Python Scikit-learn

R Caret

Import libraries to perfrom Extract-Transform-Load (ETL) and Exploratory Data Analysis (EDA)

The caret package (short for _C_lassification _A_nd _RE_gression _T_raining) is a set of functions that attempt to streamline the process for creating predictive models.

import pandas as pd
import seaborn as sns
import statsmodels.api as sm
import matplotlib.pyplot as plt
plt.style.use('ggplot') # if you are an R user and want to feel at home

library(readxl)
library(ggplot2)
library(corrplot)
library(tidyverse)

Load Data

power_plant = pd.read_excel("Folds5x2_pp.xlsx")

power_plant = read_excel("Folds5x2_pp.xlsx")

Exploratory Data Analysis (EDA)

This is a step that we should always perform before trying to fit a model to the data, as this step will often lead to important insights about our data.

type(power_plant)

pandas.core.frame.DataFrame

class(power_plant)

1. 'tbl_df'
2. 'tbl'
3. 'data.frame'

# See first few rows

power_plant.head()

	AT	V	AP	RH	PE
0	14.96	41.76	1024.07	73.17	463.26
1	25.18	62.96	1020.04	59.08	444.37
2	5.11	39.40	1012.16	92.14	488.56
3	20.86	57.32	1010.24	76.64	446.48
4	10.82	37.50	1009.23	96.62	473.90

# Caret faces problems working with tbl, 
# so let's change the data to simple data frame

power_plant = data.frame(power_plant)
message("The class is now ", class(power_plant))

# See first few rows
head(power_plant)

The class is now data.frame

AT	V	AP	RH	PE
14.96	41.76	1024.07	73.17	463.26
25.18	62.96	1020.04	59.08	444.37
5.11	39.40	1012.16	92.14	488.56
20.86	57.32	1010.24	76.64	446.48
10.82	37.50	1009.23	96.62	473.90
26.27	59.44	1012.23	58.77	443.67

The columns in the DataFrame are:

AT = Atmospheric Temperature in C

V = Exhaust Vacuum Speed

AP = Atmospheric Pressure

RH = Relative Humidity

PE = Power Output

Power Output is the value we are trying to predict given the measurements above.

# Size of  DataFrame

power_plant.shape   # we have 9568 rows and 5 columns

(9568, 5)

dim(power_plant)     

# we have 9568 rows and 5 columns

1. 9568
2. 5

# class of each column in the DataFrame

power_plant.dtypes    # all columns are numeric

AT    float64
V     float64
AP    float64
RH    float64
PE    float64
dtype: object

map(power_plant, class) 

# all columns are numeric

$AT

'numeric'

$V

'numeric'

$AP

'numeric'

$RH

'numeric'

$PE

'numeric'

# Are there any missing values in any of the columns?

power_plant.info()  # There is no missing data in all of the columns

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 9568 entries, 0 to 9567
Data columns (total 5 columns):
AT    9568 non-null float64
V     9568 non-null float64
AP    9568 non-null float64
RH    9568 non-null float64
PE    9568 non-null float64
dtypes: float64(5)
memory usage: 373.8 KB

# Check for missing values

map(power_plant, ~sum(is.na(.)))  # using purrr package

# There is no missing data in all of the columns

$AT

0

$V

0

$AP

0

$RH

0

$PE

0

Visualize relationship between variables

Before we perform any modeling, it is a good idea to explore correlations between the predictors and the predictand. This step can be important as it helps us to select appropriate models. If our features and the outcome are linearly related, we may start with linear regression models. However, if the relationships between the label and the features are non-linear, non-linear ensemble models such as random forest can be better.

# Correlation between power output and temperature


power_plant.plot(x ='AT', y = 'PE', kind ="scatter", 
                 figsize = [10,10],
                 color ="b", alpha = 0.3, 
                fontsize = 14)
plt.title("Temperature vs Power Output", 
          fontsize = 24, color="darkred")

plt.xlabel("Atmospheric Temperature", fontsize = 18) 

plt.ylabel("Power Output", fontsize = 18)

plt.show()

# Correlation between atmospheric temperature and 
# power output 

power_plant %>% ggplot(aes(AT, PE)) +
    geom_point(color= "blue", alpha = 0.3) +
    ggtitle("Temperature vs Power Output") +
    xlab("Atmospheric Temperature") +
    ylab("Power Output") +
    theme(plot.title = element_text(color="darkred",
                                    size=18,hjust = 0.5),
                     axis.text.y = element_text(size=12),
                      axis.text.x = element_text(size=12,hjust=.5),
                      axis.title.x = element_text(size=14),
                      axis.title.y = element_text(size=14))
  

As shown in the above figure, there is strong linear correlation between Atmospheric Temperature and Power Output.

# Correlation between Exhaust Vacuum Speed and power output 

power_plant.plot(x ='V', y = 'PE',kind ="scatter", 
                 figsize = [10,10],
                 color ="g", alpha = 0.3, 
                fontsize = 14)

plt.title("Exhaust Vacuum Speed vs Power Output", fontsize = 24, color="darkred")

plt.xlabel("Atmospheric Temperature", fontsize = 18) 

plt.ylabel("Power Output", fontsize = 18)

plt.show()

# Correlation between Exhaust Vacuum Speed
# and power output 

power_plant %>% ggplot(aes(V, PE)) +
    geom_point(color= "darkgreen", alpha = 0.3) +
    ggtitle("Exhaust Vacuum Speed vs Power Output") +
    xlab("Exhaust Vacuum Speed") +
    ylab("Power Output") +
    theme(plot.title = element_text(color="darkred",size=18,hjust = 0.5),
                     axis.text.y = element_text(size=12),
                      axis.text.x = element_text(size=12,hjust=.5),
                      axis.title.x = element_text(size=14),
                      axis.title.y = element_text(size=14))