import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt # visualization
import re
%matplotlib inline

To get a brief overview, this notebook is dedicated to 5 sections as follows

Context of dataset
Data cleaning
EDA
Results
Conclusion

Loading file Hp_laptos.csv into dataframe

df=pd.read_csv('Hp_laptops.csv',encoding='unicode-escape')

Let's have glance at a bit of data

df.head()

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	Unnamed: 0	Name	actual_price	discout	display	final_price	generation	graphic_card	hard_disk	included_items	os_installed	processor	processor_company	processor_type	ram	rating
0	0	HP ENVY x360 - 13-ag0035au	83,496	10,506	13.3" FHD multitouch-enabled edge-to-edge glas...	72,990	NaN	AMD Radeon� Vega 8 Graphics	256 GB SSD	NaN	Windows 10 Home Single Language 64	AMD Ryzen� 5 processor	AMD	Ryzen� 5	8 GB DDR4-2400 SDRAM (onboard)	3.6667
1	1	HP Gaming Pavilion - 15-cx0140tx	86,476	13,486	NaN	72,990	8.0	NVIDIA® GeForce® GTX 1050 (4 GB GDDR5 dedicated)	1 TB 7200 rpm SATA	HP Odyssey backpack (Worth ?3,499),Microsoft O...	Windows 10 Home Single Language 64	8th Generation Intel® Core� i5 processor	Intel	i5	8 GB DDR4-2666 SDRAM (1 x 8 GB)	5.0000
2	2	HP Notebook - 15-da0435tx	50,292	5,712	NaN	44,580	7.0	NVIDIA® GeForce® MX110 (2 GB DDR3 dedicated)	1 TB 5400 rpm SATA	NaN	Windows 10 Home Single Language 64	7th Generation Intel® Core� i3 processor	Intel	i3	8 GB DDR4-2133 SDRAM (1 x 8 GB)	4.0000
3	3	HP Notebook - 15g-dr0006tx	66,137	7,146	NaN	58,991	8.0	NVIDIA® GeForce® MX110 (2 GB DDR3 dedicated)	1 TB 5400 rpm SATA	HP Original Laptop Bag (Worth ?1,123),1 Year O...	Windows 10 Home Single Language 64	8th Generation Intel® Core� i5 processor	Intel	i5	8 GB DDR4-2400 SDRAM (1 x 8 GB)	4.0625
4	4	HP Notebook 15-da1030tu	50,720	3,730	NaN	46,990	8.0	NaN	1 TB 5400 rpm SATA	HP Original Laptop Bag (Worth ?1,123),Microsof...	Windows 10 Home 64	8th Generation Intel® Core� i5 processor	Intel	i5	4 GB DDR4-2400 SDRAM	2.0000

Information of each column in the dataframe

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 163 entries, 0 to 162
Data columns (total 16 columns):
Unnamed: 0           163 non-null int64
Name                 163 non-null object
actual_price         163 non-null object
discout              163 non-null object
display              29 non-null object
final_price          163 non-null object
generation           131 non-null float64
graphic_card         161 non-null object
hard_disk            161 non-null object
included_items       131 non-null object
os_installed         163 non-null object
processor            149 non-null object
processor_company    146 non-null object
processor_type       146 non-null object
ram                  161 non-null object
rating               158 non-null float64
dtypes: float64(2), int64(1), object(13)
memory usage: 20.5+ KB

Shape of the dataframe

df.shape

(163, 16)

In the dataframe first column is no use at all

Dropping the first column

df.drop(['Unnamed: 0'],axis=1,inplace=True)

Data cleaning

check if any NA's are present

df.isnull().sum()

Name                   0
actual_price           0
discout                0
display              134
final_price            0
generation            32
graphic_card           2
hard_disk              2
included_items        32
os_installed           0
processor             14
processor_company     17
processor_type        17
ram                    2
rating                 5
dtype: int64

The column display has 134 NaN values which is more than 40% of total data. So we can drop it

df.drop(['display'],axis=1,inplace=True)

df.head()

