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BUS5PA Predictive Analytics
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BUS5PA Predictive Analytics
0 Download6 Pages / 1,373 Words
Course Code: BUS5PA
University: La Trobe University
MyAssignmentHelp.com is not sponsored or endorsed by this college or university
Country: Australia
Questions:
a) Revise BUS5PA material on predictive modellingb) Demonstrate knowledge of data exploration and selection of variables to apply for the predictive modelsc) Demonstrate knowledge of building different types of predictive models using Rd) Demonstrate knowledge on comparing and evaluating different predictive modelse) Relate theoretical knowledge of predictive models and best practices to application scenarios
Answer:
Part A
The table shows the identification of variables that are continuous,ordinal and nominal from cereals’ data.
Continuous Variables
Ordinal Variable
Nominal Variable
1. Calories
2. Protein
3. Fat
4. Sodium
5. Fiber
6. Complex Carbos
7. Tot Carbo
8. Sugars
9. Calories fr Fat
10. Potassium
11. Enriched
12. Wt/serving
13. Cup/serv
1.Hot/cold
2. High/Medium/Low
1. Name
2. Categories of Manufacturers
3. Mfr
Computation of statistics:
Summary: mean, median, Max and standard deviation for each continuous variable.
Variable
Statistic Summary
Mean
Median
Max
Standard deviation
Calories
140.50
120.00
250.00
49.609
Protein
3.25
3.00
7.00
1.729
Fat
1.447
1.000
9.000
1.559
Sodium
194.9
210.0
420.0
103.13
Fiber
3.066
3.000
13.00
2.872
Complex Carbos
19.16
17.50
38.00
7.930
Tot Carbo
31.37
27.00
50.00
9.60
Sugars
9.145
11.000
20.000
5.754
Calories fr Fat
12.37
10.00
50.00
11.239
Potassium
122.0
92.5
390.0
100.030
Enriched
28.62
25.00
100.00
20.143
Wt/serving
36.65
30.00
60.00
N/A
Cup/serv
0.8911
1.0000
1.3300
0.232
Count for categorical variables.
Manufacturer
Frequency/count
America Home
1
General Mills
25
Kellogs
23
Nabisco
5
Post
10
Quaker Oats
12
Mfr
Frequency/count
A
1
G
25
K
23
N
5
P
10
Q
12
Hot/Cold
Frequency
Hot
3
Cold
73
Fiber Gr
Frequency
High
11
Medium
32
Low
33
Histogram of each continuous Variable.
Variability
Variability: is a statistic that describe that show the spread of values in a distribution(Jaeger,1990). The statistics used describe variability are range, inter-quartile range, variance and standard deviation.
Variables with the highest variability is Potassium; it has the biggest range(390), inter-quartile range(165), and highest standard deviation(100.03).
Skewed variables
Fat
Protein
Fiber
Extreme values
There are extreme values. The maximum and minimum values of some variables are far away from their median value.
Variable with missing values are
Wt/serving
Methods of handling missing values.
According to Allison (2001), the following are methods that can be used to take care of missing data.
Listwise deletion
Here the sample after erasing process id not a representation for the original sample. The probability of obtaining biased results is high.
Pairwise deletion
The outcomes are the same if the a data has two variable
This preserves statistical power in the analytical process
Examine the price of Toyota Corolla vehicle
summary(Price) Min. 1st Qu. Median Mean 3rd Qu. Max.
4350 8450 9900 10731 11950 32500
Price has inter-quartile range of 3500, range of 28150. The Median and mean prices are 9900 and 10731 respectively. Price will assume normal distribution since its sample size is greater than 30.
Missing value
There are no missing values, all data cells on R are filled and the values of the variable are at least zero.
The categorical values that need to be transformed into numerical values is Fuel-Type. The best transformation is Dummy Coding; this involve representing categorical values using dummy variables.
