The first step is to import our data into python. Locate WeatherDataP.csv and copy it into your local disc under a new file called ProjectData.

**You can view the code used in this Episode here: **[**SampleCode**](https://www.kaggle.com/mazennadirahmed/polynomial-regression-example)

The first step is to import our data into python.

We can do that by going on the following link:**Data**

Click on “code” and download ZIP.

Locate WeatherDataP.csv and copy it into your local disc under a new file called **ProjectData**

Note: WeatherData.csv and WeahterDataM.csv were used inand[Simple Linear Regression_](https://medium.com/ai-in-plain-english/linear-regression-in-python-part-1-simple-linear-regression-fae7672ff552)[Multiple Linear Regression_](https://medium.com/ai-in-plain-english/implementing-multiple-linear-regression-in-python-1364fc03a5a8).

**Now we are ready to import our data into our Notebook:**

How to set up a new Notebook can be at the start of Episode 4.3

Note: Keep this medium post on a split screen so you can read and implement the code yourself.

```
## Import Pandas Library, used for data manipulation
## Import matplotlib, used to plot our data
## Import numpy for linear algebra operations
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
## Import our WeatherDataP.csv and store it in the variable rweather_data_p
weather_data_p = pd.read_csv("D:\ProjectData\WeatherDataP.csv")
## Display the data in the notebook
weather_data_p
```

In order to check what kind of relationship Pressure forms with Humidity, we plot our two variables.

```
## Set our input x to Pressure, use [[]] to convert to 2D array suitable for model input
X = weather_data_p[["Pressure (millibars)"]]
y = weather_data_p.Humidity
## Produce a scatter graph of Humidity against Pressure
plt.scatter(X, y, c = "black")
plt.xlabel("Pressure (millibars)")
plt.ylabel("Humidity")
```

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