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	Name	actual_price	discout	final_price	generation	graphic_card	hard_disk	included_items	os_installed	processor	processor_company	processor_type	ram	rating
0	HP ENVY x360 - 13-ag0035au	83,496	10,506	72,990	NaN	AMD Radeon� Vega 8 Graphics	256 GB SSD	NaN	Windows 10 Home Single Language 64	AMD Ryzen� 5 processor	AMD	Ryzen� 5	8 GB DDR4-2400 SDRAM (onboard)	3.6667
1	HP Gaming Pavilion - 15-cx0140tx	86,476	13,486	72,990	8.0	NVIDIA® GeForce® GTX 1050 (4 GB GDDR5 dedicated)	1 TB 7200 rpm SATA	HP Odyssey backpack (Worth ?3,499),Microsoft O...	Windows 10 Home Single Language 64	8th Generation Intel® Core� i5 processor	Intel	i5	8 GB DDR4-2666 SDRAM (1 x 8 GB)	5.0000
2	HP Notebook - 15-da0435tx	50,292	5,712	44,580	7.0	NVIDIA® GeForce® MX110 (2 GB DDR3 dedicated)	1 TB 5400 rpm SATA	NaN	Windows 10 Home Single Language 64	7th Generation Intel® Core� i3 processor	Intel	i3	8 GB DDR4-2133 SDRAM (1 x 8 GB)	4.0000
3	HP Notebook - 15g-dr0006tx	66,137	7,146	58,991	8.0	NVIDIA® GeForce® MX110 (2 GB DDR3 dedicated)	1 TB 5400 rpm SATA	HP Original Laptop Bag (Worth ?1,123),1 Year O...	Windows 10 Home Single Language 64	8th Generation Intel® Core� i5 processor	Intel	i5	8 GB DDR4-2400 SDRAM (1 x 8 GB)	4.0625
4	HP Notebook 15-da1030tu	50,720	3,730	46,990	8.0	NaN	1 TB 5400 rpm SATA	HP Original Laptop Bag (Worth ?1,123),Microsof...	Windows 10 Home 64	8th Generation Intel® Core� i5 processor	Intel	i5	4 GB DDR4-2400 SDRAM	2.0000

df.actual_price.dtype

dtype('O')

In the dataframe actual_price,final_price and discount columns should be of numerical datatype but they are of object datatype in the dataframe so function strtoint is definded below which removes the comma in the values and convert them into int datatype

def strtoint(column):
    return column.apply(lambda x:int(x.replace(',','')))

df['actual_price']=strtoint(df['actual_price'])
df['discout']=strtoint(df['discout'])
df['final_price']=strtoint(df['final_price'])

df.head()

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	Name	actual_price	discout	final_price	generation	graphic_card	hard_disk	included_items	os_installed	processor	processor_company	processor_type	ram	rating
0	HP ENVY x360 - 13-ag0035au	83496	10506	72990	NaN	AMD Radeon� Vega 8 Graphics	256 GB SSD	NaN	Windows 10 Home Single Language 64	AMD Ryzen� 5 processor	AMD	Ryzen� 5	8 GB DDR4-2400 SDRAM (onboard)	3.6667
1	HP Gaming Pavilion - 15-cx0140tx	86476	13486	72990	8.0	NVIDIA® GeForce® GTX 1050 (4 GB GDDR5 dedicated)	1 TB 7200 rpm SATA	HP Odyssey backpack (Worth ?3,499),Microsoft O...	Windows 10 Home Single Language 64	8th Generation Intel® Core� i5 processor	Intel	i5	8 GB DDR4-2666 SDRAM (1 x 8 GB)	5.0000
2	HP Notebook - 15-da0435tx	50292	5712	44580	7.0	NVIDIA® GeForce® MX110 (2 GB DDR3 dedicated)	1 TB 5400 rpm SATA	NaN	Windows 10 Home Single Language 64	7th Generation Intel® Core� i3 processor	Intel	i3	8 GB DDR4-2133 SDRAM (1 x 8 GB)	4.0000
3	HP Notebook - 15g-dr0006tx	66137	7146	58991	8.0	NVIDIA® GeForce® MX110 (2 GB DDR3 dedicated)	1 TB 5400 rpm SATA	HP Original Laptop Bag (Worth ?1,123),1 Year O...	Windows 10 Home Single Language 64	8th Generation Intel® Core� i5 processor	Intel	i5	8 GB DDR4-2400 SDRAM (1 x 8 GB)	4.0625
4	HP Notebook 15-da1030tu	50720	3730	46990	8.0	NaN	1 TB 5400 rpm SATA	HP Original Laptop Bag (Worth ?1,123),Microsof...	Windows 10 Home 64	8th Generation Intel® Core� i5 processor	Intel	i5	4 GB DDR4-2400 SDRAM	2.0000