as.numeric(Fuel_Type)
[1] 2 2 2 2 2 2 2 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[29] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 3 2 3 2 3 3 3 3 3
[57] 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[85] 3 3 3 2 3 2 3 2 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2
[113] 2 2 2 2 2 3 2 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3
[141] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[169] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 1 2 2 2 2 2 3
[197] 2 2 3 1 3 3 3 2 2 3 3 3 3 1 2 2 3 3 2 3 2 3 3 3 3 3 3 1
[225] 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 2 3 3 3 3 3 3 3 3
[253] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 3 2 3 3 3 3 3 3 3
[281] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3
[309] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[337] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[365] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 1 2 3 2 1 2 2 2 2
[393] 3 2 3 3 2 2 3 3 3 2 2 3 3 3 2 3 3 3 3 3 2 3 3 3 2 3 2 3
[421] 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 2
[449] 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 2 3 2 3 3 3 3 3 3 3 3 3 3
[477] 3 3 3 3 2 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[505] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[533] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[561] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[589] 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 2 3 2 2 2 2 3 3 2 2 3 2
[617] 2 2 1 2 3 2 1 3 1 2 2 2 2 3 2 2 2 3 2 2 2 3 3 2 3 3 3 3
[645] 2 3 3 2 3 3 1 3 1 3 2 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3
[673] 3 3 3 3 3 3 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3
[701] 3 3 3 3 3 3 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[729] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[757] 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[785] 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[813] 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[841] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3
[869] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[897] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[925] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[953] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[981] 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3
Correlations between variables
On the R space
Regression models
Three models created from R-studio
Call:
lm(data = Toyota, Age_08_04 = Mfg_Year + KM)
Residuals:
Min 1Q Median 3Q Max
-340.42 -72.46 -15.52 64.82 518.15
Coefficients: (2 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) -7.082e+02 1.392e+02 -5.088 4.11e-07 ***
Price 1.681e-02 2.695e-03 6.238 5.86e-10 ***
Age_08_04 2.653e+01 4.774e-01 55.566 < 2e-16 ***
Mfg_Month 2.339e+01 9.649e-01 24.242 < 2e-16 ***
Mfg_Year NA NA NA NA
KM -2.853e-03 1.201e-04 -23.753 < 2e-16 ***
HP -1.467e+00 2.614e-01 -5.612 2.41e-08 ***
Met_Color 8.638e+00 6.836e+00 1.264 0.206555
Automatic -1.159e+01 1.364e+01 -0.850 0.395417
cc -1.455e-02 7.807e-03 -1.864 0.062601 .
Doors 3.894e+00 3.535e+00 1.102 0.270819
Cylinders NA NA NA NA
Gears -8.521e+01 1.788e+01 -4.767 2.07e-06 ***
Quarterly_Tax -1.207e-01 1.235e-01 -0.978 0.328298
Weight 3.996e-01 1.055e-01 3.789 0.000158 ***
Mfr_Guarantee -2.892e+01 6.708e+00 -4.312 1.73e-05 ***
BOVAG_Guarantee 5.296e+00 1.157e+01 0.458 0.647137
Guarantee_Period 5.256e+00 1.250e+00 4.206 2.76e-05 ***
ABS -9.332e+01 1.151e+01 -8.105 1.14e-15 ***
Airbag_1 -1.171e+01 2.252e+01 -0.520 0.603143
Airbag_2 5.452e+00 1.178e+01 0.463 0.643517
Airco -3.692e+00 8.145e+00 -0.453 0.650440
Automatic_airco 6.024e+01 1.843e+01 3.269 0.001104 **
Boardcomputer -1.289e+01 1.070e+01 -1.205 0.228516
CD_Player 5.004e+00 9.052e+00 0.553 0.580467
Central_Lock 2.420e+01 1.302e+01 1.858 0.063334 .
Powered_Windows -2.816e+00 1.304e+01 -0.216 0.829009
Power_Steering 4.676e+01 2.535e+01 1.844 0.065368 .
Radio -8.018e+01 6.702e+01 -1.196 0.231795
Mistlamps -3.135e+01 9.888e+00 -3.170 0.001557 **
Sport_Model 6.611e+01 7.964e+00 8.301 2.40e-16 ***
Backseat_Divider -9.657e+00 1.136e+01 -0.850 0.395467
Metallic_Rim 2.918e+01 8.618e+00 3.386 0.000729 ***
Radio_cassette 4.912e+01 6.706e+01 0.732 0.464064
Tow_Bar -6.237e-01 7.181e+00 -0.087 0.930793
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 113.7 on 1403 degrees of freedom
Multiple R-squared: 0.9272, Adjusted R-squared: 0.9255
F-statistic: 558.3 on 32 and 1403 DF, p-value: < 2.2e-16
The three models are:
lm(data = Toyota, Age_08_04 = Mfg_Year + KM)
lm(data = Toyota, Price = Age_08_04 + KM + HP)
lm(data = Toyota, KM = HP + cc + Weight)
The accurate model among the three above
lm(data = Toyota, KM = HP + cc + Weight)
KM has the least standard error among the three
Decision tree models
The first model is good for decision make as its the high pricing model.
Part(formula = Fuel_Type ~ Automatic + Gears + Price, data = data)
Has low expected loss
Comparison between Regression model and decision tree
Regression has the lower risk due to it low standard error than that of decision
Reference
Allison, P. D. (2001). Missing data (Vol. 136). Sage publications.
Jaeger, R. M. (1990). Statistics: A spectator sport (Vol. 5). Sage.
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