As the world entered the era of big data in the last few decades, the need for better and efficient data storage became a significant challenge. The main focus of businesses using big data was on building frameworks that can store a large amount of data. Then, frameworks like Hadoop were created, which helped in storing massive amounts of data.
With the problem of storage solved, the focus then shifted to processing the data that is stored. This is where data science came in as the future for processing and analyzing data. Now, data science has become an integral part of all the businesses that deal with large amounts of data. Companies today hire data scientists and professionals who take the data and turn it into a meaningful resource.
Let’s now dig deep into data science and how data science with Python is beneficial.
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What is Data Science?
Let us begin our learning on Data Science with Python by first understanding of data science. Data science is all about finding and exploring data in the real world and using that knowledge to solve business problems. Some examples of data science are:
- Customer Prediction - System can be trained based on customer behavior patterns to predict the likelihood of a customer buying a product
- Service Planning - Restaurants can predict how many customers will visit on the weekend and plan their food inventory to handle the demand
Now that you know what data science is and before we get deep into the topic of Data Science with Python is let’s talk about Python.
When it comes to data science, we need some sort of programming language or tool, like Python. Although there are other tools for data science, like R and SAS, we will focus on Python and how it is beneficial for data science in this article.
Python is used as a programming language for data science because it contains costly tools from a mathematical or statistical perspective. It is one of the significant reasons why data scientists around the world use Python. If you track the trends over the past few years, you will notice that Python has become the programming language of choice, particularly for data science.
There are several other reasons why Python is one of the most used programming languages for data science, including:
- Speed - Python is relatively faster than other programming languages
- Availability - There are a significant number of packages available that other users have developed, which can be reused
- Design goal - The syntax roles in Python are intuitive and easy to understand, thereby helping in building applications with a readable codebase
If you want to learn how to install Python, check out the below instructional video on Data Science with Python -
Python Libraries for Data Analysis
Python is a simple programming language to learn, and there is some basic stuff that you can do with it, like adding, printing statements, and so on. However, if you want to perform data analysis, you need to import specific libraries. Some examples include:
- Pandas - Used for structured data operations
- NumPy - A powerful library that helps you create n-dimensional arrays
- SciPy - Provides scientific capabilities, like linear algebra and Fourier transform
- Matplotlib - Primarily used for visualization purposes
- Scikit-learn - Used to perform all machine learning activities
In addition to these, there are other libraries as well, like:
- Networks & I graph
Let’s now take a look at some of the most important Python libraries in detail:
As the name suggests, it is a scientific library that includes some special functions:
- It currently supports special functions, integration, ordinary differential equation (ODE) solvers, gradient optimization, and others
- It has fully-featured versions of the linear algebra modules
- It is built on top of NumPy
NumPy is the fundamental package for scientific computing with Python. It contains:
- Powerful N-dimensional array objects
- Tools for integrating C/C++, and Fortran code
- It has useful linear algebra, Fourier transform, and random number capabilities
Pandas is used for structured data operations and manipulations.
- The most useful data analysis library in Python
- Instrumental in increasing the use of Python in the data science community
- Used extensively for data mugging and preparation
Next, in our learning of Data Science with Python let us learn the exploratory analysis using Pandas.
Exploratory Analysis using Pandas
Exploratory data analysis is an approach used to analyze large data sets to summarize their main characteristics. This process uses visual methods to derive valuable insights.
Let’s now understand the two most common terms used in Pandas:
- Series - It is a one-dimensional object that can hold any data type, such as integers, floats, and strings
- Dataframe - A two-dimensional object that can have columns with potentially different data types
Fig: DataFrame with 4 rows and 3 columns
Let’s explore more on how to use Pandas to predict whether a particular customer’s loan application will be approved or not.
1. Import the necessary libraries and read the dataset using the read_csv() function:
2. Check the summary of the dataset using the describe() function:
3. Visualize the distribution of the loan amount:
4. Visualize the distribution for the applicant’s income:
5. Visualize the distribution for categorical values:
If you want to learn more about exploratory analysis using Pandas, check out Simplilearn’s Data Science with Python video, which can help.
We can see that columns like LoanAmount and ApplicantIncome contain some extreme values. We need to process this data using data wrangling techniques to normalize and standardize the data.
We will now take a look at data wrangling using Pandas as a part of our learning of Data Science with Python.
Data Wrangling using Pandas
Data wrangling refers to the process of cleaning and unifying messy and complicated data sets. The following are some of the benefits of data wrangling:
- Reveals more information about your data
- Enables decision-making skills in the organization
- Helps to gather meaningful and precise data for the business
In reality, most of the data a business generates will be messy and carry missing values. The loan data set has missing values in some of its columns.
To check if your data has missing values:
There are various ways to fill in the missing values. Deciding which parameters to use when filling them in will depend on the business scenario.
Here is an example of replacing the missing values by taking the mean of a particular column.
You can check the data types for each column using dtypes:
You can also combine and merge data frames using simple concatenation and merge methods.
To learn how you can see if your data has missing values, you can watch Simplilearn’s Data Science with Python video.
Now that we have completed the wrangling steps let’s jump into building the model using scikit-learn which enhances our learning of Data Science with Python.
- We need to import the various models from the scikit-learn module
- Extract the independent and dependent variables from the dataset
- Split the dataset into training and testing - 75 percent for training and 25 percent for testing
We will use the Logistic Regression algorithm to build the model. Logistic Regression is suitable when the dependent variable is binary.
- Feature scaling to standardize the independent features present in the data within a fixed range
- Fitting the data into the Logistic Regression model
- Predict the values of the test set
- Build a confusion matrix to evaluate the performance of the model
Let’s now understand how the confusion matrix decides the accuracy of the model.
The following will calculate the model’s accuracy:
(True Positive (TP) + True Negative (TN)) / Total
(103+18)/150 = 0.80
Precision is when it predicts yes and how often is it correct.
True Positive / Predicted Yes = 103/130 = 0.79
- Find the accuracy of the model
As you can see, we have successfully built a logistic regression model with 80 percent accuracy.
After reading this Data Science with Python article, you have learned what data science is, why it is important, and the different libraries involved in data science. You learned the different skills needed when it comes to data science, such as exploratory data analysis, data wrangling, and model building. Finally, you built a model using Logistic Regression, which helps predict whether a particular customer’s loan will be approved or not.
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