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Mobile Price Prediction new.py
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Mobile Price Prediction new.py
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
from sklearn.preprocessing import LabelEncoder
from sklearn.impute import SimpleImputer,KNNImputer
warnings.filterwarnings('ignore')
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
lr = LinearRegression()
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
from sklearn.linear_model import Lasso
import xgboost as xgb
# In[2]:
import xgboost as xgb
# In[3]:
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
# In[4]:
df = pd.read_csv('Mobile Price Prediction.csv')
# In[5]:
df.sample(5)
# In[6]:
df.info()
# In[7]:
df1 = df.copy()
# In[8]:
df1.drop(['model','2G_bands','3G_bands','4G_bands','dimentions','weight_oz','display_size','OS','Chipset','GPU','loud_speaker','bluetooth','GPS','radio','battery','colors','img_url'],axis=1,inplace=True)
# In[9]:
df1.info()
# In[10]:
label = LabelEncoder()
# In[11]:
def encode(x):
return label.fit_transform(x)
# In[12]:
def unique(x):
return x.unique()
# In[13]:
len(list(unique(df1['brand'])))
# In[14]:
df1.NFC.unique()
# In[15]:
df1['NFC'].replace({np.nan:'No','TBD':'No','To be confirmed':'No','Region specific':'Yes','Carrier dependent':'Yes','TBC':'No','Optional':'Yes','Via software update':'No','O2 UK only':'No','NFC':'Yes'},inplace=True)
# In[16]:
df1.NFC.value_counts()
# In[17]:
nfc = df1['NFC'].str.split(' ',expand=True)[0]
# In[18]:
nfc.replace('\/','',inplace=True,regex=True)
# In[19]:
nfc.unique()
# In[20]:
df1['NFC'] = encode(nfc)
# In[21]:
df1['NFC'].unique()
# In[22]:
df1['sensors'].unique()
# In[23]:
df1['internal_memory'].unique()
# In[24]:
internal = df1['internal_memory'].str.split(' ',expand=True)[0]
# In[25]:
internal.unique()
# In[26]:
internal.replace({'ROM':158,'16/32':16000,'32':32000,'8':8000,'16':16000,'11':11000,'16/32/64':3200,'32/64':32000,'8/16':8000,'8/16/32':8000,'4/16':4000,'64/256/512':256000,'32/128':128000,'32/128/256':128000,'16/32/64/128':64000,'16/64/128':64000,'8/16/32/64':32000,'4/8/16':8000,'64/128/256':128000,'32/64/128':64000,'64/128':64000,'4/8':4000,'GB':000,'128/256':128000,'40/80':40,'16/64':16000,'2/4':2000,'Yes':10000,'1/2':512,'2/8':2000,'2/8/16':2000,'20/40':20},inplace=True)
# In[27]:
internal.replace({np.nan:100000,'No':1,'64/256':64000},inplace=True)
# In[28]:
internal.replace('GB',000,inplace=True,regex=True)
# In[29]:
internal.replace('',100000,inplace=True,regex=True)
# In[30]:
internal.replace({100000:32,10000:32},inplace=True)
# In[31]:
internal = internal.astype(float,6)
# In[32]:
internal.median()
# In[33]:
df1['internal_memory'] = internal
# In[34]:
df1.info()
# - primary camera
# In[35]:
secondary = df1['secondary_camera'].str.split(' ',expand=True)[[0,1]]
# In[36]:
secondary.sample(10)
# In[37]:
secondary[0].unique()
# In[38]:
secondary[1].unique()
