Data Analysis and Machine Learning Project

This project contains code for performing data analysis and machine learning tasks, including data preprocessing, visualization, classification, regression, and clustering. It uses Python and several popular libraries such as pandas, numpy, matplotlib, and scikit-learn.

Getting Started

To run this project on your local machine, follow the instructions below:

Prerequisites

• Python 3.7
• Conda package manager

Installation

1. Clone this repository to your local machine or download the source code as a ZIP file.
2. Open a terminal or Anaconda Prompt and navigate to the project directory.

Setup the Environment

1. Create a new Conda environment using the provided YAML file:

   • For Windows: conda env create -f windows-env.yaml
   • For macOS: conda env create -f mac-env.yaml

2. Activate the created environment:

   • For Windows: conda activate windows-env
   • For macOS: conda activate mac-env

Usage

1. Place your dataset file in the project directory.
2. Open the main.py file and modify the following variables:
   • dataset_file: Set it to the name of your dataset file.
   • target_variable: Replace 'target' with the actual column name of your target variable.
   • regression_target: Replace 'regression_target' with the actual column name of your regression target variable.
   • num_clusters: Set it to the desired number of clusters for clustering.
3. Save the changes and run the main.py file using Python: python main.py.
4. The code will perform data preprocessing, visualization, classification, regression, and clustering based on the provided functions.
5. You can modify the code and functions according to your specific requirements.

Contributing

Contributions to this project are welcome! If you find any issues or want to add new features, please feel free to open an issue or submit a pull request.

Acknowledgments

• This project was inspired by the need for a code template for data analysis and machine learning tasks.
• Thanks to the creators and maintainers of the pandas, numpy, matplotlib, and scikit-learn libraries for providing powerful tools for data manipulation and analysis.

Feel free to modify this README file according to your specific project needs. Include any additional information or instructions that might be relevant to users or contributors.

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