Machine Learning Project 2

Overview

This repository contains a comprehensive machine learning project that covers tasks such as data preprocessing, feature engineering, model training, evaluation, and hyperparameter tuning.

Skills Used

• Data Preprocessing
• Feature Engineering
• Model Training
• Model Evaluation
• Hyperparameter Tuning

Libraries Used

• pandas
• numpy
• scikit-learn
• matplotlib
• seaborn

Contents

1. Data Preprocessing:

   • Handling missing values, encoding categorical variables, and scaling features.

2. Feature Engineering:

   • Creating and selecting relevant features for model training.

3. Model Training:

   • Training different machine learning models on the dataset.

4. Model Evaluation:

   • Evaluating model performance using metrics such as accuracy, precision, recall, and F1-score.

5. Hyperparameter Tuning:

   • Optimizing model performance by tuning hyperparameters.

Usage

Ensure Python and the required libraries are installed. Run the provided Jupyter notebook to execute the machine learning workflows and visualizations.

File

• Machine_Learning_Project_2.ipynb: Contains all the code and analyses used in this project.

Contributing

Contributions are welcome! For suggestions or queries, please create a pull request or open an issue.

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