The generation column has 32 missing values Let's see the unique values and their frequency details

df.generation.value_counts()

8.0    99
7.0    29
6.0     3
Name: generation, dtype: int64

generations are for the Intel core processor series. AMD, Intel pentium series doesn't have generation so replace those with version 1

df.generation.fillna('1.0',inplace=True)
df.generation.isnull().sum()

The column graphic_card has two null values.Let's fill them with mode of that column

df.graphic_card.value_counts()[:5]

Intel® UHD Graphics 620                                    54
Intel® HD Graphics 620                                     19
AMD Radeon� Pro WX 3100 Graphics (2 GB GDDR5 dedicated)    12
NVIDIA® Quadro® P1000 (4 GB GDDR5 dedicated)                8
NVIDIA® Quadro® P600 (4 GB GDDR5 dedicated)                 7
Name: graphic_card, dtype: int64

df.graphic_card.fillna(df.graphic_card.mode()[0],inplace=True)
df.graphic_card.isnull().sum()

df.hard_disk.value_counts()[:5]

1 TB 5400 rpm SATA        51
1 TB 7200 rpm SATA        16
512 GB PCIe® NVMe� SSD    13
512 GB SSD                13
256 GB SSD                11
Name: hard_disk, dtype: int64

df.hard_disk.fillna(df.hard_disk.mode()[0],inplace=True)
df.hard_disk.isnull().sum()

df.included_items.fillna('Not provided',inplace=True)
df.included_items.isnull().sum()

df.ram.fillna(df.ram.mode()[0],inplace=True)
df.included_items.isnull().sum()

df.rating.fillna(df.rating.mean(),inplace=True)
df.rating.isnull().sum()

df[df.processor_company.isnull()]

