Price of Flights Prediction Algorithms Based on Alibaba.ir Flights in Azar (Nov-Dec)¶

In [1]:
#libraries
import pandas as pd 
import numpy as np 
import seaborn as sns 
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split,GridSearchCV
from sklearn.metrics import accuracy_score,confusion_matrix

import warnings
warnings.filterwarnings('ignore')
In [5]:
df=pd.read_csv('df.csv')

Data Cleaning¶

In [6]:
#There were some missing values (Unannounced Flight Prices)
df.dropna(inplace=True)
df.reset_index
Out[6]:
<bound method DataFrame.reset_index of      Depart Arrival       Price     Company Destination Location        Date
0     16:40   18:10  10500000.0      coming         SYZ      MHD  01/09/2022
1     20:45   22:25  10700000.0     Caspian         SYZ      MHD  01/09/2022
2     20:45   22:25  11000000.0     Caspian         SYZ      MHD  01/09/2022
3     16:00   17:45  11000000.0  Iran Irtor         SYZ      MHD  01/09/2022
4     16:40   18:10  11000000.0      coming         SYZ      MHD  01/09/2022
...     ...     ...         ...         ...         ...      ...         ...
3623  11:40   13:10   9289000.0       Mahan         THR      SYZ  18/09/2022
3624  15:00   16:20   9289000.0   Qeshm Air         THR      SYZ  18/09/2022
3625  11:35   13:10   9288000.0       Caron         THR      SYZ  18/09/2022
3626  09:30   10:50   9289000.0    Sepahran         THR      SYZ  18/09/2022
3628  18:00   19:05   6963000.0     the sky         BND      SYZ  18/09/2022

[3157 rows x 7 columns]>
In [7]:
def cid(col):
    df[col]=pd.to_datetime(df[col],format)
In [8]:
df.columns
Out[8]:
Index(['Depart', 'Arrival', 'Price', 'Company', 'Destination', 'Location',
       'Date'],
      dtype='object')
In [9]:
df['Depart']=pd.to_datetime(df['Depart'],format='%H:%M')
df['Arrival']=pd.to_datetime(df['Arrival'],format='%H:%M')
df['Date']=pd.to_datetime(df['Date'],format='%d/%m/%Y')
In [ ]:
In [10]:
for i in ['Depart','Arrival','Date']:
    cid(i)
In [11]:
df['Price']=df['Price'].astype(int)
In [12]:
df['Time']=df['Arrival']-df['Depart']
In [13]:
df['jday']=df['Date'].dt.day
In [14]:
df
Out[14]:
Depart Arrival Price Company Destination Location Date Time jday
0 1900-01-01 16:40:00 1900-01-01 18:10:00 10500000 coming SYZ MHD 2022-09-01 0 days 01:30:00 1
1 1900-01-01 20:45:00 1900-01-01 22:25:00 10700000 Caspian SYZ MHD 2022-09-01 0 days 01:40:00 1
2 1900-01-01 20:45:00 1900-01-01 22:25:00 11000000 Caspian SYZ MHD 2022-09-01 0 days 01:40:00 1
3 1900-01-01 16:00:00 1900-01-01 17:45:00 11000000 Iran Irtor SYZ MHD 2022-09-01 0 days 01:45:00 1
4 1900-01-01 16:40:00 1900-01-01 18:10:00 11000000 coming SYZ MHD 2022-09-01 0 days 01:30:00 1
... ... ... ... ... ... ... ... ... ...
3623 1900-01-01 11:40:00 1900-01-01 13:10:00 9289000 Mahan THR SYZ 2022-09-18 0 days 01:30:00 18
3624 1900-01-01 15:00:00 1900-01-01 16:20:00 9289000 Qeshm Air THR SYZ 2022-09-18 0 days 01:20:00 18
3625 1900-01-01 11:35:00 1900-01-01 13:10:00 9288000 Caron THR SYZ 2022-09-18 0 days 01:35:00 18
3626 1900-01-01 09:30:00 1900-01-01 10:50:00 9289000 Sepahran THR SYZ 2022-09-18 0 days 01:20:00 18
3628 1900-01-01 18:00:00 1900-01-01 19:05:00 6963000 the sky BND SYZ 2022-09-18 0 days 01:05:00 18

