DS#6 Components of Python for data Science

 Components of Python for DS

1.3.1 Python Components for Data Science

Data science” is just about as broad of a term as they come. It may be easiest to describe what it is by listing its more concrete components:

Data exploration & analysis.

• Included here: Pandas; NumPy; SciPy; a helping hand from Python’s Standard Library.

Data visualization. A pretty self-explanatory name. Taking data and turning it into something colorful.

• Included here: Matplotlib; Seaborn; Datashader; others.

Classical machine learning. Conceptually, we could define this as any supervised or unsupervised learning task that is not deep learning (see below). Scikit-learn is far-and-away the go-to tool for implementing classification, regression, clustering, and dimensionality reduction, while StatsModels is less actively developed but still has a number of useful features.

• Included here: Scikit-Learn, StatsModels.

Deep learning. This is a subset of machine learning that is seeing a renaissance, and is commonly implemented with Keras, among other libraries. It has seen monumental improvements over the last ~5 years, such as AlexNet in 2012, which was the first design to incorporate consecutive convolutional layers.

• Included here: Keras, TensorFlow, and a whole host of others.

Data storage and big data frameworks. Big data is best defined as data that is either literally too large to reside on a single machine, or can’t be processed in the absence of a distributed environment. The Python bindings to Apache technologies play heavily here.

• Apache Spark; Apache Hadoop; HDFS; Dask; h5py/pytables.

odds and ends. Includes subtopics such as natural language processing, and image manipulation with libraries such as OpenCV.

• Included here: nltk; Spacy; OpenCV/cv2; scikit-image; Cython.

Among the various components listed above, we will discuss about most important components in Python for Data Science.

1.3.1.1. pandas (Python n-dimensional arrays)

Pandas is a Python library used for working with data sets. It has functions for analyzing, cleaning, exploring, and manipulating data. The name "Pandas" has a reference to both "Panel Data", and "Python Data Analysis" and was created by Wes McKinney in 2008.

Features:

·         Provides a really fast and efficient way to manage and explore data

·         Alignment and indexing is so easy

·         Handling missing data is very much possible

·         Cleaning up data is simple

·         Input and output tools are there

·         Multiple file formats are supported

·         Merging and joining of datasets is so easy

·         A lot of time series support in pandas

·         Optimized performance for arrays is supported

·         Very opt for Visualization tool

·         Supports Grouping

·         Supports Masking data

·         Supports to pick unique data

·         Perform mathematical operations on the data

 

Installation:

            pip install pandas

Usage:

            import pandas as pd

1.3.1.2. NumPy:

Python numPy is a library adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. 

numPy (Numerical Python) is an open-source core Python library for scientific computations. It is a general-purpose array and matrices processing package.

 Python is slower as compared to Fortran and other languages to perform looping. To overcome this we use numPy that converts monotonous code into the compiled form.

 Faster than Python inherent list data structure, takes less storage and more compact.

 

Important Features of numpy

1.        High-performance N-dimensional array object - 1D array, 2D Array, Multi-Dimensional Array
2.         It contains Tools for integrating many C/C++ like languages
3.         It contains a multidimensional container for generic data
4.         It supports linear algebra, Fourier transform, and random number capabilities
5.         It consists of broadcasting functions
6.         It had data type definition capability to work with varied databases

Installation:

            pip install numpy

 Usage:

            import numpy as np

 Which is better NumPy or Pandas?

NumPy is more memory efficient in comparison to Pandas.

It helps to work on the “N” dimensional data structure which gives it a clear edge over Pandas data frames.

When it comes to working in the domain of data science, the NumPy library possesses multiple toolkits such as Tensorflow and Seaborn which can be fed to the models, unlike Pandas.

NumPy is also relatively faster than the Pandas series as it takes much time for indexing the data frames.

Pandas have their own importance as the python library, but looking at all the above advantages offered by the NumPy, the conclusion is that NumPy is better than Pandas.

1.3.1.3. matplotlib

Matplotlib is a low level graph plotting library in python that serves as a visualization utility.

Matplotlib was created by John D. Hunter.