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	Name	actual_price	discout	final_price	generation	graphic_card	hard_disk	included_items	os_installed	processor	processor_company	processor_type	ram	rating
40	HP ZBook 15v G5 Mobile Workstation	225937	22095	203842	1.0	NVIDIA® Quadro® P600 (4 GB GDDR5 dedicated)	1 TB 7200 rpm SATA	HP Original Bag (Worth ?1,499) (#5DD44PA),3 Y...	Windows 10 Pro 64	16 GB DDR4-2666 SDRAM (1 x 16 GB)	NaN	NaN	8 GB DDR4-2400 SDRAM (1 x 8 GB)	1.194378
41	HP ZBook 15v G5 Mobile Workstation	233611	22861	210750	1.0	NVIDIA® Quadro® P600 (4 GB GDDR5 dedicated)	256 GB SSD	HP Original Bag (Worth ?1,499) (#5DD44PA),3 Y...	Windows 10 Pro 64	NaN	NaN	NaN	16 GB DDR4-2666 SDRAM (1 x 16 GB)	1.194378
42	HP ZBook 15v G5 Mobile Workstation	216245	21125	195120	1.0	NVIDIA® Quadro® P600 (4 GB GDDR5 dedicated)	1 TB 7200 rpm SATA	HP Original Bag (Worth ?1,499) (#5DD44PA),3 Y...	Windows 10 Pro 64	NaN	NaN	NaN	16 GB DDR4-2666 SDRAM (1 x 16 GB)	1.194378
43	HP ZBook 15v G5 Mobile Workstation	203281	19828	183453	1.0	NVIDIA® Quadro® P600 (4 GB GDDR5 dedicated)	2 TB 5400 rpm SATA	HP Original Bag (Worth ?1,499) (#5DD44PA),3 Y...	Windows 10 Pro 64	NaN	NaN	NaN	16 GB DDR4-2666 SDRAM (1 x 16 GB)	1.194378
44	HP ZBook 15v G5 Mobile Workstation	181301	17100	164201	1.0	NVIDIA® Quadro® P600 (4 GB GDDR5 dedicated)	1 TB 7200 rpm SATA	HP Original Bag (Worth ?1,499) (#5DD44PA),3 Y...	Windows 10 Pro 64	NaN	NaN	NaN	16 GB DDR4-2666 SDRAM (1 x 16 GB)	1.194378
106	HP ZBook 15 G5 Mobile Workstation	350406	124853	225553	1.0	NVIDIA® Quadro® P2000 (4 GB GDDR5 dedicated)	1 TB 7200 rpm SATA + 512 GB SSD	HP Business Backpack (Worth ?4,200)	Windows 10 Pro 64	NaN	NaN	NaN	16 GB (1x16 GB) DDR4 2666	0.000000
107	HP ZBook 15 G5 Mobile Workstation	316190	110771	205419	1.0	NVIDIA® Quadro® P1000 (4 GB GDDR5 dedicated)	1 TB 7200 rpm SATA + 128 GB SSD	HP Business Backpack (Worth ?4,200)	Windows 10 Pro 64	NaN	NaN	NaN	8 GB DDR4-2666 SDRAM (1 x 8 GB)	0.000000
108	HP ZBook 15 G5 Mobile Workstation	240765	83941	156824	1.0	NVIDIA® Quadro® P1000 (4 GB GDDR5 dedicated)	1 TB 7200 rpm SATA	HP Business Backpack (Worth ?4,200)	Windows 10 Pro 64	NaN	NaN	NaN	8 GB DDR4-2666 SDRAM (1 x 8 GB)	0.000000
109	HP ZBook x2 G4 Detachable Workstation	408283	145897	262386	1.0	NVIDIA® Quadro® M620 (2 GB GDDR5 dedicated)	512 GB SSD	HP Business Backpack (Worth ?4,200)	Windows 10 Pro 64	NaN	NaN	NaN	16 GB DDR4-2133 SDRAM (2 x 8 GB)	0.000000
110	HP ZBook x2 G4 Detachable Workstation	369052	128360	240692	1.0	NVIDIA® Quadro® M620 (2 GB GDDR5 dedicated)	512 GB SSD	HP Business Backpack (Worth ?4,200)	Windows 10 Pro 64	NaN	NaN	NaN	8 GB DDR4-2133 SDRAM (2 x 4 GB)	0.000000
111	HP ZBook Studio x360 G5 Convertible Workstation	400665	144380	256285	1.0	NVIDIA® Quadro® P1000 (4 GB GDDR5 dedicated)	1 TB SSD	HP Business Backpack (Worth ?4,200)	Windows 10 Pro 64	NaN	NaN	NaN	16 GB DDR4-2666 SDRAM (1 x 16 GB)	0.000000
113	HP ZBook Studio G5 Mobile Workstation	254780	89781	164999	1.0	NVIDIA® Quadro® P1000 (4 GB GDDR5 dedicated)	512 GB SSD	HP Business Backpack (Worth ?4,200)	Windows 10 Pro 64	NaN	NaN	NaN	8 GB DDR4-2666 SDRAM (1 x 8 GB)	0.000000
122	HP ProBook 640 G4 Notebook PC	95603	2830	92773	1.0	Intel® UHD Graphics 620	1 TB 7200 rpm SATA	HP Overnighter Backpack (Worth ?2,499)	8th Generation Intel® Core� i5 processor	NaN	NaN	NaN	8 GB DDR4-2400 SDRAM (1 x 8 GB)	0.000000
123	HP ProBook 640 G4 Notebook PC	106867	3164	103703	1.0	Intel® UHD Graphics 620	1 TB 7200 rpm SATA	HP Overnighter Backpack (Worth ?2,499)	Intel Core i7-8550U Processor	NaN	NaN	NaN	8 GB DDR4-2400 SDRAM (1 x 8 GB)	0.000000
126	HP ZBook 15u G4 Mobile Workstation	176453	5453	171000	1.0	AMD FirePro� W4190M Graphics (2 GB GDDR5 dedic...	1 TB 5400 rpm SATA	Not provided	Windows 10 Pro 64	16 GB DDR4-2133 SDRAM (1 x 16 GB)	NaN	NaN	8 GB DDR4-2400 SDRAM (1 x 8 GB)	0.000000
146	HP 245 G6	24200	0	24200	1.0	Intel® UHD Graphics 620	1 TB 5400 rpm SATA	Not provided	FreeDOS	BU IDS UMA A9-9425	NaN	NaN	4GB (1x4GB) 1866 DDR4	0.000000
150	HP ZBook 15u G5 Mobile Workstation	264534	103017	161517	1.0	AMD Radeon� Pro WX 3100 Graphics (2 GB GDDR5 d...	512 GB SSD	HP Business Backpack (Worth ?4,200)	Windows 10 Pro 64	NaN	NaN	NaN	16 GB DDR4-2400 SDRAM (1 x 16 GB)	0.000000

