In this example, we will figure out how to create and deploy predictive model which helps in the expectation of house costs utilizing python script. The important framework utilizing for deployment of predictive system incorporates Anaconda and “Jupyter Notebook”.
Follow these steps to deploy a predictive system −
Step 1 − Implement the following code to convert values from csv documents to associated values.
import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import mpl_toolkits %matplotlib inline data = pd.read_csv("kc_house_data.csv") data.head()
The above code generates the following output −
Step 2 − Execute the describe function to get the data types included in attributed of csv documents.
Step 3 − We can drop the related values based on the deployment of the predictive model that we made.
train1 = data.drop(['id', 'price'],axis=1) train1.head()
Step 4 − You can visualize the data according to the records. The data can be utilized for data science analysis and output of white papers.