<jupyter_start><jupyter_text>[View in Colaboratory](https://colab.research.google.com/github/schwaaweb/aimlds1_02/blob/master/W02_A--Data_Visualization_Assignment_Master.ipynb) https://youtu.be/igOZIsa9yjY Data VisualizationVery little machine learning or data science takes place without visualizations. Visualizations help us comprehend our data, segment it, and eventually transform it in a way that allows ML algorithms to produce insights.The fundamental visualizations are:* [line charts](https://jakevdp.github.io/PythonDataScienceHandbook/04.01-simple-line-plots.html)* [scatter plots](https://jakevdp.github.io/PythonDataScienceHandbook/04.02-simple-scatter-plots.html)* [bar charts](https://jakevdp.github.io/PythonDataScienceHandbook/04.03-errorbars.html)Visualizations are useful in* 2 dimensions* [3 dimensions](https://jakevdp.github.io/PythonDataScienceHandbook/04.12-three-dimensional-plotting.html)* [images](https://matplotlib.org/users/image_tutorial.html)Visualizations are highlighted by* [color](https://jakevdp.github.io/PythonDataScienceHandbook/04.07-customizing-colorbars.html)* [axis size and spacing](https://jakevdp.github.io/PythonDataScienceHandbook/04.10-customizing-ticks.html)* [legends](https://jakevdp.github.io/PythonDataScienceHandbook/04.06-customizing-legends.html)* [annotations](https://jakevdp.github.io/PythonDataScienceHandbook/04.08-multiple-subplots.html)* [subplots](https://jakevdp.github.io/PythonDataScienceHandbook/04.08-multiple-subplots.html)In this assignment, you will use visualizations to explore the titanic and okcupid datasets, looking for linear, polynomial, or gaussian-like relationships. Every other relationship looks like noise.  AssignmentThe following dataset contains a number of hidden functions. Use bar plots, scatter plots, and line plots to explore the dataset, looking for relationships. When you discover a relationship, you will know it.It is possible to create a 2d graph for every pair of columns, and a 3d graph for every triple of columns. This would produce `10 choose 2` 2d graphs and `10 choose 3` 3d graphs. See if you can find the functions in this dataset through visualization. Make as many visualizations as you like.<jupyter_code>import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

data = pd.read_csv('https://www.dropbox.com/s/4jgheggd1dak5pw/data_visualization.csv?raw=1', index_col=0)
print(data.columns)
print(data.head(5))

# plot the data until you intuit information from it<jupyter_output>Index(['night', 'slice', 'height', 'wind', 'alpha', 'x', 'length', 'y',
       'under', 'dataset', 'interior', 'pi', 'theta', 'rho', 'nu', 'arrow',
       'delta'],
      dtype='object')
       night         slice        height          wind        alpha  \
0   7.192724   5590.286963  3.677346e+05  15874.681584   288.844905   
1   7.965880  25839.548627  7.779735e+06   9731.870015  1327.648026   
2  12.649505  29783.806651  1.036852e+07   9961.937864  1497.827365   
3   6.020414  10870.002427  1.383920e+06  13044.847041   323.124781   
4  10.966374  29566.406602  1.017405e+07   5728.689441   485.635683   

           x    length          y     under      dataset      interior  \
0  10.791882 -0.092260 -54.865187 -1.454278  9511.765099  1.028905e+10   
1   6.764308  0.153516   7.928925  0.391918  7178.690641  4.472129e+09   
2  14.004564  1.369974 -60.682680  1.091300  7298.335485  4.658803e+09   
3   9.359527 -1.176298   8.748563 -0.114204  5276.337621  1.754074e+09   
4  10.127836 -1.[...]
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