print(df.processor_type.mode()[0])
print(df.processor.mode()[0])
print(df.processor_company.mode()[0])

i5
8th Generation Intel® Core� i5 processor
Intel

df.at[38, 'processor']=df.processor.mode()[0]
df.at[120,'processor']=df.processor.mode()[0]
df.at[147,'processor']=df.processor.mode()[0]
df.processor.fillna(df.processor.mode()[0],inplace=True)
df.processor_company.fillna(df.processor_company.mode()[0],inplace=True)
df.processor_type.fillna(df.processor_type.mode()[0],inplace=True)

df.isnull().sum()

Name                 0
actual_price         0
discout              0
final_price          0
generation           0
graphic_card         0
hard_disk            0
included_items       0
os_installed         0
processor            0
processor_company    0
processor_type       0
ram                  0
rating               0
dtype: int64

creating a new column with ram size

import re
def getSize(value):
    b=re.findall(r'\b\d+',str(value))
    if len(b)>0:
        return b[0]
    return None
df['ram_size']=df['ram'].apply(getSize)

df['ram_size'].value_counts()

8     86
4     44
16    32
32     1
Name: ram_size, dtype: int64

df.ram_size.isnull().sum()

df.ram_size=df.ram_size.astype('int')

creating a new column with hard disk size

def getSize(value):
    p=re.compile(r'\b\d+\s\w+')
    val=p.findall(value)
    size=None
    if len(val)>0:
        size=val[0]
        size_val=re.findall(r'\d+',size)[0]
        if 'TB' in size:
            size_val=int(size_val)*1024
        return size_val
    return size

df['hd_size(GB)']=df.hard_disk.apply(getSize)

df['hd_size(GB)'].isnull().sum()

df['hd_size(GB)'].fillna(df['hd_size(GB)'].mode()[0],inplace=True)

df['hd_size(GB)']=df['hd_size(GB)'].astype('int')

df.head()

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	Name	actual_price	discout	final_price	generation	graphic_card	hard_disk	included_items	os_installed	processor	processor_company	processor_type	ram	rating	ram_size	hd_size(GB)
0	HP ENVY x360 - 13-ag0035au	83496	10506	72990	1.0	AMD Radeon� Vega 8 Graphics	256 GB SSD	Not provided	Windows 10 Home Single Language 64	AMD Ryzen� 5 processor	AMD	Ryzen� 5	8 GB DDR4-2400 SDRAM (onboard)	3.6667	8	256
1	HP Gaming Pavilion - 15-cx0140tx	86476	13486	72990	8	NVIDIA® GeForce® GTX 1050 (4 GB GDDR5 dedicated)	1 TB 7200 rpm SATA	HP Odyssey backpack (Worth ?3,499),Microsoft O...	Windows 10 Home Single Language 64	8th Generation Intel® Core� i5 processor	Intel	i5	8 GB DDR4-2666 SDRAM (1 x 8 GB)	5.0000	8	1024
2	HP Notebook - 15-da0435tx	50292	5712	44580	7	NVIDIA® GeForce® MX110 (2 GB DDR3 dedicated)	1 TB 5400 rpm SATA	Not provided	Windows 10 Home Single Language 64	7th Generation Intel® Core� i3 processor	Intel	i3	8 GB DDR4-2133 SDRAM (1 x 8 GB)	4.0000	8	1024
3	HP Notebook - 15g-dr0006tx	66137	7146	58991	8	NVIDIA® GeForce® MX110 (2 GB DDR3 dedicated)	1 TB 5400 rpm SATA	HP Original Laptop Bag (Worth ?1,123),1 Year O...	Windows 10 Home Single Language 64	8th Generation Intel® Core� i5 processor	Intel	i5	8 GB DDR4-2400 SDRAM (1 x 8 GB)	4.0625	8	1024
4	HP Notebook 15-da1030tu	50720	3730	46990	8	Intel® UHD Graphics 620	1 TB 5400 rpm SATA	HP Original Laptop Bag (Worth ?1,123),Microsof...	Windows 10 Home 64	8th Generation Intel® Core� i5 processor	Intel	i5	4 GB DDR4-2400 SDRAM	2.0000	4	1024

