jamesgeorge007/gender-classifier

A machine learning model which predicts the gender based on the data with which it is trained.

Gender-Classifier

Basic Info

This is a machine learning model which is trained with the data-sets corresponding to the height, weight and shoe sizes of male and female and then predicting based on an input.

Trained using 3 sci-kitlearn models:

1. DecisionTreeClassifier
2. KNeighborsClassifier
3. QuadraticDiscriminantAnalysis 

Instructions

1. Make sure that you have python installed and the path is properly configured.
2. Install the dependency using pip package manager i,e open your command prompt and type in py -m pip install sklearn.
3. Install the Git desktop client.
4. Clone the repository: git clone https://github.com/jamesgeorge007/Gender-Classifier-Machine-Learning-model-in-sklearn-
5. Navigate to the src directory where you can find the source file.

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See the AUTHORS.rst <AUTHORS.rst>_ file for a complete list of contributors.

It is currently maintained by a team of volunteers.

Website: http://scikit-learn.org

Installation

Dependencies

scikit-learn requires:

- Python (>= 2.7 or >= 3.4)
- NumPy (>= 1.8.2)
- SciPy (>= 0.13.3)

For running the examples Matplotlib >= 1.3.1 is required.

scikit-learn also uses CBLAS, the C interface to the Basic Linear Algebra
Subprograms library. scikit-learn comes with a reference implementation, but
the system CBLAS will be detected by the build system and used if present.
CBLAS exists in many implementations; see `Linear algebra libraries
<http://scikit-learn.org/stable/modules/computational_performance.html#linear-algebra-libraries>`_
for known issues.

User installation

If you already have a working installation of numpy and scipy, the easiest way to install scikit-learn is using pip ::

pip install -U scikit-learn

or conda::

conda install scikit-learn

The documentation includes more detailed installation instructions <http://scikit-learn.org/stable/install.html>_.

Development

We welcome new contributors of all experience levels. The scikit-learn community goals are to be helpful, welcoming, and effective. The Development Guide <http://scikit-learn.org/stable/developers/index.html>_ has detailed information about contributing code, documentation, tests, and more. We've included some basic information in this README.

Important links

- Official source code repo: https://github.com/scikit-learn/scikit-learn
- Download releases: https://pypi.python.org/pypi/scikit-learn
- Issue tracker: https://github.com/scikit-learn/scikit-learn/issues

Source code
~~~~~~~~~~~

You can check the latest sources with the command::

    git clone https://github.com/scikit-learn/scikit-learn.git

Setting up a development environment

Quick tutorial on how to go about setting up your environment to contribute to scikit-learn: https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md

Testing

After installation, you can launch the test suite from outside the
source directory (you will need to have the ``pytest`` package installed)::

    pytest sklearn

See the web page http://scikit-learn.org/dev/developers/advanced_installation.html#testing
for more information.

    Random number generation can be controlled during testing by setting
    the ``SKLEARN_SEED`` environment variable.

Submitting a Pull Request

Before opening a Pull Request, have a look at the full Contributing page to make sure your code complies with our guidelines: http://scikit-learn.org/stable/developers/index.html

Project History

The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See the AUTHORS.rst <AUTHORS.rst>_ file for a complete list of contributors.

The project is currently maintained by a team of volunteers.

Note: scikit-learn was previously referred to as scikits.learn.

Help and Support

Documentation

- HTML documentation (stable release): http://scikit-learn.org
- HTML documentation (development version): http://scikit-learn.org/dev/
- FAQ: http://scikit-learn.org/stable/faq.html

Communication

• Mailing list: https://mail.python.org/mailman/listinfo/scikit-learn
• IRC channel: #scikit-learn at webchat.freenode.net
• Stack Overflow: http://stackoverflow.com/questions/tagged/scikit-learn
• Website: http://scikit-learn.org

Citation

If you use scikit-learn in a scientific publication, we would appreciate citations: http://scikit-learn.org/stable/about.html#citing-scikit-learn