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Python and data analytics
This document provides an overview of machine learning in Python using key Python libraries. It discusses popular Python libraries for machine learning like NumPy, SciPy, Pandas, Matplotlib and scikit-learn. It outlines the typical steps in a machine learning project including defining the problem, preparing and summarizing data, evaluating algorithms, and presenting results. It also introduces the Iris dataset as a sample classification dataset and discusses loading, handling and visualizing sample data for a machine learning project in Python.
Python and data analytics
- 1. Python and DataAnalyticsPython and Data Analytics •Understand the problem By Understanding the Data •Predictive Model Building: Balancing Performance, Complexity, and theBig Data
- 2. Machine learningMachine learning machinelearning explores the study and construction of algorithms that can learn from and make predictions on data.
- 4. Predictive model buildingPredictivemodel building The process of building a predictive model is called training. Attributes: the variables being used to make predictions is known as: ◦ Predictors. ◦ Features ◦ Independent variables ◦ Input Labels are also known as, ◦ Outcomes ◦ Targets ◦ Dependent variables ◦ Responses
- 5. A machine learningproject may not be linear, but it has a number of well known steps: Define Problem. Prepare Data. Evaluate Algorithms. Improve Results. Present Results.
- 6. the iris datasethas followingthe iris dataset has following structurestructure Attributes are numeric so you have to figure out how to load and handle data. It is a classification problem, allowing you to practice with perhaps an easier type of supervised learning algorithm. It is a multi-class classification problem (multi- nominal) that may require some specialized handling. It only has 4 attributes and 150 rows, meaning it is small and easily fits into memory. All of the numeric attributes are in the same units and the same scale, not requiring any special scaling or transforms to get started.
- 7. Machine Learning inPython:Machine Learning in Python: Step-By-StepStep-By-Step Installing the Python and SciPy platform. Loading the dataset. Summarizing the dataset. Visualizing the dataset. Evaluating some algorithms. Making some predictions.
- 8. Basic library inpythonBasic library in python NumPy‘s array type augments the Python language with an efficient data structure useful for numerical work, e.g., manipulating matrices. NumPy also provides basic numerical routines, such as tools for finding eigenvectors. SciPy contains additional routines needed in scientific work: for example, routines for computing integrals numerically, solving differential equations, optimization, and sparse matrices. The matplotlib module produces high quality plots. With it you can turn your data or your models into figures for presentations or articles. No need to do the numerical work in one program, save the data, and plot it with another program.
- 9. The Pandasmodule is a massive collaboration of many modules along with some unique features to make a very powerful module. Pandas is great for data manipulation, data analysis, and data visualization. The Pandas modules uses objects to allow for data analysis at a fairly high performance rate in comparison to typical Python procedures. With it, we can easily read and write from and to CSV files, or even databases. From there, we can manipulate the data by columns, create new columns, and even base the new columns on other column data. The scikit library used for Simple and efficient tools for data mining and data analysis Accessible to everybody, and reusable in various contexts Built on NumPy, SciPy, and matplotlib Open source, commercially usable
- 10. NumPy: Base n-dimensionalarray package SciPy: Fundamental library for scientific computing Matplotlib: Comprehensive 2D/3D plotting IPython: Enhanced interactive console Sympy: Symbolic mathematics Pandas: Data structures and analysis
- 11. 1. Downloading, Installingand Starting Python SciPy 1.1 Install SciPy Libraries There are 5 key libraries that you will need to install. Below is a list of the Python SciPy libraries required for this tutorial: scipy numpy matplotlib pandas sklearn
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