📝 Adding README file

Author: Miguel Angel

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+<h1 align="center">NUMPY FORMULAS</h1>
+<p align="center"><i>Implementation of math known formulas in Numpy</i></p>
+<div align="center">
+  <a href="https://github.com/TheWorstOne/numpy-formulas/stargazers"><img src="https://img.shields.io/github/stars/TheWorstOne/numpy-formulas" alt="Stars Badge"/></a>
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+<a href="https://github.com/TheWorstOne/numpy-formulas/graphs/contributors"><img alt="GitHub contributors" src="https://img.shields.io/github/contributors/TheWorstOne/numpy-formulas?color=2b9348"></a>
+<a href="https://github.com/TheWorstOne/numpy-formulas/blob/master/LICENSE"><img src="https://img.shields.io/github/license/TheWorstOne/numpy-formulas?color=2b9348" alt="License Badge"/></a>
+</div>
+<br>
+
+<!-- ABOUT THE PROJECT -->
+I made this repo in order to improve my mathematical python skills. I saw it necessary because I was taking the Data Mining course at my University. In this course I learned a lot of things about distances, matrices, proximities, etc. And I took the opportunity to get a little fun with the Numpy library. Feel free to use it if it is useful to you or to improve it if you think so! ✌
+
+<p align="center">
+  <a target="_blank" href="https://realpython.com/numpy-tutorial/">
+    <img src="assets/numpy.png" alt="Logo" width="" height="">
+  </a>
+</p>
+<p align="center"><i>Image taken from <a target="_blank" href="https://realpython.com/numpy-tutorial/">realpython.com/numpy-tutorial/</a></i></p>
+<br>
+
+If you like this Repo, Please click the :star:
+
+<!-- TABLE OF CONTENTS -->
+## **Contents**
+  - [Distances](#Distances)
+  - [Normalization](#Normalization)
+  - [Proximity Measure](#Proximity-Measure)
+  - [Contact](#contact)
+
+  <!-- GETTING STARTED -->
+## **Distances**
+
+Distance measures play an important role in machine learning.
+
+A distance measure is an objective score that summarizes the relative difference between two objects in a problem domain. Most commonly, the two objects are rows of data that describe a subject (such as a person, car, or house), or an event (such as a purchase, a claim, or a diagnosis).
+
+- [Euclidean](https://github.com/TheWorstOne/numpy-formulas/blob/main/Distances/euclidean.py)
+- [Manhattan](https://github.com/TheWorstOne/numpy-formulas/blob/main/Distances/manhattan.py)
+- [Minkowski](https://github.com/TheWorstOne/numpy-formulas/blob/main/Distances/minkowski.py)
+- [Superior/Chebyshev](https://github.com/TheWorstOne/numpy-formulas/blob/main/Distances/superior.py)
+- [Cosine](https://github.com/TheWorstOne/numpy-formulas/blob/main/Distances/cosine.py)
+
+## **Normalization**
+
+Normalization is a technique often applied as part of data preparation for machine learning. The goal of normalization is to change the values of numeric columns in the dataset to use a common scale, without distorting differences in the ranges of values or losing information.
+
+- [Min-Max](https://github.com/TheWorstOne/numpy-formulas/blob/main/Normalization/minmax.py)
+- [Z-Norm](https://github.com/TheWorstOne/numpy-formulas/blob/main/Normalization/znorm.py)
+
+## **Proximity Measure**
+
+Proximity measures refer to the Measures of Similarity and Dissimilarity. Similarity and Dissimilarity are important because they are used by a number of data mining techniques, such as clustering, nearest neighbour classification, and anomaly detection.
+
+- [Nominal](https://github.com/TheWorstOne/numpy-formulas/blob/main/ProximityMeasure/nominal_proximity.py)
+- [Ordinal](https://github.com/TheWorstOne/numpy-formulas/blob/main/ProximityMeasure/ordinal_proximity.py)
+- [Binary](https://github.com/TheWorstOne/numpy-formulas/blob/main/ProximityMeasure/binary_proximity.py)
+- [Mixed](https://github.com/TheWorstOne/numpy-formulas/blob/main/ProximityMeasure/mix_proximity.py)
+
+<!-- CONTACT -->
+## **Contact**
+
+Miguel Ángel Macías - 👨‍💻[Linkedin](https://www.linkedin.com/in/mangelladev/)
+
+My Personal Website: ✨[mangelladev.com](https://mangelladev.web.app/)
+