Predicting Missing Values with Python

Predicting Missing Values with Python

Building Models for Data Imputation. For data scientists, handling missing data is an important part of the data cleaning and model development process. Often times, real data contains multiple sparse fields or fields that are laden with bad values. In this post, we will discuss how to build models that can be used to impute missing or bad values in data.

For data scientists, handling missing data is an important part of the data cleaning and model development process. Often times, real data contains multiple sparse fields or fields that are laden with bad values. In this post, we will discuss how to build models that can be used to impute missing or bad values in data.

Let’s get started!

For our purposes, we will be working with the wines dataset which can be found here.

To start, let’s read the data into a Pandas data frame:

import pandas as pd
df = pd.read_csv("winemag-data-130k-v2.csv")

Next, let’s print the first five rows of data:

print(df.head())

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Let’s take a random sample of 500 records from this data. This will help with speeding up model training and testing, though it can easily be modified by the reader:

import pandas as pd
df = pd.read_csv("winemag-data-130k-v2.csv").sample(n=500, random_state = 42)

Now, let’s print the info corresponding to our data which will give us an idea of which columns have missing values:

print(df.info())

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