Predictive-Modeling

Introduction

This repository is based on my master's degree program with Grand Canyon University. The purpose of this class is to take us through using predictive analysis to make a prediction. As part of this class, we learned how to apply the data science methodology, which includes understanding the problem, data preparation and exploration, setup, modeling, and evaluation. There are eight weeks of lectures for this class, and each class folder is provided, including the codes, datasets, and written reports.

Requirements

Python program is the programming language used for this class. All code is built using jupyter notebook.

##Installation
Python download: https://www.python.org/downloads/
Anaconda download: https://www.anaconda.com/products/individual

Folders

Topic 1 - The Predictive Modeling Process
Topic 2 - Data Preparation and Exploratory Data Analysis
Topic 3 - Predictive Modeling Using Decision Trees
Topic 4 - Predictive Modeling Using Naïve Bayes Classifcation
Topic 5 - Predictive Modeling Using Clustering
Topic 6 - Predictive Modeling Using Generalized Linear Models
Topic 7 - Presenting and Interpreting Results of Predictive Models
Topic 8 - Applications of Predictive Models in Science, Engineering, and Business

Usage

1. Download or clone the folder
2. Create a path in your local machine that will be the source for the dataset
3. Launch anaconda
4. Open the file using a jupyter notebook
5. Change the path of the data to your new path
6. Run the code