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 in [Simple Linear Regression_](https://medium.com/ai-in-plain-english/linear-regression-in-python-part-1-simple-linear-regression-fae7672ff552) and [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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