3157 rows × 9 columns

In [15]:
def extract_hour(data,col):
    data[col+'_hour']=data[col].dt.hour
    
def extract_min(data,col):
    data[col+'_min']=data[col].dt.minute
    

def drop_col(data,col):
    data.drop(col,axis=1,inplace=True)
In [16]:
extract_hour(df,'Depart')
extract_min(df,'Depart')
drop_col(df,'Depart')
extract_hour(df,'Arrival')
extract_min(df,'Arrival')
drop_col(df,'Arrival')
In [17]:
df['Duration']=((df['Arrival_hour']*60)+40)-((df['Depart_hour']*60)+40)
In [18]:
drop_col(df,'Time')
In [19]:
df
Out[19]:
Price Company Destination Location Date jday Depart_hour Depart_min Arrival_hour Arrival_min Duration
0 10500000 coming SYZ MHD 2022-09-01 1 16 40 18 10 120
1 10700000 Caspian SYZ MHD 2022-09-01 1 20 45 22 25 120
2 11000000 Caspian SYZ MHD 2022-09-01 1 20 45 22 25 120
3 11000000 Iran Irtor SYZ MHD 2022-09-01 1 16 0 17 45 60
4 11000000 coming SYZ MHD 2022-09-01 1 16 40 18 10 120
... ... ... ... ... ... ... ... ... ... ... ...
3623 9289000 Mahan THR SYZ 2022-09-18 18 11 40 13 10 120
3624 9289000 Qeshm Air THR SYZ 2022-09-18 18 15 0 16 20 60
3625 9288000 Caron THR SYZ 2022-09-18 18 11 35 13 10 120
3626 9289000 Sepahran THR SYZ 2022-09-18 18 9 30 10 50 60
3628 6963000 the sky BND SYZ 2022-09-18 18 18 0 19 5 60

3157 rows × 11 columns

In [20]:
column=[column for column in df.columns if df[column].dtype=='object']
column
categorical = df[column]
In [21]:
continuous_col =[column for column in df.columns if df[column].dtype!='object']
continuous_col
Out[21]:
['Price',
 'Date',
 'jday',
 'Depart_hour',
 'Depart_min',
 'Arrival_hour',
 'Arrival_min',
 'Duration']
In [22]:
categorical.head()
Out[22]:
Company Destination Location
0 coming SYZ MHD
1 Caspian SYZ MHD
2 Caspian SYZ MHD
3 Iran Irtor SYZ MHD
4 coming SYZ MHD
In [23]:
categorical['Company'].value_counts()
Out[23]:
the sky       353
Kish Air      315
Zagros        285
Mahan         273
Caspian       273
Warsh         257
Iran Irtor    256
Iran Air      240
Sepahran      194
coming        167
Caron         157
Chabahar      116
Qeshm Air      76
Shining        75
Ascension      51
Saha           44
dynamic        13
Pars Air        6
Asagt           4
Fly Persia      2
Name: Company, dtype: int64
In [24]:
plt.figure(figsize=(15,8))
sns.boxplot(x='Company',y='Price',data=df.sort_values('Price',ascending=False))
Out[24]:
<AxesSubplot: xlabel='Company', ylabel='Price'>

Graph 1, shows Mahan is on average the most expensive airline to travel with and IranAir is the cheapest¶

In [25]:
plt.figure(figsize=(15,8))
sns.boxplot(x='Destination',y='Price',data=df.sort_values('Price',ascending=False))
Out[25]:
<AxesSubplot: xlabel='Destination', ylabel='Price'>

Graph 2 shows Bandar Abbas and Mahshahr are the most expensive cities to travel to¶

In [26]:
plt.figure(figsize=(15,8))
sns.boxplot(x='Location',y='Price',data=df.sort_values('Price',ascending=False))
Out[26]:
<AxesSubplot: xlabel='Location', ylabel='Price'>

