Step-by-Step Building Block For Machine Learning Models

Step-by-Step Building Block For Machine Learning Models

Step-by-Step Building Block For Machine Learning Models. Step-by-Step Building Block For Machine Learning Models How to develope a machine model, what are the steps in developing.

Machine learning is a process where the machine can learn hidden patterns from the data and has the potential to give predictions. It is also called the subset and application of Artificial Intelligence. There are many different real-life use cases of machine learning that are widely used today for example, in the banking sector where the authorities use machine learning models to predict whether a loan applicant will be a defaulter or not. The website that generates your credit score also uses machine learning for calculations. There are mainly two types of tasks that are done in machine learning that includes Classification and Regression. Classification is a task where predictive models are trained to classify data into different classes like classifying different fruits by passing images to the model whereas regression is a task where models are built to predict continuous variables like predicting the temperature of the next day.

In this article, we will explore classification tasks mainly and we will see how to build a classification model in machine learning following the different steps that are required. We will make use of Iris data set that is publicly available for downloading on the UCI Machine learning Repository. The data set contains the length and width of sepals and petals with their respective species. We will build a machine learning model that would be able to predict which species the flower belongs to when we pass these lengths of the flower to the model.

What Will You Learn From This Article? 

  1. Import data from csv files. 
  2. Exploratory Data analysis
  3. Data visualisation
  4. Splitting data into training and testing 
  5. Building machine learning models
  6. Predictions by the models
  7. Model Evaluation 
  8. Importing the data from csv files

There is a function in the pandas package that is widely used for importing datasets. It allows you to import data in different formats like csv files, xlsx, etc. We will make use of the same function. Use the below code to import the data set and print the first 10 rows in the data. We will first import the pandas package and then read the data.

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