# Linear Regression using Python

Whoever wants to learn machine learning or become a data scientist, the most obvious thing to learn first time is linear regression. Linear regression is the simplest machine learning algorithm and it is generally used for forecasting. The goal of linear regression is to find a relationship between one or more independent variables and a dependent variable by fitting the best line. This best fit line is known as regression line and defined by a linear equation Y= a *X + b.

For instance, in the case of the height of children vs their age. After collecting the data of children height and their age in months, we can plot the data in a scatter plot such as in Figure below.

Linear regression will find the relationship between age as the independent variable and height as the dependent variable. Linear regression will find the best fit line from all points on the scatter plot. Finally, it can be used as a prediction, for instance, to predict what height the children when his age enter 35 months?

How to implement this linear regression in Python?

First, to make easier, I will generate a random dataset for our experiment.

```
import pandas as pd
import numpy as np

np.random.seed(0)
x = np.random.rand(100, 1) #generate random number for x variable
y = 2 + 3 * x + np.random.rand(100, 1) # generate random number of y variable
x[:10], y[:10] #Show the first 10 rows of each x and y

```

There are many ways to build a regression model, we can build it from scratch or just use the library from Python. In this example, I use scikit-learn

```
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from matplotlib import pyplot as plt

# Initialize the model
model = LinearRegression()
# Train the model - fit the data to the model
model.fit(x, y)
# Predict
y_predicted = model.predict(x)

# model evaluation
rmse = mean_squared_error(y, y_predicted)
r2 = r2_score(y, y_predicted)

# printing values
print('Slope:' ,model.coef_)
print('Intercept:', model.intercept_)
print('Root mean squared error: ', rmse)
print('R2 score: ', r2)

# plotting values
plt.scatter(x, y, s=5)
plt.xlabel('x')
plt.ylabel('y')

# predicted values
plt.plot(x, y_predicted, color='r')
plt.show()

```

Tarraaa!, it’s easy right?

See you next time

# Read the data and plotting with multiple markers

Let’s assume that we have an excel data and we want to plot it on a line chart with different markers. Why markers? just imagine, we have plotted a line chart with multiple lines using a different colour, but we only have black and white ink, after printing, all lines will be in black colour. That’s why we need markers.

For instance, our data can be seen in Table above, this is just a dummy data that tells about algorithms performance vs. the number of k. We want to plot this data to the line chart.  We already have the previous experiment, how to plot the line chart with multiple lines and multiple styles. However, in the previous experiment, we used static declaration for each line. It will be hard if we have to declare one by one for each line.

Let’s get started

The first step is to load our Excel data to the DataFrame in pandas.

```
import pandas as pd
import numpy as np

import matplotlib as mpl

xl = pd.ExcelFile("Experiment_results.xlsx")

```

It’s very easy to load Excel data to DataFrame, we can use some parameters which very useful such as sheet name, header, an index column. In this experiment, I use “Sheet2″ due to my data in the Sheet2, and I use ”1″ as the header parameter which means I want to load the header to the DataFrame, and if you don’t want to load it just fill it with ”0″. I also use index_col equal to “0”, which means I want to use the first column in my Excel dataset as the index in my DataFrame. Now we have a dataframe that can be seen in the Table above.

The second step is how to set the markers. As I said in the previous experiment that matplotlib supports a lot of markers. Of course, I don’t want to define one by one manually. Let see the code below:

```
# create valid markers from mpl.markers
valid_markers = ([item[0] for item in mpl.markers.MarkerStyle.markers.items() if
item[1] is not 'nothing' and not item[1].startswith('tick') and not item[1].startswith('caret')])

# valid_markers = mpl.markers.MarkerStyle.filled_markers

markers = np.random.choice(valid_markers, df.shape[1], replace=False)

```

Now, we have a list of markers inside the ‘markers’ variable. We need to select the markers randomly which are defined by df.shape[1] (the number of columns). Let start to plot the data.

```
ax = df.plot(kind='line')
for i, line in enumerate(ax.get_lines()):
line.set_marker(markers[i])

ax.legend(ax.get_lines(), df.columns, loc='best')
plt.show()

```

Taraaaa!!!!, it’s easy, right?

The next question is how to plot Figure like below?

Check it out here.

