Laptop Battery Life Challenge is an illustration which demonstrates the importance of analyzing the data before thinking of any ML solution to the problem.
The Problem is as below:
Fred charges his Laptop and uses it to watch TV shows until the battery dies. He has maintained a log which includes how long he had charged his Laptop and for how long he was able to watch the shows.
Thus the input data file has 100 lines each with 2 commas separated numbers:
1. The amount of time the Laptop was charged
2. The amount of time the battery lasted
Now, Fred wants to use this log to predict how long will he be able to watch TV based on his charging time so that he can plan his activities after watching his TV shows accordingly.
Here, I will put the code that I had run in rStudio for analysis. Once the analysis was done, the code in Hackerrank editor was quite simple.
Let's start with loading the data first.
Code:
Code:
There are 100 records and 2 variables. One is "charged_time" and the other one is "life_time". We need to predict "life_time" based on the "charged_time" value.The Problem is as below:
Fred charges his Laptop and uses it to watch TV shows until the battery dies. He has maintained a log which includes how long he had charged his Laptop and for how long he was able to watch the shows.
Thus the input data file has 100 lines each with 2 commas separated numbers:
1. The amount of time the Laptop was charged
2. The amount of time the battery lasted
Now, Fred wants to use this log to predict how long will he be able to watch TV based on his charging time so that he can plan his activities after watching his TV shows accordingly.
Here, I will put the code that I had run in rStudio for analysis. Once the analysis was done, the code in Hackerrank editor was quite simple.
Let's start with loading the data first.
Code:
# Set Working Directory
setwd("/Users/oindrilasen/WORK_AREA/Data Science/HackerRank/LaptopBattery")
# Input Format
input
glimpse(input)
class(input)
Let's take a look at the data we have so far.setwd("/Users/oindrilasen/WORK_AREA/Data Science/HackerRank/LaptopBattery")
# Input Format
input
glimpse(input)
class(input)
Now, we will convert the data in a data frame with two columns so that it gets easier to plot the data and analyze it.
Code:
m = strsplit(input,split = " ")
m = unlist(m)
m
n = length(m)
n
tmp
charged_time
life_time
for (i in 1:n){
tmp[i]
charged_time[i]
life_time[i]
}
charged_time
life_time
train_data
glimpse(train_data)
summary(train_data$charged_time)
summary(train_data$life_time)
m = unlist(m)
m
n = length(m)
n
tmp
charged_time
life_time
for (i in 1:n){
tmp[i]
charged_time[i]
life_time[i]
}
charged_time
life_time
train_data
glimpse(train_data)
summary(train_data$charged_time)
summary(train_data$life_time)
Before proceeding further let's take a quick look if there is any NA's.
Code:
# deal with NA's
sapply(train_data, function (x) sum(is.na(x)))
The data looks good. Since we have two continuous variables, I thought maybe a Linear Regression model will fit the data. I tried creating a Linear Regression Model and for the input, my predicted value was far from the expected output. So, let's plot the data first!sapply(train_data, function (x) sum(is.na(x)))
Code:
# Plot data
library(ggplot2)
ggplot(train_data, aes(x = charged_time, y = life_time)) +
geom_point() +
geom_smooth(method = "lm") +
scale_x_continuous(limits = c(0, 12), breaks = seq(0, 12, 1)) +
scale_y_continuous(limits = c(0, 8), breaks = seq(0, 8, 0.8))
Here is the plot! library(ggplot2)
ggplot(train_data, aes(x = charged_time, y = life_time)) +
geom_point() +
geom_smooth(method = "lm") +
scale_x_continuous(limits = c(0, 12), breaks = seq(0, 12, 1)) +
scale_y_continuous(limits = c(0, 8), breaks = seq(0, 8, 0.8))
The simple plot shows clearly that a linear model doesn't seem to be a good fit.
But there is a pattern in the data. If the Laptop is charged for 4 hours or more, the max life_time of the laptop is 8 hours. In other words, 4 hours can be used as a cutoff. If the Laptop is charged for less than 4 hours, the life_time seems to be directly proportional to the charged_time. But if the laptop is charged for 4 hours or more, the life_time is always 8 hours.
Here, I will divide the dataset into 2 parts. One where life_time is 8 hours and the other where life_time is less than 8 hours and then let's take a look at how the plot looks like.
Code:
# Max life_time = 8
library(dplyr)
part1_train_data
dim(part1_train_data)
# plot
ggplot(part1_train_data, aes(x = charged_time, y = life_time)) +
geom_point() +
geom_smooth(method = "lm") +
scale_x_continuous(limits = c(0, 12), breaks = seq(0, 12, 1)) +
scale_y_continuous(limits = c(0, 8), breaks = seq(0, 8, 0.8))
# Max life_time
part2_train_data
dim(part2_train_data)
ggplot(part2_train_data, aes(x = charged_time, y = life_time)) +
geom_point() +
geom_smooth(method = "lm") +
scale_x_continuous(limits = c(0, 12), breaks = seq(0, 12, 1)) +
scale_y_continuous(limits = c(0, 8), breaks = seq(0, 8, 0.8))
library(dplyr)
part1_train_data
dim(part1_train_data)
# plot
ggplot(part1_train_data, aes(x = charged_time, y = life_time)) +
geom_point() +
geom_smooth(method = "lm") +
scale_x_continuous(limits = c(0, 12), breaks = seq(0, 12, 1)) +
scale_y_continuous(limits = c(0, 8), breaks = seq(0, 8, 0.8))
# Max life_time
part2_train_data
dim(part2_train_data)
ggplot(part2_train_data, aes(x = charged_time, y = life_time)) +
geom_point() +
geom_smooth(method = "lm") +
scale_x_continuous(limits = c(0, 12), breaks = seq(0, 12, 1)) +
scale_y_continuous(limits = c(0, 8), breaks = seq(0, 8, 0.8))
| Life_time is 8 hours |
| Life_time is less than 8 hours |
Code:
calcLifeTime
if (input
output
}
else{
output
}
return(output)
}
value1
value
value2
value
Well, that's it! No ML Model or no big fat complex coding is required to solve this challenge. The data speaks everything. The code that we need to run at Hackerrank is here.if (input
output
}
else{
output
}
return(output)
}
value1
value
value2
value
Thank You for reading!