Graph 3 Shows that theyre also the most expensive to travel from¶

In [27]:
# As Company is Nominal Categorical data we will perform OneHotEncoding
Airline=pd.get_dummies(categorical['Company'],drop_first=True)
In [28]:
#encoding of location and Destination columns
source=pd.get_dummies(categorical['Location'],drop_first=True)

destination=pd.get_dummies(categorical['Destination'],drop_first=True)
In [29]:
for i in categorical.columns:
    print('{} has total {} categories'.format(i,len(categorical[i].value_counts())))

Company has total 20 categories
Destination has total 27 categories
Location has total 28 categories
In [30]:
df.columns
Out[30]:
Index(['Price', 'Company', 'Destination', 'Location', 'Date', 'jday',
       'Depart_hour', 'Depart_min', 'Arrival_hour', 'Arrival_min', 'Duration'],
      dtype='object')
In [31]:
df.plot.hexbin(x='Depart_hour',y='Price',gridsize=15)
Out[31]:
<AxesSubplot: xlabel='Depart_hour', ylabel='Price'>

Graph 4 Shows Depart hour does not affect price¶

In [32]:
# Applying label encoder
from sklearn.preprocessing import LabelEncoder
encoder = LabelEncoder()
In [33]:
for i in ['Location','Destination','Company']:
    categorical[i]=encoder.fit_transform(categorical[i])
In [34]:
categorical.head(70)
Out[34]:
Company Destination Location
0 17 21 15
1 3 21 15
2 3 21 15
3 7 21 15
4 17 21 15
... ... ... ...
73 3 14 22
74 3 14 22
75 3 14 22
76 7 14 22
77 3 14 22

70 rows × 3 columns

In [35]:
#merge it back in
final_df=pd.concat([categorical,df[continuous_col]],axis=1)
In [36]:
pd.set_option('display.max_columns',500)
final_df
Out[36]:
Company Destination Location Price Date jday Depart_hour Depart_min Arrival_hour Arrival_min Duration
0 17 21 15 10500000 2022-09-01 1 16 40 18 10 120
1 3 21 15 10700000 2022-09-01 1 20 45 22 25 120
2 3 21 15 11000000 2022-09-01 1 20 45 22 25 120
3 7 21 15 11000000 2022-09-01 1 16 0 17 45 60
4 17 21 15 11000000 2022-09-01 1 16 40 18 10 120
... ... ... ... ... ... ... ... ... ... ... ...
3623 9 23 22 9289000 2022-09-18 18 11 40 13 10 120
3624 11 23 22 9289000 2022-09-18 18 15 0 16 20 60
3625 2 23 22 9288000 2022-09-18 18 11 35 13 10 120
3626 13 23 22 9289000 2022-09-18 18 9 30 10 50 60
3628 19 5 22 6963000 2022-09-18 18 18 0 19 5 60

3157 rows × 11 columns

In [37]:
#check for outliers
def plot(data,col):
    fig,(ax1,ax2)=plt.subplots(2,1)
    sns.distplot(data[col],ax=ax1)
    sns.boxplot(data[col],ax=ax2)
In [38]:
plot(final_df,'Price')

Graph 5 shows us the average price of tickets are around 10million Rials, but range from 5mill to 20mill, we can also see we have one ticket that is at 40million, we can safely consider that an outlier and eliminate it from our dataset¶

In [39]:
#As there is one outlier in price feature, we will replace it with the median
final_df['Price']=np.where(final_df['Price']>=40000000,final_df['Price'].median(),final_df['Price'])
In [40]:
plot(final_df,'Price')

Data is ready for us to start modelling¶

In [41]:
#Seperate the dataset in X and Y columns

X=final_df.drop('Price',axis=1)
y=df['Price']
In [42]:
#Feature Selection
from sklearn.feature_selection import mutual_info_classif
In [43]:
del X['Date']
In [44]:
X
Out[44]:
Company Destination Location jday Depart_hour Depart_min Arrival_hour Arrival_min Duration
0 17 21 15 1 16 40 18 10 120
1 3 21 15 1 20 45 22 25 120
2 3 21 15 1 20 45 22 25 120
3 7 21 15 1 16 0 17 45 60
4 17 21 15 1 16 40 18 10 120
... ... ... ... ... ... ... ... ... ...
3623 9 23 22 18 11 40 13 10 120
3624 11 23 22 18 15 0 16 20 60
3625 2 23 22 18 11 35 13 10 120
3626 13 23 22 18 9 30 10 50 60
3628 19 5 22 18 18 0 19 5 60

