Agile Data Science - Deploying a Predictive System



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 −

above_code_generates

Step 2 − Execute the describe function to get the data types included in attributed of csv documents.

data.describe()

describe_function

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()

associated_values

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.

data.floors.value_counts().plot(kind='bar')

data_science_analysis





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