# Plot multiple lines on one chart with different style Python matplotlib

Sometimes we need to plot multiple lines on one chart using different styles such as dot, line, dash, or maybe with different colour as well. It is quite easy to do that in basic python plotting using matplotlib library.

```
import matplotlib.pyplot as plt
plt.plot([1,2,3,4])

# when you want to give a label
plt.xlabel('This is X label')
plt.ylabel('This is Y label')
plt.show()

```

Let’s go to the next step, several lines with different colour and different styles.

```
import numpy as np
import matplotlib.pyplot as plt

# evenly sampled time at 200ms intervals
t = np.arange(0., 5., 0.2)

# red dashes, blue squares and green triangles
plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')
plt.show()

```

If only three lines, it seems still easy, how if there are many lines, e.g: six lines.

```
import matplotlib.pyplot as plt
import numpy as np

x=np.arange(6)

fig=plt.figure()

ax.plot(x,x,c='b',marker="^",ls='--',label='Greedy',fillstyle='none')
ax.plot(x,x+1,c='g',marker=(8,2,0),ls='--',label='Greedy Heuristic')
ax.plot(x,(x+1)**2,c='k',ls='-',label='Random')
ax.plot(x,(x-1)**2,c='r',marker="v",ls='-',label='GMC')
ax.plot(x,x**2-1,c='m',marker="o",ls='--',label='KSTW',fillstyle='none')
ax.plot(x,x-1,c='k',marker="+",ls=':',label='DGYC')

plt.legend(loc=2)
plt.show()

```

Now, we can plot multiple lines with multiple styles on one chart.

These are some resources from matplotlib documentation that may useful:

1. Marker types of matplotlib https://matplotlib.org/examples/lines_bars_and_markers/marker_reference.html
2. Line styles matplotlib https://matplotlib.org/1.3.1/examples/pylab_examples/line_styles.html
3. Matplotlib marker explanation https://matplotlib.org/api/markers_api.html

In this experiment, we define each line manually while it can be hard if we want to generate line chart from dataset. In the next experiment, we use real an Excel dataset and plot the data to line chart with different markers without defining one by one for each line -> just check it here https://pydatascience.org/2017/12/05/read-the-data-and-plotting-with-multiple-markers/

*Some part of the codes, I took from StackOverflow

# Python for Data Science

I have been two years doing processing and manipulating data using R and mostly I use this language for my research project. I only heard and never tried Python for my work before. But now, after I use Python, I really fall in love with this language. Python is very simple and it is been known that this language is the easiest one to be learned. The reason why previously I used R was this language is supported by tons of libraries for scientific analysis and all of those are open source. Now, with the popularity of Python, I can find easily all libraries that I need in Python and all of them open source as well.

There are core libraries that you must know when you start to do data analytics using Python:

1. NumPy, it stands for Numerical Python. Python is different with R, the purpose of R language is for scientist. On the other side, Python is just general programming language. That’s why Python needs a library to handle numerical things such as complex arrays and matrics. Repo project link: https://github.com/numpy/numpy
2. SciPy, this library is for scientific and it handles such as statistic computing, linear algebra, optimation etc. Repo project link: https://github.com/scipy/scipy
3. Pandas, if you have experiences with R, it is very similar to DataFrame. Using DataFrame, we can easily manipulate, aggregate, and doing analysis on our dataset. The data will be shown in a table similar to Excel Spreadsheet or DataFrame in R and it convenient to access the data by columns, rows or else. Repo project link: https://github.com/pandas-dev/pandas
4. Matplotlib, Plotting is very important for data analysis. Why we need plotting? the simple answer is to make anyone easier and we know that one picture can descript 1000 words. To generate visualization from dataset, we absolutely need data visualization tools. If you have experiences with Excel, it is very easy, just block the table that you want to plot and select the plotting types such as Bar chart, line chart, etc. In R, the most popular tools for plotting is ggplot, basically, you can use standard library ‘plot’ in R but if you want more advanced and more beautiful figure you need to use ggplot.  How about in Python? Matplotlib is the basic library for visualization in Python, Repo project link: https://github.com/matplotlib/matplotlib

Those are the core basic libraries that you need when you start to use Python for data analytics. There are tons of Python libraries out there, here some of them that may useful for you:

1. SciKit-Learn, when you want to apply machine learning, you have to understand this.
2. Scrapy, to scrap the data from the Web, when you want to gather the data from websites for your analysis. For instance, collecting tweets data from Twitter.
3. NLTK, if you want to do natural language processing.
4. Theano, Tensorflow, Keras, when you are not satisfied with NumPy performance or want to apply neural network algorithms or doing deep learning stuff, you have to understand these libraries.
5. Interactive Visualization Tools, matplotlib is basic plotting tool and it is enough for me as researcher especially for publications, but when we want a dynamic plotting or more interactive, we can use Seaborn, Ploty, or Bokeh.

#### If you do not want to think too much about how to install all of those libraries, just try to use Anaconda, it is really cool.

See ya next time

Brisbane, 24 November 2017