3157 rows × 9 columns

In [45]:
mutual_info_classif(X,y)
Out[45]:
array([2.17542178, 1.54024816, 1.25597602, 0.69158529, 0.93495738,
       0.70005497, 0.96351747, 0.73204306, 0.90033304])
In [46]:
imp = pd.DataFrame(mutual_info_classif(X,y),index=X.columns)
In [47]:
imp.columns=['importance']
imp.sort_values(by='importance',ascending=False)
Out[47]:
importance
Company 2.153031
Destination 1.518041
Location 1.148863
Duration 0.942153
Depart_hour 0.904052
Arrival_hour 0.898867
Depart_min 0.751783
Arrival_min 0.715833
jday 0.709763
In [48]:
# spiliting the dataset
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.30,random_state=123)
In [49]:
from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error
def predict(ml_model):
    print('Model is: {}'.format(ml_model))
    model= ml_model.fit(X_train,y_train)
    print("Training score: {}".format(model.score(X_train,y_train)))
    predictions = model.predict(X_test)
    print("Predictions are: {}".format(predictions))
    print('\n')
    r2score=r2_score(y_test,predictions) 
    print("r2 score is: {}".format(r2score))
          
    print('MAE:{}'.format(mean_absolute_error(y_test,predictions)))
    print('MSE:{}'.format(mean_squared_error(y_test,predictions)))
    print('RMSE:{}'.format(np.sqrt(mean_squared_error(y_test,predictions))))
     
    sns.distplot(y_test-predictions)
In [50]:
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import GradientBoostingRegressor,RandomForestRegressor
In [51]:
predict(RandomForestRegressor())

