Regression Intro - Practical Machine Learning Tutorial with Python p.2 Video Lecture | Machine Learning with Python - AI & ML

Alright, so now we are at least going to get started with setting up a simple linear regression example.
The first thing that we need to make sure we have is scikit learn, Pandas and Quandl. So open up terminal, command prompt, whatever.
And pip install sklearn. pip install quandl. And pip install pandas.
Once you have all those, you are good to go. So to install those, go ahead and pause the video and pick back up once you have them.
Ok, so once you have those, let's go ahead and get started with a simple example. So we are starting with regression,
and the idea of regression is to take continuous data,
and figure out a best fit line to that data.
and basically with that just boils down to
we are trying to like "model" your data
and the way we do that with regression at least with simple linear regression
is just with a straight line so the equation of the line as we will talk more about down the line
but as you might remember from school, y=mx+b,
so if you have x, you can figure out what y is, also if you have m and b.
So basically the whole point of regression is to find out what m an b is.
So for example, a lot of people use regression with stock prices so that's what we are gonna do
at least in this one.
And, so the idea is, this is continuous data and you've got months and months of stock prices and
and each price is in its own kind of unique day.
But all the data is kind of one dataset together as opposed to with like classification,
where each group of data has its own unique label.
So with machine learning, basically everything boils down, at least with supervised machine learning,
everything boils down to features and labels.
Features are like your attributes, or in this case, the continuous data.
So, let's go ahead and get started and we'll talk a little bit more about features.
So first of all, let's go ahead and import pandas as pd.
And then we are gonna import Quandl with a capital Q.
And then what we are gonna say is df for dataframe
equals Quandl.get and we'll put in the ticker.
You can get this from quandl.
So if you just go to quandl.com
You can use a little search and find stuff like
if I say google stock
we can probably find it.
Let's see I am trying to find, we are using the wiki dataset
Let's just do free. Anyway, when you find it you can find
all kinds of different datasets here but we are looking
just simply for the wiki one.
Here it is.
You will pick up a dataset and you can
come over here
You can either just download here or more importantly
here is the quandl code and then you can click on like
python and this is the exact statement to get it.
If you have an account, you can make basically unlimited
request free data. If you don't use an account,
like we are not gonna use an account here,
like we are not gonna use an auth token.
If you don't have an account, I think, it's limited like 50 calls a day.
We are actually only use quandl fairly short term here
and then maybe later on. So you really don't
need to create an account, but if you like quandl,
you might as well make an account at some point.
So anyways, quandl.get and then wiki/google was the ticker there
so then we can just simply print
let's print the df.head
just so we can see what it is we are working with.
We'll see that basically each column here
is a feature.
So the open, high, low, close, these are features.
So in machine learning you can have all the features you want but you want to have meaningful features
features that actually have something to do with your data
So some people are pretty avid believers in the ideas like
pattern recognition with stock prices
and that might be you but
do you need every single one of these
open high low close columns to do
pattern recognition? No.
Also, you would know we've got
open high low close volume and then adjusted
and adjusted is adjusted after a thing like stock splits
so a stock split
maybe your company has 10 stocks
and each stock is $1000 a share
and you decide I want people to be able to buy shares of my company for less than $1000
So you might say, ok, BAM,
every share is now two shares
so we have 20 total shares and the share price is $500
so you have adjusted prices to account for that
so it doesn't like like the stock price went from $1000 to $500
so that's what adjusted is
so we are gonna be using those
but again, each one of these is really related to the other one
like the correlation of these two columns is super high
so would you use each one of these columns
Does that the next one really brings that much meaningful data? No
but one thing to always think about
when you have features and labels is maybe like
what about the relationship between those columns
so when we get into something like deep learning and then some of the other algorithms
you can start to discover relationships between attributes
but with regression, just simply no.
what you wanna do
you wanna like simplify your data as much as possible.
You want as many meaningful features as you can get
but useless features as we'll show kinda through this series
can really cause a lot of trouble for your machine learning classifiers
especially the more simple ones in supervised learning and so on
anyways let's close out of this and let's go ahead and grab some features
what we're gonna say
first we are gonna pair this down,
we are gonna say dataframe equals the df
and then we are going to create a long list basically all of the columns that we wanna have
so we are gonna take adjusted, open, and then I'm just gonna go ahead and copy this
copy, ok
so that's adjusted, open, and then we are gonna take
oepn, so high, low, close, and volume
ok so now we have just these columns so we
kinda recreated our dataframe to just be the open high low close and volume of the adjusted ones.
so then, like I was saying, some of these columns are relatively worthless
but they do have some relationships
so for example, like what is interesting about high and low
is the margin of high and low tells us
a little bit about volatility for the day
Also, the open price that's the starting price for the day
and it's relationship to the close price
tells us did the price go up
if so, by how much
and did it go down? If so, by how much and so on.
so the relationship there is very valuable.
But a simple linear regression is not gonna seek out that
relationship. It's just gonna work with whatever
features you feed through it
so what we need to do is define those special relationships
and then use those as our features rather than
redundant almost prices that not gonna really give us
anything else very useful
first let's do the high minus the low percent
so this is like the percent volatility almost
so we are gonna define a new column
we are gonna call it HL_percent
and then that is going to be
I'm having a hard time here
that's gonna be equal to
so percent change is
in this case it would be the high minus low
divided by the low times 100
so for us it would be df Adj high minus the df Adj close
and what's happening here is just on a per row basis
which is just this column minus this column
so that column divided by df Adj close and then times 100
you can either times by 100 or not
the classifier really is not gonna care about that
we are just doing that for ourselves
So that's the high minus low percent
and then we actually want just the daily percent change
like the daily move
so I'm just gonna copy that whole line, paste
and then we are gonna call this one percent_change
and that is equal to pretty much the same thing only we need to change some stuff
so normally percent change is new minus the old divided by the old times 100
so that would be adjusted close minus adjusted open
so new minus the old
divided by the old times 100
oh, I am sorry, ha, we did it the wrong way.
divided by the old, so this would be open times 100
so that's percent change.
actually, you can pass close here again
the classifier doesn't really care as long as everything kinda normalized
but yea so either way would been fine
this is the actual way you should do it anyways
once we have that data
we are gonna define a new dataframe
and we are instead gonna say
so it's gonna be df equals df[]
and then now we define the only columns that
we really acutally care about
and so in our case the columns we care about are gonna be
adjusted close, the high low percent, the percent change
and then volume is also somewhat useful to have
so volume is just how many trades were occurred basically that day
so volume is also kinda related to volatility
so you can also make more features with some sort of relationship there
but we'll try to keep this pretty simple
so for now we'll just print df.head
and we wait just to make sure everything worked out
and sure enough it did
so we have all the numbers we are kinda interested in
so we got our features and eventually
this will actually wound up being, possibly, our label
but we'll get to..., I guess think about
between now and the next tutorial
features are the kinda of the attributes that make up the label
and the label is, hopefully, some sort of prediction
into the future
so will the adjusted close, will this column,
actually be a feature? or will it be a label
as it stands right now.
so think about that and the next tutorial
we'll pick it up and start getting closer actually
making real predictions with this data
so if you have any questions, comments, whatever, leave them below
otherwise, as always, thanks for watching, thanks for all the supports, subscriptions and until next time