df.isnull().sum()

Name                 0
actual_price         0
discout              0
final_price          0
generation           0
graphic_card         0
hard_disk            0
included_items       0
os_installed         0
processor            0
processor_company    0
processor_type       0
ram                  0
rating               0
ram_size             0
hd_size(GB)          0
dtype: int64

creating a new .csv file

df.to_csv('Hp_laptops_new.csv')

Exploratory Data Analysis

df.actual_price.plot(kind='hist')

<matplotlib.axes._subplots.AxesSubplot at 0x1476f947cf8>

Laptops in the range of 30k-60k with 1 TB hard disk and 8GB ram (Economical range)

df[(df.final_price>30000)&(df.final_price<60000) & (df['hd_size(GB)']==1024) & (df['ram_size']==8)]

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	Name	actual_price	discout	final_price	generation	graphic_card	hard_disk	included_items	os_installed	processor	processor_company	processor_type	ram	rating	ram_size	hd_size(GB)
2	HP Notebook - 15-da0435tx	50292	5712	44580	7	NVIDIA® GeForce® MX110 (2 GB DDR3 dedicated)	1 TB 5400 rpm SATA	Not provided	Windows 10 Home Single Language 64	7th Generation Intel® Core� i3 processor	Intel	i3	8 GB DDR4-2133 SDRAM (1 x 8 GB)	4.0000	8	1024
3	HP Notebook - 15g-dr0006tx	66137	7146	58991	8	NVIDIA® GeForce® MX110 (2 GB DDR3 dedicated)	1 TB 5400 rpm SATA	HP Original Laptop Bag (Worth ?1,123),1 Year O...	Windows 10 Home Single Language 64	8th Generation Intel® Core� i5 processor	Intel	i5	8 GB DDR4-2400 SDRAM (1 x 8 GB)	4.0625	8	1024
5	HP Pavilion - 15-cw0027au	70025	10035	59990	1.0	AMD Radeon� Vega 8 Graphics	1 TB 5400 rpm SATA + 128 GB SSD	HP Trendsetter Bag (Worth ?2,356)	Windows 10 Home Single Language 64	AMD Ryzen� 5 processor	AMD	Ryzen� 5	8 GB DDR4-2400 SDRAM (1 x 8 GB)	3.0000	8	1024
48	HP 348 G4 Notebook PC	55539	2756	52783	7	Intel® HD Graphics 620	1 TB 7200 rpm SATA	HP Original Bag (Worth ?1,499) (#5DD44PA)	FreeDOS	7th Generation Intel® Core� i5 processor	Intel	i5	8 GB DDR4-2133 SDRAM (1 x 8 GB)	5.0000	8	1024
115	HP Notebook - 15-da0074tx	46010	6516	39494	7	NVIDIA® GeForce® MX110 (2 GB DDR3 dedicated)	1 TB 5400 rpm SATA	HP Original Laptop Bag (Worth ?1,123)	FreeDOS 2.0	7th Generation Intel® Core� i3 processor	Intel	i3	8 GB DDR4-2133 SDRAM (1 x 8 GB)	5.0000	8	1024
117	HP Notebook - 15-da0077tx	57786	7065	50721	8	NVIDIA® GeForce® MX110 (2 GB GDDR5 dedicated)	1 TB 5400 rpm SATA	HP Original Laptop Bag (Worth ?1,123),1 Year O...	FreeDOS 2.0	8th Generation Intel® Core� i5 processor	Intel	i5	8 GB DDR4-2400 SDRAM (1 x 8 GB)	5.0000	8	1024

which processor is used more

df.processor_company.value_counts().plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x1476fe8ae80>