Matplotlib is open source and we can use it freely.

Matplotlib is mostly written in python, a few segments are written in C, Objective-C and Javascript for Platform compatibility.

Matplotlib is one of the most amazing powerful libraries in python for data visualization.

Data visualization which is the process of translating the numbers, text, or large data sets into various types of graphs such as histograms, maps, bar plots, pie charts, etc. For visualizations, we need some tools or technology. This is the library which helps to draw charts.

Matplotlib is an open-source drawing library that supports various drawing types, Easy to generate plots, histograms, bar charts, scatter and other types of charts with just a few lines of code

It’s often used in web application servers, shells, and Python scripts

The above fig shows Various Drawing Types supported by matplotlib.pyplot

Features

·        Supports 14 types of charts

·        Charting is easy, fun and child’s play with matplotlib.

·        Supports Titles, Labels, gridlines, legends, color and some cool visual aids for charts

Installation

            pip install  matplotlib

Usage 

Import matplotlib.pyplot as plt

1.3.1.4. scikit  (scikit-learn)

Scikit-learn (Sklearn) is the most useful and robust library for machine learning

It provides a selection of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction

This library is built upon NumPy, SciPy and Matplotlib.

Scikit-learn (Sklearn) is the most useful and robust library for machine learning

This library is built upon NumPy, SciPy and Matplotlib.

Through scikit-learn, we can implement various machine learning models for regression, classification, clustering, and statistical tools for analyzing these models.

It also provides functionality for dimensionality reduction, feature selection, feature extraction, ensemble techniques, and inbuilt datasets. We will be looking into these features one by one.

Sci-kit Features

 

• Data Splitting -  Splitting the dataset is essential for an unbiased evaluation of prediction performance. 

• Linear Regression - This supervised ML model is used when the output variable is continuous and it follows linear relation with dependent variables. It can be used to forecast sales in the coming months by analyzing the sales data for previous months.

• Logistic regression : Logistic Regression is also a supervised regression algorithm just like linear regression. The only difference is that the output variable is categorical. It can be used to predict whether a patient has heart disease or not.

• Decision Trees : A Decision Tree is a powerful tool that can be used for both classification and regression problems. It uses a tree-like model to make decisions and predict the output. It consists of roots and nodes. Roots represent the decision to split and nodes represent an output variable value. A decision tree is an important concept. Decision trees are useful when the dependent variables do not follow a linear relationship with the independent variable i.e linear regression does not accurate results.

• The ensemble method is a technique in which multiple models are used to predict the output variable instead of a single one. The dataset is randomly divided into subsets and then passed to different models to train them. The average of all the models is considered when we predict the output. The Ensemble technique is used to reduce the variance-biases trade-off.There are generally two types of ensembling techniques:
• Bagging is a technique in which multiple models of the same type are trained with random samples from the training set. The inputs to different models are independent of each other.
• Boosting is a technique in which multiple models are trained in such a way that the input of a model is dependent on the output of the previous model. In Boosting, the data which is predicted incorrectly is given more preference.

• Random Forest
• Random Forest is a bagging technique in which hundreds/thousands of decision trees are used to build the model. Random Forest can be used for both classification and regression problems. It can be used to classify loan applicants, identify fraudulent activity and predict diseases.

• XG Boost
• XGBoost stands for eXtreme Gradient Boosting. It is a boosting technique that provides a high-performance implementation of gradient boosted decision trees. The main features of XG-Boost are it can handle missing data on its own, it supports regularization and generally gives much more accurate results than other models.

• Support Vector Machines(SVM)
• Supervised Vector Machine is a supervised ML algorithm in which we plot each data item as a point in n-dimensional space where n is the number of features in the dataset. After, we perform classification by finding the hyperplane that differentiates the classes very well. The data points which are closest to the hyperplane are called support vectors. It can also be used for regression problems but generally used in classification only. It is used in many applications such as face detection, classification of mails, etc.
• Feature Extraction
• Cross validation
• Clustering
• Scaling – Standardization, and Normalisation

Installation : 

            pip install -U scikit-learn

Usage : 

            import sklearn as skl