How To Use Logistic Regression in Python?

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Logistic regression is amongst the most popular machine learning (ML) algorithms. If you are learning about machine learning and its implementation, then you must be well-acquainted with this algorithm as it forms the basis for many advanced algorithms.

Python tutorialIn the following Python tutorial, we will discuss what logistic regression is, and how you can use this machine learning algorithm through Python while using Python for data science.

What is logistic regression?

Logistic regression refers to a machine learning algorithm used for classifying data points. It is a supervised learning algorithm, which means that it maps inputs to output according to the example pairs of the input and output.

Even though logistic regression is a simple algorithm, it has many applications in various sectors such as detecting spam, identifying cancer, and predicting diabetes.

The reason why it is called logistic regression is that it operates quite similarly to a linear regression algorithm. Notably, linear regression is another simple and highly popular machine learning algorithm.

How to use logistic regression in Python?

To perform logistic regression in Python, you will need to follow several steps. The first prerequisite is to be familiar with the algorithm and programming in Python. You should know the fundamental theorem behind logistic regression to use it effectively.

The steps for using logistic regression in Python are:

  • Installing the required Python packages (Matplotlib, NumPy, scikit-learn, and StatsModels)
  • Getting the data to train and test the model
  • Preparing the data, including cleaning it and fixing missing values
  • Transforming the data into the required form
  • Making the classification model
  • Training your model with the available data
  • Testing the model to check its accuracy
  • Optimizing the model until it has reached the required accuracy

You only need to follow such a small list of steps while using Python for this algorithm.

How to pursue a career in data science?

Data science is a broad field and the machine learning algorithm we discussed above is only a small fraction of it. If you are interested in pursuing a career in data science, then we recommend taking a data science course in India.

Investment Banking CoursesTaking such a course will help you learn the various concepts present in this subject including several machine learning algorithms and the use of artificial intelligence (AI). You can get a data science certification India-based that teaches you the latest in-demand skills for this field quickly and efficiently.

A well-reputed data science certification India-based would teach you big data, data visualization, SQL, statistics, R, Apache Spark, and many relevant skills necessary to become an expert. Moreover, having a certification will make it easier for you to stand out from your peers and become a preferred choice among the recruiters.

Learning about machine learning algorithms can be very interesting. If you are keen on learning about Python for data science through a Python tutorial, then it would be best to complete a data science course in India. And, to learn logistic regression effectively, you should practice it in different use cases. You can check out our data analytics course here.

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