1. Mirror mirror on the wall, help me
predict and know it all…
Ankur Sansanwal
https://www.linkedin.com/in/ankursansanwal

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Why are we here

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Lets establish the baseline
• What does this represent?
I eat this for breakfastThere is always a first time

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Lets establish the baseline
• How familiar R you with
Jedi Master
I publish R libraries
It’s a dance
studio down the
road

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Lets get ready
• Please install R Studio (GUI) or R (Shell)
– You can download R Studio from https://www.rstudio.com/ and R from
https://cran.r-project.org/bin/windows/base/
• Copy the wine.csv & wine_test.csv from the thumb drive
– This data comes from Liquid Assets
(www.liquidasset.com/winedata.html)
• This talk is inspired by Edge Analytics course work at edx.org 

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Statistics refresher before the fun part
 Independent & Dependent variable
 One Variable Linear Regression
 SSE
 SST
 R2

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What is
Dependent variable  Variable that you are tying to predict
Independent variable  Variables that you believe influence dependent variable

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• Y = 0.5 (avg. growing temp) – 1.25
How does price change based on temp?
Is this line perfect?
• Baseline model  y = 7
What is the price of wine when temp is 16 or 18 degrees?
•
One Variable Linear Regression
Avg. growing Temp
(Independent Var.)
Price(DependentVar.)
1
1
2
2

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• How do you calculate error?
The best model (line) should have minimal errors…
Avg. growing Temp
(Independent Var.)
Price(DependentVar.)
2
2
• Error = Actual value – Prediction value
= 8 – 7 = 1

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One of the measures of quality of the model is Sum of
Squared Errors (SSE)
Avg. growing Temp
(Independent Var.)
Price(DependentVar.)
2
• SSE = (e1)2 + (e2)2 + (e3)2 + (e4)2 + …………… + (en)2

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• Lets call SSE for baseline as SST
• SST = 10.15
• SSE = 6.5
The smaller the errors vs baseline the better is the model
Avg. growing Temp
(Independent Var.)
Price(DependentVar.)
1
2
• R2 = 1 – SSE / SST
= 1 – 6.5 / 10.15
= 0. 44

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But why is value of R2 always between 0 & 1?
• R2 = 1 – SSE / SST
• 0 <= SSE < = SST  Why is this the case?

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Quiz
• What would be the R2 of a perfect model?

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Remember
• Good models for easy problems will have R2 close to 1
• Good models for hard problems will have R2 close to 0

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The real deal
 Build a multi-variable regression model in R

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Step 1 – Have a look at the data (wine.csv)

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Step 2 – Ask: what's the business question we are trying to
solve?
“Predict the price of wine?”

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Quiz
• What’s the independent & dependent variable in our data-set?

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Step 3 – Check your working directory?

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Step 4 – Your working directory should be set to the
location of wine.csv file

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Step 5 – Load csv file

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Step 6 – Lets create 1 variable regression model
Name of the model
Dependent var
Independent var
Name of the data set

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Step 7 – Understand the output of the model
Error terms
Model used
R2
Adjusts R2 for number of number
of indep. var. relative to the no. of
data pts.

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Step 8 – Calculate SSE for Model1
As the name goes, sum of Squared errors

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Step 9 – Add another independent variable to the model
The new variable

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Step 10 – Compare the two models  Which is better?

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Step 11 – But lets calculate SSE for Model 2 as well….
To know which of the two models is better, compare their SSE

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Step 12 – Lets go all in…

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Step 13 – So which of the models is the best?
Model 1 Model 2 Model 3
Impendent variables AGST AGST + Harvest Rain AGST + Harvest Rain + Winter
Rain + Age + FrancePop
R2 0.43 0.70 0.82
SSE 5.73 2.97 1.73

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Step 14 – What does the output tell us about the independent
variables
Coefficient
• If a coefficient is close to 0 remove it  It means that the independent variable does
not change our prediction for dependent variable
• Larger the abs. value of t-value, the more likely the coefficient is to be significant
• Closer the value of Pr(>|t|) to 1, less significant is the independent variable
Estimate / Std. Error
How much is coef. likely to
vary from est. value
Probability of coefficient is
actually 0

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Step 15 – Lets improve the model by excluding FrancePop
(most insignificant var.)

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Refresher – What is correlation
+10-1
Highly correlatedHighly correlated No correlation

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Step 16 – ID Multicollinearity .ie. Situation when two
independent var. are highly correlated

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Are we there yet!
 Use our model to predict price of wine

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Before we do that
• Data we build our model is called train data
• Data we test our model is called test data

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Step 17 – Load test data

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Step 18 – Predict (finally)
Pretty close!

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Step 19 – Last but not the least, lets calculate R2 to quantify
how good our prediction is…

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Any intelligent fool can make things bigger, more complex, and more
violent. It takes a touch of genius -- and a lot of courage -- to move in
the opposite direction.

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Things that have worked for me
• “See” the data before you model
– I use Sublime text editor
• Define the business question – WRITE it down
• Be friend’s with ETL jedi (Data transformation expert)
• Its Ok if its simple!