Machine Learning 🧠. A machine that learns. Sounds cool and… | by Isbilya Nathifa | Mar, 2025

First issues first open your Google Colab because it’s the medium we’ll be utilizing. Subsequent, like every other information evaluation venture, you must enter or make an information set that may be learn by the machine. On this introduction, we’re going to make use of a pre-made information set that encompass salaries of various folks and their components (variable) that, could or could not, impact their wage.

import pandas as pd
information = pd.read_csv('/content material/Salary_Data.csv')
information.head() #that is to indicate the primary 5 information of the set

Now, let’s have a look of what kind of knowledge every variable is. (*A variable is each information that isn’t what we try to foretell. On this case, variables are each information besides the wage)

information.dtypes

Discover how a number of the information are within the type of object. This can be a drawback we have to resolve. Why? As a result of a machine can’t predict a linear regression if the info saved are usually not numbers (this embody float and integer). That’s why our subsequent step is to vary each information which can be non numbers (objects) into numbers (float/integer).

from sklearn.preprocessing import LabelEncoder
categorical_column = information.select_dtypes(embody=['object']).columns
label_encoders = {}
for col in categorical_column:
label_encoders[col] = LabelEncoder()
information[col] = label_encoders[col].fit_transform(information[col])
information.head() #this exhibits the primary 5 information of the brand new set

Okay, now that’s carried out, you in all probability will assume that we will go straight to linear regression, hey? Nicely, dangerous information is… not but. There’s another factor we’ve got to examine which is the NaN worth. “Why on earth do we’d like that?” NaN means there isn’t any worth within the information. No ‘0’, no ‘1’, no nothing. That is dangerous for the machine as a result of it signifies lacking information. In an effort to forestall the machine from getting confused for not getting a whole information set, we have to examine what number of NaN worth are within the set utilizing this straightforward code.

information.isna().sum()

As you’ll be able to see, there are some NaN, or lacking values, in a few of these information. In an effort to change that, we have to fill in these with values that may fairly characterize the info. There are lots of choices. You should utilize median, imply, or mode. However now we’re going to go along with the info imply of their variable.

information.fillna(information.imply(), inplace=True)
information.isna().sum()

Okay, after some checking and setting, the info set is now prepared to make use of to foretell wage with linear regression.