Date created

8/10/2023

Mean Normalization and Data Separation

This project focuses on mean normalization and data separation techniques for data preprocessing in machine learning.

Overview

The purpose of this project is to demonstrate how to preprocess data using mean normalization and separate it into training and testing sets. Mean normalization is a technique used to scale numerical features in a dataset by subtracting the mean value and dividing by the standard deviation. Data separation involves splitting the dataset into two subsets: one for training the model and another for evaluating its performance.

Architectural Diagram

The architectural diagram will illustrates the flow of the project. The dataset is first loaded and then mean normalization is applied to the numerical features. The normalized dataset is then split into training and testing sets. The training set is used to train the machine learning model, while the testing set is used to evaluate its performance.

Instructions for Running the Project

To run this project, follow these steps:

1. Clone the repository to your local machine.
2. Install the required dependencies by running pip install -r requirements.txt.
3. Place your dataset file in the project directory.
4. Open the nplesson.py file and update the dataset_path variable with the path to your dataset file.
5. Run the python script using the command python "filename".py.
6. The script will output the mean-normalized dataset and the separated training and testing sets.

How to Improve the Project in the Future

Here are some suggestions for improving this project:

1. Implement feature scaling techniques other than mean normalization, such as min-max scaling or standardization.
2. Add data visualization techniques to gain insights into the dataset before and after mean normalization.
3. Explore different methods for data separation, such as stratified sampling or k-fold cross-validation.
4. Extend the project to include other preprocessing techniques, such as handling missing values or categorical features.

Software

• Anaconda: python, numpy, pandas
• Pycharm

Credits

I would like to express my sincere gratitude and appreciation to the entire Udacity team, for empowering me with the knowledge and skills needed to excel in the field of data science. I am truly grateful for the opportunity to be a part of this remarkable learning community. You can click the links below for more infomation.

1. Udacity

2. W3Schools

3. Stack Overflow

4. Slack