# In[39]:
secondary[0].replace({'VGA/':0.3,'VGA':0.3,'QVGA':0.08,'Videocall':5,'CIF':0.2,'Videocalling':5,'8MP':8,np.nan:10000,'VGA@15fps':2,'Spy':1,'QCIF':0.5,'QCIF@15fps':1,'Dual':10000,'720p':13,'1.3MP':1.3,'HD':1.6,'No':0,'Yes':10000},inplace=True)
# In[40]:
secondary[0].unique()
# In[41]:
secondary = secondary[0].astype(float)
# In[42]:
secondary.median()
# In[43]:
secondary.mode()
# In[44]:
secondary.replace({10000:0.3},inplace=True)
# In[45]:
df1['secondary_camera'] = secondary
# In[46]:
df1.info()
# In[47]:
df1['network_technology'].unique()
# In[48]:
df2 = df1[df1['network_technology']!='No cellular connectivity']
# In[49]:
df2.reset_index(drop=True,inplace=True)
# In[50]:
df2.index
# In[51]:
df2['network_technology'].unique()
# In[52]:
network_technology = df2['network_technology'].str.split(' /',expand=True)
# In[53]:
network_technology.columns = ['network1','network2','network3','network4','network5']
# In[54]:
network_technology['network1'].unique()
# In[55]:
network_technology['network2'].unique()
# In[56]:
network_technology['network2'].replace({np.nan:'No',' HSPA':'HSPA',' UMTS':'UMTS',' EVDO':'EVDO',' CDMA':'CDMA',' LTE':'LTE',' CDMA2000':'CDMA'},inplace=True)
# In[57]:
network_technology['network2'].unique()
# In[58]:
network_technology['network3'].unique()
# In[59]:
network_technology['network3'].replace({np.nan:'No',' LTE':'LTE',' HSPA':'HSPA',' EVDO':'EVDO',' UMTS':'UMTS',' CMDA2000':'CMDA2000'},inplace=True)
# In[60]:
network_technology['network3'].unique()
# In[61]:
network_technology['network4'].unique()
# In[62]:
network_technology['network4'].replace({np.nan:'No',' LTE':'LTE',' EVDO':'EVDO',' CDMA2000':'CDMA'},inplace=True)
# In[63]:
network_technology['network4'].unique()
# In[64]:
network_technology['network5'].unique()
# In[65]:
network_technology['network5'].replace({np.nan:'No'},inplace=True)
# In[66]:
network_technology['network5'].unique()
# In[67]:
network1 = pd.DataFrame(encode(network_technology['network1']),columns=['network1'])
# In[68]:
network2 = pd.DataFrame(encode(network_technology['network2']),columns=['network2'])
# In[69]:
network3 = pd.DataFrame(encode(network_technology['network3']),columns=['network3'])
# In[70]:
network4 = pd.DataFrame(encode(network_technology['network4']),columns=['network4'])
# In[71]:
network5 = pd.DataFrame(encode(network_technology['network5']),columns=['network5'])
# In[72]:
df2.drop('network_technology',axis=1,inplace=True)
# In[73]:
df2.insert(loc=1,value=network1,column='network1')
# In[74]:
df2.insert(loc=2,value=network2,column='network2')
# In[75]:
df2.insert(loc=3,value=network3,column='network3')
# In[76]:
df2.insert(loc=4,value=network4,column='network4')
# In[77]:
df2.insert(loc=5,value=network5,column='network5')
# In[78]:
df2.info()
# In[79]:
df2['sensors'].unique()
# In[80]:
sensors = df2['sensors'].str.split('\/',expand=True)
# In[81]:
sensors[0].nunique()
# In[82]:
sensors[1].nunique()
# In[83]:
sensors[2].nunique()
# In[84]:
sensors[3].nunique()
# In[85]:
sensors[4].nunique()
# In[86]:
sensors[5].nunique()
# In[87]:
sensors[6].nunique()
# In[88]:
sensors[7].nunique()
# In[89]:
sensors[8].nunique()
# In[90]:
sensors[9].nunique()