Intel company processor is used more

which processor is mostly used

df.processor.value_counts().plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x1476ffea390>

df.groupby(['processor_company']).mean()

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	actual_price	discout	final_price	rating	ram_size	hd_size(GB)
processor_company
AMD	57390.000000	2974.181818	54415.818182	1.712127	5.818182	625.454545
Intel	134072.809211	22852.592105	111220.217105	1.156910	8.842105	796.894737

which os is mostly used

df.os_installed.value_counts()

Windows 10 Pro 64                                                     85
Windows 10 Home Single Language 64                                    43
FreeDOS 2.0                                                           13
Chrome OS� 64                                                          5
Windows 10 Home 64                                                     3
Windows 10 Home Single Language 64 � HP recommends Windows 10 Pro.     3
FreeDOS                                                                2
Windows 10 Pro 64 � HP recommends Windows 10 Pro.                      2
Free DOS                                                               1
FreeDos 2.0                                                            1
Windows 7 Professional 64                                              1
FreeDOS 3.0                                                            1
8th Generation Intel® Core� i5 processor                               1
Windows 10 Pro                                                         1
Intel Core i7-8550U Processor                                          1
Name: os_installed, dtype: int64

df.os_installed.value_counts().plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x147700539b0>

Does ram size effects price

df.ram_size.corr(df.actual_price)

0.76195099638699681

Those two columns have a good correlation

df.ram_size.value_counts().plot(kind='pie')

<matplotlib.axes._subplots.AxesSubplot at 0x14771156f98>

df.hard_disk.value_counts().plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x14771176668>

df.corr()

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	actual_price	discout	final_price	rating	ram_size	hd_size(GB)
actual_price	1.000000	0.856027	0.956428	-0.237846	0.761951	-0.124959
discout	0.856027	1.000000	0.667800	-0.233100	0.498787	-0.133535
final_price	0.956428	0.667800	1.000000	-0.210822	0.815429	-0.104509
rating	-0.237846	-0.233100	-0.210822	1.000000	-0.033313	0.152389
ram_size	0.761951	0.498787	0.815429	-0.033313	1.000000	-0.032704
hd_size(GB)	-0.124959	-0.133535	-0.104509	0.152389	-0.032704	1.000000

df.describe()

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	actual_price	discout	final_price	rating	ram_size	hd_size(GB)
count	163.000000	163.000000	163.000000	163.000000	163.000000	163.000000
mean	128897.895706	21511.104294	107386.791411	1.194378	8.638037	785.325153
std	100316.252958	39349.265400	69667.796932	1.831696	4.487049	511.257767
min	20999.000000	0.000000	20999.000000	0.000000	4.000000	16.000000
25%	61889.500000	1886.000000	58016.000000	0.000000	4.000000	512.000000
50%	88742.000000	3286.000000	84390.000000	0.000000	8.000000	1024.000000
75%	173869.500000	11370.000000	146606.000000	2.235350	8.000000	1024.000000
max	667152.000000	224481.000000	442671.000000	5.000000	32.000000	5400.000000

df.describe(include=[np.object]).transpose()

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	count	unique	top	freq
Name	163	93	HP ZBook 15u G5 Mobile Workstation	9
generation	163	4	8	99
graphic_card	163	38	Intel® UHD Graphics 620	56
hard_disk	163	31	1 TB 5400 rpm SATA	53
included_items	163	23	Not provided	32
os_installed	163	15	Windows 10 Pro 64	85
processor	163	30	8th Generation Intel® Core� i5 processor	58
processor_company	163	2	Intel	152
processor_type	163	16	i5	73
ram	163	33	8 GB DDR4-2400 SDRAM (1 x 8 GB)	56

print(df[df.actual_price>100000]['Name'].unique())

