forest	Handles overfitting better than a single tree, works well with complex data.	Slower to train and predict for large datasets.	Both classification and regression tasks with large datasets.
Ridge Regression	Reduces multicollinearity, prevents overfitting.	Doesn't perform feature selection like Lasso.	Regression tasks where all features are relevant.

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Requirements

To run the notebooks, ensure you have the required libraries installed. Refer to the requirements.txt file for the complete list:

scikit-learn numpy pandas matplotlib

Install them using the following command:

pip install -r requirements.txt

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How to Use

1. Clone the Repository:

   git clone https://github.com/mischieff01/Project-Machine-Learning-Models
   cd Project-Machine-Learning-Models

2. Set Up the Environment: Ensure you have Python installed along with the required libraries. Install dependencies using the requirements.txt file.

3. Explore the Notebooks: Open any notebook (.ipynb file) in Jupyter Notebook or Jupyter Lab:

   jupyter notebook

• Navigate to the specific file based on your interest:
  • Decision Tree: decisiontree.ipynb
  • Lasso Regression: lasso.ipynb
  • Random Forest: randomforest.ipynb
  • Ridge Regression: ridge.ipynb

4. Run the Notebooks: Follow the code cells sequentially. Each notebook includes step-by-step explanations and visualizations.

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Contribution

Contributions are welcome! If you'd like to enhance or fix issues in the project:

1. Fork the repository.
2. Create a new branch:

   git checkout main

3. Commit your changes:

   git commit -m "Your descriptive message"

4. Push to your branch:

   git push origin main

5. Open a pull request.

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License

This project is open-source and available under the MIT License.