Python Algo_Data_Plot_ML_Playground

A multitude of exercises related to different aspects of Python. The purpose of these exercises is to test my current skills as well as to learn new techniques/methods. These exercises come from pynative and w3ressource.

Among these exercises, the following themes have been tackled:

• Algorithms on native language elements and storage objects. Aspects such as writing to files and object-oriented were also covered.
• Data manipulation via the well-known NumPy and Pandas packages.
• Data visualization via the MatplotLib and Seaborn packages.
• Putting machine learning into practice with the famous iris dataset. The various classical aspects of analysis were covered: data exploration, data visualization, principal component analysis and training of two simple models: k-NN and logistic regression.

Content of the folder exercises

• Basic's topic : Variables, Operators, Loops, String, Numbers, List
• Input/Output's topic : print() and input(), File I/O
• Loop's topic : If-else statements, loop, and while loop.
• Function's topic: Functions arguments, built-in functions.
• String's topic: String operations and manipulations.
• Data structure's topic: List, Set, Dictionary, and Tuple operations
• List's topic: List operations and manipulations, list functions, list slicing and list comprehension
• Dictionary's topic: Dictionary operations and manipulations, dictionary functions and dictionary comprehension
• Tuple's topic: Tuple creation, operations, unpacking of a tuple
• Set's topic: Set operations, manipulations, and set functions
• OOP's topic: Object, Classes, Inheritance
• Date and Time's topic: Date, time, DateTime, Calendar.
• JSON's topic: JSON creation, manipulation, Encoding, Decoding, and parsing
• Numpy's topic: Array manipulations, numeric ranges, Slicing, indexing, Searching, Sorting, and splitting
• Pandas' topic: Data-frame, Data selection, group-by, Series, sorting, searching, and statistics
• Matplotlib's topic: Line plot, Style properties, multi-line plot, scatter plot, bar chart, histogram, Pie chart, Subplot, stack plot
• Random data generation's topic: random module, secrets module, UUID module

Content of the folder machine_learning

• Exploration's topic : Data manipulation to understand the dataset
• Visualization's topic : Data manipulation to generate trend graphs and a PCA
• kNN's topic : Various manipulations of test and training datasets to work with the kNN model
• Logistic regression's topic: Data manipulation for logistic regression

Launch virtual env and packages

From the root of the project :

$ python -m venv env
$ source env/Scripts/activate
$ pip install -r requirements.txt

Don't forget to select the good interpreter inside VSCode

Launch all the tests

From the root of the project :

$ python -m unittest discover