This is the **second part** and here we would be talking about **Multiple Linear Regression**.

**Questions** on all your minds:

What is Multiple Linear Regression?

It is a

**statistical technique**that uses several explanatory variables to**predict the outcome**of a response variable.**Multiple Linear Regression**is used to**estimate**the**relationship**between**two or more independent variables and one dependent variable**.

With **Multiple Linear Regression**(MLR), you can **predict** the **price** of a car, house, and more.

Before we start coding you'll need to install the dataset we're gonna use. Click here to download the dataset we're gonna use. Open the file named `50_Startups.csv`

. You should see something like this:

###
**GOAL OF THE DAY**

We're going to make a **regression model** that would be able to **predict** the **profit** of Startups.

We're gonna start **coding** now.

### Importing the libraries

```
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
```

### Load and view dataset

```
df = pd.read_csv('50_Startups.csv')
df.head()
```

**OUTPUT**

### Feature Extraction

What is Feature Extraction?

Feature extraction is a process of dimensionality reduction by which an initial set of raw data is reduced to more manageable groups for processing.

In other terms, it is the act of selecting useful features from a dataset and dumping the rest.

```
data = df[['R&D Spend', 'Administration', 'Marketing Spend']]
data = data.values.reshape(-1,3)
labels = df[['Profit']]
```

As you can see, I did not select the `State`

column to be part of the data. The reason being that it is not really necessary, and any **unnecessary data** would decrease the chances of your **model accuracy** being **high**.

### Making a Regression Model

```
model = LinearRegression()
model.fit(data,labels)
print(model.score(data,labels))
```

**OUTPUT**

`0.9507459940683246`

**TAKE NOTE**: The closer the accuracy is to `1.0`

the better it is. It increases the chances of your model's prediction being true.

### Making predictions with your model

```
print(model.predict([[165349.20, 136897.80, 471784.10]]))
print(model.predict([[144372.41, 118671.85, 383199.62]]))
```

**OUTPUT**

```
[[192521.25289008]]
[[173696.70002553]]
```

That's how simple it is. What you've done now is that you've predicted the profit of a Startup from some of their expenses.

You can visit Kaggle to find more datasets that you can perform Linear Regression on.

**Check out my** Twitter or Instagram.

**Feel free to ask questions in the comments**.

**GOOD LUCK** π

## Top comments (0)