This repository contains code for binary classification of real data with missing values(replaced with ?) and without missing values.
Numpy - Python library to support various functionalities for arrays and higher dimensions matrix
Pandas - Python library to support data analysis. Used in the project to read csv file
ScikitLearn - Machine Learning Library for Python
Matplotplib - Scientific plotting library to visualize the result and analysis.
For running the code, Python (>= 3.5), NumPy (>= 1.11.0) and SciPy (>= 0.17.0) is required.
After installing numpy,pandas and matplotlib, scikitlearn can be downloaded using following command ::
pip install -U scikit-learn
or
conda install scikit-learn
For more details you can check documentation of scikit-learn here. Checkout the changelog for details of notable changes in ScikitLearn.
XGBoost: XGBoost is optimized gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework.
Clone this repo by typing following command:
git clone https://github.com/abinash124/Binary-Classification--Machine-Learning.git
Go to the source code directory and run
python main.py
Enjoy coding!!