# In[91]:
announced = (df2['announced'].str.split(' ',expand = True))
# In[92]:
year = pd.DataFrame(announced[0])
# In[93]:
year.value_counts()
# In[94]:
year.replace({'Exp.':2014,'Not':0,'2Q':2003,'3Q':2002,'':1,'2007.':2007,'2008.':2008,'2009.':2009,'2010.':2010,'2011.':2011,'1Q':2004,'4Q':2002,'Q3':2001,'Feb-01':0,'Q2':2001,'Jun-02':0,'Sep-02':0,'Oct-02':0,'Feb-02':0,'Nov-02':0,'Apr-02':0,'Apr-04':0,'Jun-03':0,'May-01':0,'May-02':0,'Mid':0,'Never':1,'Q4':2002,'2006.':2006},inplace=True)
# In[95]:
year.replace({1:2014,np.nan:2014},inplace=True)
# In[96]:
year = year.astype(int)
# In[97]:
year = year.astype(int)
# In[98]:
df2['announced'] = year
# In[99]:
df2['announced'].replace({0:2014},inplace=True)
# In[100]:
df2['announced'].value_counts()
# In[101]:
status = df2['status'].str.split(' ',expand=True)
# In[102]:
status[0].unique()
# In[103]:
status[0].replace({'Available.':'Available'},inplace=True)
# In[104]:
status[0].value_counts()
# In[105]:
status = status[0]
# In[106]:
df2['status'] = status
# In[107]:
df2['status'] = encode(df2['status'])
# In[108]:
df2.info()
# In[109]:
df2['brand'] = encode(df2['brand'])
# In[110]:
memory_card = df2['memory_card'].str.split(' ',expand=True)
# In[111]:
memory_card[0].unique()
# In[112]:
memory_card.replace({'3-in-1':1,'No':0,'Adreno':10,'To':1,'Yes':1,'Memory':1,'2':'microSD'},inplace=True)
# In[113]:
memory_card[0].unique()
# In[114]:
memory_card[0].replace({1:'microSD','MMC-micro':'microMMC',0:'No',10:'not memory card'},inplace=True)
# In[115]:
df2['memory_card'] = encode(memory_card[0])
# In[116]:
df2.info()
# In[117]:
df2['USB'].unique()
# In[118]:
df2[df2['USB']=='miniUSB (charging only)']['USB'].head()
# In[119]:
df2['USB'].replace({'USB 2.0':'USB TYPE A','miniUSB':'USB TYPE B','miniUSB 2.0':'USB TYPE B','Type-C 1.0 reversible connector':'USB TYPE C',
'Type-C 1.0 reversible connector/ USB On-The-Go':'USB TYPE C','Type-C 1.0 reversible connector/ magnetic connector':'USB TYPE C','Type-C 1.0 reversible connector/ USB On-The-Go':'USB TYPE C',
'Type-C 1.0 reversible connector/ USB On-The-Go; magnetic connector':'USB TYPE C','Type-C 1.0 reversible connector (MHL2 TV-out)':'USB TYPE C',
'Type-C 1.0 reversible connector; magnetic connector':'USB TYPE C','Type-C 1.0 reversible connector; USB Host':'USB TYPE C',
'Type-C 1.0 reversible connector; USB On-The-Go':'USB TYPE C','Type-C 1.0 reversible connector/ USB On-The-Go; magnetic connector':'USB TYPE C',
'Type-C 1.0 reversible connector; USB Host':'USB TYPE C','3.1/ Type-C 1.0 reversible connector':'USB TYPE C','3.1/ Type-C 1.0 reversible connector/ USB On-The-Go':'USB TYPE C',
'3.1/ Type-C 1.0 reversible connector; USB Host':'USB TYPE C','miniUSB (charging only)':'MINIUSB','miniUSB 2.0 (charging only)':'MINIUSB',
'miniUSB 1.1, miniUSB/ USB host':'MINIUSB','miniUSB 2.0/ USB Host':'MINIUSB','miniUSB 2.0/ USB 2.0':'MINIUSB','miniUSB 2.0/ USB 2.0':'MINIUSB',
'microUSB 2.0':'MICROUSB','microUSB 2.0/ USB On-The-Go':'MICROUSB','microUSB 2.0/ USB Host':'MICROUSB','microUSB 1.1':'MICROUSB','microUSB':'MICROUSB','microUSB (charging only)':'MICROUSB',
