Building machine learning model

We all know that machine learning becoming popular in today's business, industry, and education as well, everyone wants to learn machine learning so, this article will develop your understanding of machine learning and data science.

1-Numpy: derived from two words Num means “Numerical” and py means “Python”, used for working with arrays, for fast processing, and for mathematical operations. Already downloaded with anaconda installation. if not so download it with the below command,

2-Pandas: used for reading, making, writing datasets from different resources, and making datasets on their own. download it with the below command,

3-Seaborn & Matplotlib: most popular and useful libraries for data visualization. you can download them with the below command,

4-Sklearn: Among the most popular and useful libraries that support almost all machine learning algorithms like classification, regression, clustering, etc. you can download it with the below command,

Now, we will move towards the building model so let's start step by step.

Pickle5: used for deploying a machine learning model. you can download it with the below command.

Step-1: importing necessary libraries.

Step-2: Read the dataset. You can download the dataset from the link and then need to paste it into the same directory where your jupyter-notebook file exists and then read it.

Step-3: Pre-Processing using pandas.

Step-4: Visualization of data using seaborn.

Step-5: Selection of features and the target variable.

we selected Price as the target variable because we need to predict the prices of houses.

Step-6: Split Data into Train & Test.

we split data into train and test where test-size = 20% and train-size=80%.

Step-7: fit training data into the machine learning model

Step-8: Now, the testing model with testing data.

Step-9: Now we will check the accuracy,r2-score, etc of a built model

our model accuracy is 91%, which means our model is the best now we will deploy it using pickle.

Step-10: Deploying model using pickle5.

That is all regarding “Building machine learning model”. you can try this on your own data.

About me:

I have over 1.5 years of experience working in Software development. Currently, I work as Software Engineer improving products and services for our customers by using retail analytics, standing up big-data analytical tools, creating and maintaining models, and onboarding compelling new data sets.

Previously, I was a Computer Vision Intern at The Spark Foundation, where I have gotten experience in the analysis of vision data from different open-source platforms including Kaggle, Google Images, Open Images, etc., and training different deep learning models on that data.

Please feel free to comment below if you have any questions

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