Matplotlib is a Python library for creating 2D plots. It provides an API that allows users to easily create charts with just a few lines of code. The library provides several types of charts, including line charts, histograms, scatter charts, and bar charts. Let’s see Data Science Projects using matplotlib for Practice.
Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes simple things easy and difficult things possible. Create post-quality plots. Create interactive diagrams that can be zoomed, scrolled, and updated.
Is matplotlib included in python?
Although matplotlib is not part of the standard library that is installed by default in Python, there are several toolkits available that extend the functionality of Python matplotlib.
Matplotlib is very popular because it is very easy to get started. If you have already installed Python on your machine and set up your code editor, follow these steps: First, download matplotlib. Then open a new code file and import matplotlib.
We live in a visual world, where images speak louder than words. Data visualization is extremely important in all fields of science, especially when it comes to data science. It is easier for the human brain to understand and remember images than numbers and words. Visualization also makes it easier to find trends, patterns, and relationships within groups of data. There are many high-quality visualization libraries for Python. Matplotlib is one of the most popular visualization libraries focused on creating public-quality static 2D and 3D plots as well as animated and interactive visualizations. If you are familiar with MATLAB, you will soon realize that it has a MATLAB-like interface for plotting and display.
Some of the advantages of the Matplot library include that it is easy to get started. Matplotlib is extremely powerful because it allows users to create different types of plots. It can be used in various user interfaces such as IPython shells, Python scripts, Jupyter notebooks, as well as web applications and GUI toolkits. It has support for labels and texts with LaTeX format. He has great control over all aspects of a figure or plot. It supports high-quality output in various formats including PNG, SVG and PDF. GUI for interactive diagram exploration and graph generation, background images. Useful for badge work.
One of the key features of matplotlib is the ability to use a programmatic approach in which plots are generated by writing code. Instead of manually creating graphics through a graphical user interface, you control all aspects of its appearance. This is extremely important because programmatically generated tables can be easily reproduced or adjusted when data is updated and time is saved because long and tedious processes need not be repeated in the GUI. Finally, matplotlib is open source and therefore data scientists and developers can use it for free.
Advantage of matplotlib
- Matplotlib is extremely powerful because it allows users to create many different types of plots.
- It can be used in various user interfaces such as iPython shells, Python scripts, Jupyter notebooks, as well as web applications and GUI toolkits. It has support for labels and texts with LaTeX format.
Let discuss Data Science Projects Using Matplotlib
A credit card fraud detection project as a classification problem
Credit card companies need to be able to detect fraudulent transactions in their systems so they can charge the customer fairly and appropriately. Companies must have models to understand which transactions are genuine and which are potentially fake. The problem is compounded by the fact that the dataset is unbalanced, which means that there are very few bogus transactions among the real ones.
Data set description
A data set consists of transactions made by customers during a specific block of time. A data set consists of three fields: time, amount, and numeric input values. The numeric input values are the output of the principal component analysis transformation on the feature set.
By comparison, time and amount are the transaction time and the transaction amount, respectively. The PCA transformation is applied to hide customer information and functions to maintain privacy.
library
pandas, marine, matplotlib
The core of the credit card fraud detection model is based on the concept of an authentication matrix. Validation matrices define how accurate true predictions are on actual real data. The following two types of validation matrix are used:
The recovery matrix is the ratio between the actual number of correct predictions and the total number of valid values.
The accuracy matrix is the ratio of the actual true values in the data set to the total number of true predictions given by the model.
- Random Forest Classification
- Support Vector Classifiers
- Decision Tree Classifier
- K-Nearest Neighbor Classifier or KNN
- Logistic regression
Among all the algorithms, logistic regression and k-nearest neighbors are the most accurate.
Recognition of handwritten digits using CNN for the MNIST dataset
The goal of this Data Science Projects Using Matplotlib is to correctly identify handwritten digits and store them digitally in one place. Before the advent of computers 25 years ago, organizations relied on paper to store events and details. Data is now stored in these paper documents, though it decays slowly. It is important to store these old records in digital copies for future reference if necessary. Assigning human resources to such a task seems unnecessary when it is automated and duplicated by data science and artificial intelligence.
Data set
The MNSIT or Modified National Institute of Standard and Technology dataset is very popular for handwritten digit recognition models. Stores more than 60,000 images of handwritten digits, each image size is 28×28 pixels.
Data processing
Data modeling
Shaping the data means transforming a 3-dimensional vector into a 4-dimensional vector, since the model takes 4d vectors as input.
coding
This means tagging the images with numbers so they can be efficiently processed in the model. Manipulating numbers is relatively easier than manipulating images.
Feature scaling
Images are scaled from the range of 0-255 pixels to 0-1 so that a standard scale is available for all images.
Libraries/packages: NumPy, Pandas, Matplotlib, TensorFlow, sci-kit learn, seaborn.
Time series using long-short-term memory estimation
LSTM or Long-Term Memory Network is an artificial recurrent neural network with one memory cell in each node. An LSTM has feedback connections in its hidden layers that differentiate it from a forward neural network. This overcomes the vanishing gradient problem.
Some common examples include sentiment analysis, video analysis, speech recognition, etc.
Data set description
The data set contains the monthly number of passengers traveling by a particular airline. The data has the format – Month of the year, number of passengers. The goal of the project is to predict the future number of passengers for a given month using past data and recent memory.
normalize the data
The data is normalized using the MinMaxScaler function present in the preprocessing package inside sklearn. After the MinMaxScaler operation, we need to change the set or data in the range -1 to 1.
library
pandas, matplotlib, dataset, kera, math, sklearn
Retail price optimization based on price elasticity of demand.
Price elasticity or elasticity of demand is the degree to which the actual demand for a good changes when the price changes. As a general observation, as goods become more expensive, people want them less. Price and elasticity of demand vary from product to product. There are products for which a slight increase in price will result in a drastic decrease in consumer demand. Although there are products which are rarely falling, but the demand for them regardless of how much the price has increased.
Data set description
The data set includes product name, product price, regional holidays, product combination with other products, etc.
subset
Subsetting is the idea of creating a subset of features that will properly define a model. It excludes all the extra and unnecessary features that do not contribute to the accuracy of the model.
Replace missing data points
Delete tickets with a price of 0
library
numpy, pandas, matplotlib, seaborn, sklearn, scipy.sparse, lightGBM
Implementation
Linear regression plots variables on a line graph to model a transformed/normalized data set.
LightGBM uses a tree-based algorithm for gradient-boosting framework.
Conlusion:
There are many more Data Science Projects Using Matplotlib. Matplotlib is Python library used in data science.