['HP Pavilion 14-ce1003tx' 'HP EliteBook x360 1030 G3 Notebook PC'
 'HP EliteBook 830 G5 Notebook PC' 'HP EliteBook 840r G4 Notebook PC'
 'HP ProBook x360 440 G1 Notebook PC' 'HP ZBook 15v G5 Mobile Workstation'
 'OMEN by HP - 15-dc0084tx' 'HP EliteBook x360 1030 G2'
 'HP EliteBook 1040 G4 Notebook PC'
 'HP EliteBook x360 1030 G2 (ENERGY STAR)' 'HP ProBook 430 G6 Notebook PC'
 'HP ZBook 15u G5 Mobile Workstation' 'HP Pavilion x360 14-dh0045tx'
 'HP ZBook 14u G5 Mobile Workstation' 'HP Spectre x360 - 13-ap0122tu'
 'HP Spectre x360 - 13-ap0121tu' 'HP Spectre x360 - 13-ap0102tu'
 'HP Spectre x360 - 13-ap0101tu' 'HP Spectre x360 - 13-ap0100tu'
 'HP ZBook 17 G5 Mobile Workstation' 'HP EliteBook 735 G5 Notebook PC'
 'HP EliteBook 1050 G1 Notebook PC' 'HP ZBook Studio G5 Mobile Workstation'
 'HP ZBook 15 G5 Mobile Workstation'
 'HP ZBook x2 G4 Detachable Workstation'
 'HP ZBook Studio x360 G5 Convertible Workstation' 'HP ENVY - 13-ah0044tx'
 'HP ProBook 640 G4 Notebook PC' 'HP ZBook 15u G4 Mobile Workstation'
 'HP Elite x2 1012 G1 Tablet (ENERGY STAR)' 'OMEN by HP 15-dc1006tx'
 'HP Spectre Folio - 13-ak0040tu' 'HP Elite x2 1013 G3 Tablet'
 'HP EliteBook x360 1020 G2']

Laptop with highest price

df[df.actual_price==df.actual_price.max()]

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	Name	actual_price	discout	final_price	generation	graphic_card	hard_disk	included_items	os_installed	processor	processor_company	processor_type	ram	rating	ram_size	hd_size(GB)
85	HP ZBook 17 G5 Mobile Workstation	667152	224481	442671	7	NVIDIA® Quadro® P4200 (8 GB GDDR5 dedicated)	512 GB PCIe® NVMe� SSD	Not provided	Windows 10 Pro 64	Intel® Xeon® processor	Intel	Xeon®	32 GB DDR4-2666 ECC SDRAM (2 X 16 GB)	0.0	32	512

plt.title("ram size vs actual price")
plt.xlabel("ram size")
plt.ylabel("price")
plt.scatter(df['ram_size'],df['actual_price'])

<matplotlib.collections.PathCollection at 0x14771fb99b0>

Conclusion

Intel company processors are mostly preferred
Windows 10 Pro 64 and 8th Generation Intel i5 processor is used in good number
ram size effect the price of laptops.They have good correlation 0.7
9 laptops models are there in economical range
34 laptop models are there with price greater than one lakh
1 TB 5400 RPM SATA is used for many models

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laptops_Data analysis.md

laptops_Data analysis.md

creating a new column with ram size

creating a new column with hard disk size

creating a new .csv file

Exploratory Data Analysis

Laptops in the range of 30k-60k with 1 TB hard disk and 8GB ram (Economical range)

which processor is used more

which processor is mostly used

which os is mostly used

Does ram size effects price

Those two columns have a good correlation

Conclusion

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laptops_Data analysis.md

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laptops_Data analysis.md

File metadata and controls

creating a new column with ram size

creating a new column with hard disk size

creating a new .csv file

Exploratory Data Analysis

Laptops in the range of 30k-60k with 1 TB hard disk and 8GB ram (Economical range)

which processor is used more

which processor is mostly used

which os is mostly used

Does ram size effects price

Those two columns have a good correlation

Conclusion