machine-learning's Introduction

Machine Learning

The repository contains code for different types of Machine Learning Models. For each type of Model, I have performed following steps:

Preprocess the data
Training model
Making Predictions
Visualizing the predictions on training and test data

Along with this I have added a data prepossessing script that can be used in most of the cases.

Tools and Technologies:

Following are the tools and technologies used in the project:

Scikit-Learn
Pandas
Matplotlib
Python

Project Structure

    Machine Learning
        |
        |- DataPreprocessing
        |   |
        |   |- data_preprocessing.py
        |
        |- DataSets
        |   |- DataPreprocessing
        |   |       |- Data File
        |   |- {Model Type 1}
        |   |       |- Data File
        |   |       .
        |   |       .
        |   |       .
        |   |- {Model Type N}
        |   |       | - Data File   
        |   |
        |
        |- Regression
        |   |
        |   | - {Type 1}
        |   |       |- Implementation of Model
        |   |       .
        |   |       .
        |   |       .
        |   | - {Type N} 
        |   |       |- Implementation of Model
        |   |
        |
        |- .gitignore
        |- requirements.txt

Getting Started

Following instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Clone the repository using below command:
git clone <https://github.com/iftikhar1995/Machine-Learning.git>
Install the dependencies mentioned in the requirements.txt file. Following is the command to install the dependencies:
pip install -r requirements.txt

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