'microUSB (charging and headset only)':'MICROUSB','microUSB 3.0':'MICROUSB','microUSB/ USB Host':'MICROUSB','microUSB 2.0 (SlimPort)':'MICROUSB','microUSB 2.0 (SlimPort TV-out)/ USB Host':'MICROUSB',
'microUSB 2.0 (SlimPort TV-out)':'MICROUSB','microUSB 2.0 (MHL TV-out)':'MICROUSB','microUSB v2.0/ USB On-The-Go':'MICROUSB','microUSB 3.0/ USB Host':'MICROUSB','microUSB 2.0 HS':'MICROUSB',
'microUSB 2.0 (charging only)':'MICROUSB','microUSB 2.0 (SlimPort 4K)':'MICROUSB','microUSB 2.0 (SlimPort 4K)/ USB On-The-Go':'MICROUSB','microUSB 2.0 (SlimPort 4K)/ USB Host':'MICROUSB',
'microUSB 2.0 (MHL 2 TV-out)/ USB Host':'MICROUSB','microUSB 2.0 (MHL TV-out)/ USB Host':'MICROUSB','microUSB 2.0 (MHL 3 TV-out)/ USB Host':'MICROUSB',
'microUSB 2.0 (MHL 2.1 TV-out)/ USB Host':'MICROUSB','microUSB 2.0 (MHL 1.3 TV-out)/ USB Host':'MICROUSB','microUSB 3.0 (MHL TV-out)/ USB Host':'MICROUSB',
'microUSB 2.0 (MHL)':'MICROUSB',' microUSB 3.0 (MHL 2 TV-out)/ USB Host':'MICROUSB',' microUSB 2.0 (MHL 1.2)/ USB Host':'MICROUSB',
'Pop-Port':'OTHERS',' Pop-Port 2.0':'OTHERS','Pop-Port 1.1':'OTHERS','Yes/':10,'Yes (optional)':10,'Yes 2.0':10,'TBC':'NONE','1':10,'1.1 (USB host)':'MICROUSB','2.0 (TV-out)':'MICROUSB','2.0/ reversible connector':'USB TYPE C',
'2.0/ reversible connector; magnetic connector':'USB TYPE C','2.0/ Type-C 1.0 reversible connector/ USB':'USB TYPE C','microUSB 2.0/ USB 2.0':'MICROUSB'},inplace=True)
# In[120]:
df2['USB'].unique()
# In[121]:
df2['USB'].replace({'miniUSB 1.1':'MINIUSB','2.0/ Type-C 1.0 reversible connector':'USB TYPE C','3.0/ Type-C 1.0 reversible connector':'USB TYPE C',
'3.0/ reversible connector; magnetic connector':'USB TYPE C','3.0/ Type-C 1.0 reversible connector/ USB On-The-Go':'USB TYPE C',
'2.0/ Type-C 1.0 reversible connector/ USB On-The-Go':'USB TYPE C','microUSB 2.0/ USB Host/ USB OTG':'MICROUSB','3.0/ Type-C 1.0 reversible connector; magnetic connector':'USB TYPE C',
'microUSB 2.0 (MHL 3.0 TV-out)/ USB Host':'MICROUSB','microUSB 2.0 (MHL 3.0 TV-out)':'MICROUSB','Yes/ microUSB 2.0':'MICROUSB','USB host':'MICROUSB',
'microUSB 2.0 (SlimPort)/ USB Host':'MICROUSB','3.1/ Type-C 1.0 reversible connector/ magnetic connector/ USB On-The-Go':'USB TYPE C',
'Yes (charging only)':'MICROUSB','2.0/ USB host':'MICROUSB','USB 3.0/ USB Host':'MICROUSB','Pop-Port 2.0':'OTHERS','miniUSB/ USB host':'MINIUSB','microUSB 2.0; USB 2.0':'MICROUSB',
'3.1/ Type-C 1.0 reversible connector; magnetic connector':'USB TYPE C','microUSB 3.0 (MHL 2.1 TV-out)/ USB Host':'MICROUSB',
'microUSB 2.0 (MHL)/ USB Host':'MICROUSB','microUSB 2.0 (MHL 2.1)/ USB Host':'MICROUSB','microUSB 3.0 (MHL 2 TV-out)/ USB Host':'MICROUSB','microUSB 2.0 (MHL 1.2)/ USB Host':'MICROUSB',
'2.0/ USB Host':'MICROUSB','microUSB 2.0 (TV-out)/ USB Host':'MICROUSB','2.0 (TV-out)/ USB Host':'MICROUSB','2.0/ Type-C 1.0 reversible connector; USB Host':'USB TYPE C',
'microUSB 2.0 (MHL 3 TV-out)/ USB Host; magnetic connector':'MICROUSB','microUSB 2.0 (MHL TV-out)/ USB Host; magnetic connector':'MICROUSB','microUSB 2.0 (MHL 3 TV-out); magnetic connector':'MICROUSB',
