q Introduction to Machine Learning Multiple Regression - codingstreets
Search

Introduction to Machine Learning Multiple Regression

Machine Learning Multiple Regression – Before moving ahead, let’s take a look at Introduction to Machine Learning Polynomial Regression.

### Multiple Regression

Multiple Regression is a regression that predict future value based on multiple variables.

#### How Does It Work?

The Pandas module allows to read csv file and return a DataFrame object.

Example: Import necessary module to create multiple regression.

```				```
import pandas as pd
from sklearn import linear_model

X = file_read[["Weight", "Roll_No"]]     # independent value
y = file_read["ID"]                      # dependent value

regression = linear_model.LinearRegression()
regression.fit(X, y)                     # create linear object

# predict the Weight of a student where the Roll_No is 100, and the ID is 1000:
predictIDvalue = regression.predict([[100, 1000]])

print(predictIDvalue)

```
```
```				```
Output -

[76.69555739]
```
```

In the final analysis, it returned predicted value of variable ‘ID.’

Here’s the brief explanation of above mentioned code.

In lines 1 and 2,

Imported Pandas and sklearn module to create multiple regression.

In line 4,

Loaded a file in csv format named ‘file’ to make a list of independent and dependent values.

In lines 6 and 7,

Stored the list of independent values in variable called X and dependent values in variable called y.

In lines 9 and 10,

From sklearn module, import linear_model to use LinearRegression() method to create a linear regression object.

This object has a method called fit() that takes the independent and dependent values as parameters and fills the regression object with data that describes the relationship:

In line 13,

Now we have a regression object that are ready to predict ID value based on Weight and Roll_No.

### Coefficient

Coefficient is the value, defined the with the variable.

Example: 8x, where 8 is a coefficient and x is a variable.

Example: Find the coefficient values of the regression object.

```				```
import pandas as pd
from sklearn import linear_model

X = file[["Weight", "Roll_No"]]
y = file["ID"]

regression_data = linear_model.LinearRegression()
regression_data.fit(X, y)

print(regression_data.coef_)

```
```
```				```
Output -

Weight - [8.87761264e-02]
Roll_No - [7.26086069e-05]

```
```

As a result, it returned coefficient value for both “Weight” and “Roll_No.”

Example: Assumed the different values for variables Weight and Roll_No to predict ID of a student.

```				```
import pandas as pd
from sklearn import linear_model

X = file_read[["Weight", "Roll_No"]]     # independent value
y = file_read["ID"]                      # dependent value

regression = linear_model.LinearRegression()
regression.fit(X, y)                     # create linear object

# predict the Weight of a student where the Roll_No is 1500, and the ID is 4500:
predictIDvalue = regression.predict([[1500, 4500]])
print(predictIDvalue)

```
```
```				```
Output -

[201.23626454]
```
```

In the final analysis, it returned predicted value of variable ‘ID.’

If you find anything incorrect in the above-discussed topic and have any further questions, please comment below.

Connect on:

Share on