'Yes (charging/ mass storage)':'MINIUSB','miniUSB 1.1/ USB host':'MINIUSB','2.0/ USB On-The-Go':'MINIUSB','charging only':'MINIUSB','microUSB 2.0 USB On-The-Go':'MICROUSB','1.1':'MICROUSB','1.2':'MICROUSB','Yes':10,'No':'None','2':'MICROUSB'},inplace=True)
# In[122]:
df2['USB'].replace({np.nan:'MICROUSB','OTHERS':'MINIUSB',10:'MINIUSB','None':'MICROUSB'},inplace=True)
# In[123]:
df2['USB'] = encode(df2['USB'])
# In[124]:
df2['USB'].unique()
# In[125]:
df2.info()
# In[126]:
df2.drop(['network_speed'],axis=1,inplace=True)
# In[127]:
df2.info()
# In[128]:
df2['sensors'].isnull().sum()
# In[129]:
sensors[0].unique()
# In[130]:
sensors[0].replace({'Accelerometer':'Motion sensor','Fingerprint (front-mounted)':'Biometric sensors1','Fingerprint (rear-mounted)':'Biometric sensors2','Fingerprint (front-mounted'
:'Biometric sensors1','Proximity':'Proximity sensor','microUSB 2.0 (SlimPort)':'Other','Compass':'Environmental sensors','Gyro':'Motion sensor','Iris scanner':'Biometric Isensors','Fingerprint (side-mounted)':'Biometric sensors3','Fingerprint (rear-mounted; region dependent)':'Biometric sensors2','Ambient light sensor':'Environmental sensors','Thermometer':'Environmental sensors','Sound level meter':'Environmental sensors','Fingerprint (side-mounted':'Biometric sensors3','Fingerprint (side-mounted; EU model only)':'Biometric sensors3','Barometer':'Environmental sensors','Fingerprint (Exclusive edition only; side-mounted)':'Biometric sensors4','Yes':'fill','No':'Nothing'},inplace=True)
# In[131]:
sensors[0].unique()
# In[132]:
sensors[0].value_counts()
# In[133]:
sensors[0].replace({'fill':sensors[0].mode()[0]},inplace=True)
# In[134]:
sensors[0].replace({'Motion sensor':0,'Biometric sensors1':1,'Biometric sensors2':2,'Biometric sensors3':3,'Biometric sensors4':4,'Environmental sensors':5,
'Proximity sensor':6,'Nothing':7,'Other':8,'Biometric Isensors':9},inplace=True)
# In[135]:
imputer = KNNImputer(n_neighbors = 3)
# In[136]:
sensors[0] = imputer.fit_transform(sensors[[0]])
# In[137]:
sensors[0].unique()
# In[138]:
df2['sensors'] = sensors[0]
# In[139]:
df2.drop(['GPRS','EDGE'],axis=1,inplace=True)
# In[140]:
df2.info()
# In[141]:
df2.head(2)
# ### weight_g
# In[142]:
df2['weight_g'].unique()
# In[143]:
df2['weight_g'].isnull().sum()
# In[144]:
df2['weight_g'].mode()
# In[145]:
df2['weight_g'].fillna(df['weight_g'].mode()[0],inplace=True)
# In[146]:
df2['weight_g'].isnull().sum()
# In[147]:
weight = df2['weight_g'].str.split('-',expand=True)
# In[148]:
weight.sample(3)
# In[149]:
weight.sort_values([0]).tail(10)
# In[150]:
weight[0].replace({'From':weight[0].mode()[0]},inplace = True)
# In[151]:
weight.sort_values([0]).tail(10)
# In[152]:
weight[1].unique()
# In[153]:
weight[weight[1]=='236']
# In[154]:
weight[0].replace({'225':((225+236)/2)},inplace=True)
# In[155]:
weight[weight[1]=='185']
# In[156]:
weight[0].replace({'165':((165+185)/2)},inplace=True)
# In[157]:
weight1= pd.DataFrame(weight[0])
# In[158]:
weight1.sample(5)