Model is: RandomForestRegressor()
Training score: 0.7970841337183335
Predictions are: [14889459.33333333 10643496.66666667  9716687.5        10302206.19047619
 10927847.28571429 10051396.33333333 11502090.         11620593.21428571
 12142363.85714286 10623245.5         9957648.95238095  9718507.93650793
 10963749.81746032 10006900.         10780586.30952381 12632530.
  9294140.92857143 12934583.33333334  8404638.66666667  9664557.5
 11734197.83333334 17159077.33333333 10790227.         10886043.55952381
 12739307.81746031  7316830.61111111  9464794.76190476 10499960.
  7426108.          9351510.         10246460.         10440177.88095238
 11341401.71428571 10196390.27777778 11611033.95238095 10989287.59027084
  9045690.         11626837.5         8709073.96031746 11133424.51190476
 12282510.         16914024.66666667  9240735.         12357435.
 11844154.         10825518.33333334 11249450.         12920902.14285714
 10360328.39285714  9119000.         12264876.66666666  9662706.
 12993825.         10110638.66666667 11576252.5        11283812.5
 12458925.5         8997706.66666667 10197278.03571429 10273097.5952381
 10552380.71428571 11576252.5        10313099.96428571  7509028.
 11355344.98376623 11060726.66666667 11902350.         11240303.84126984
 13579639.16666666 13918360.83333333 11780543.80952381 12144929.80952381
 13860878.14285714 11843418.33333334  9330918.85714286  9240950.
 12897248.80952381  9842746.66666667 10257302.5        11983330.
 12135977.85714286 11271276.         11388852.57142857 11433582.26190476
 11575379.52380952  9878130.          9142831.91666666 12950966.66666666
  8870145.33333333 10366109.16666667  9968363.9047619  10482490.04761905
  8680429.66666667 10330147.14285714  9654474.44444444  9336560.
 12094485.83333334 12346183.33333334  8324458.          9039510.
 11138739.28571429  9285510.         13466553.80952381 10034602.66683317
 12853342.66666667 11988342.38095238 12118865.         11652550.
  9982035.         11896141.6998557   9117235.          9654523.33333333
  9319020.         12275384.52380952  9716400.         11284094.45238095
 10577883.33333333  9079690.         11608933.66666667  8911896.66666667
  9653620.         13493310.         10090090.         10480370.
 13003490.6031746  10963749.81746032  8338750.          9616104.58333333
  9772931.42857143 11786513.33333334 12799616.66666666  9298432.
 11579491.33333333  9292485.         11284750.83333334 10453768.83333333
 10320061.66666667 12329919.66666666  9701064.66666667 10522273.33333333
  9301130.         24526754.16666667 12322810.         11113873.80952381
 11308850.39213564 10130178.75        9135243.80952381 11729502.38095238
 11801656.          8722890.          8591423.33333333  8407133.33333333
 12154030.15873016  9414648.57142857 13371266.66666667  8836308.
  9850923.2967033   7017155.          9864434.66666667 11284750.83333334
 10832981.25        9325120.         14781608.33333334 13354020.
 12522923.5        13561266.66666667  9127278.33333333  9074403.33333333
 10507665.         11373859.61111111 11146831.96969697 10034602.66683317
  9968363.9047619   9815694.66666667 12728863.38888889 11853208.66666666
  8502725.         11880147.33333333  9370616.66666667 13400203.53571429
  9296610.          9649073.80952381 13908406.9920635  13892613.33333334
 10616020.         11275520.         10437359.         10627599.33333333
  9969035.5         8586520.          9342420.          9522470.
  9835983.33333333 15167903.52380952  9414313.33333333 12202950.
 10763051.50793651 10471574.16666667 13013052.80952381 10374673.33333333
  7783054.36507936 13775385.5         9919762.22222222 12912230.
 12430710.         11041540.95238095  9016511.         11291655.
  8837142.66666667 11627141.19047619 14018098.95238095 10101430.
 12977172.5        10665402.14285714 12063797.14285714 11085322.38200688
 15649377.          9023584.66666667 10544059.11111111 15768847.14285714
 10544059.11111111 18699601.70238095  8271590.         12973878.33333333
 13187122.5        11896255.         10126218.         13146800.
  7883550.          9491047.5         8965876.66666667 10044313.80952381
 12054653.33333333 10146811.43253968 18699601.70238095 15401040.
  9810718.         13647580.         10507665.         10595121.66666667
  9716687.5        10214610.83333333  9766712.14285714 13485552.
  7881234.          9292485.          9341872.         11576265.
  9538760.         10699208.82539682 10280488.         11074206.5
 12259112.92857143  8724200.         10302206.19047619  8600580.
 13263054.16666667  9968363.9047619   9666214.28571429 10045295.71428571
 12913590.         13005072.26190476  9207895.         16091760.
  9470258.57142857  9470787.33333333 12447050.         13023280.
 12104900.33333334 13908406.9920635  11772260.         11921268.76190476
  8322910.         12114984.41666666 11952326.66666667 10221298.80952381
 17739480.          7019165.         12548842.22222222 11783047.83333333
 12021000.         11921839.         12011070.          8243596.54761905
 12636563.33333333 12675888.57142857 11979150.         13632521.33333334
 10957847.29731053 10035199.42857143 10732625.15873016 12443740.
 12738379.48412698 10612468.44444444 10675496.62837163 15983501.66666666
  9571558.45238095  9182717.         12938585.4047619  10222308.66666667
  8804524.28571429  9627750.         10987027.5        11237596.66666666
  9988256.66666667 11674351.32142857 10478714.         10967343.3015873
  7427598.          9901290.95238095 12549914.33333333 13466553.80952381
 13474231.44047619  9119000.         12716728.27380952 12686377.14285714
 10689325.3968254  10478714.         11732620.         12575883.33333333
 10840849.         10224520.         10444690.         10825518.33333334
 10166660.         13639977.76190476  8845987.5         8586520.
 10708952.5        12684225.          8723471.5        11235722.5
 12653412.66666667  8879935.33333333 10380802.28571429  8879935.33333333
 12954090.         13422127.22222222  9349363.33333333 10889415.0952381
 11625662.73809524 10108875.83333333 14341578.22222222 13023859.65151515
  7167086.         11641710.4448052  15767340.11904762  8873335.35714286
 11595327.72619048 10452427.83333333 12574687.48809524  9182658.
 11774483.33333333 13422127.22222222 10644981.         11705980.
 12221088.5952381  12424236.66666666  9625770.          9827744.83333333
  9172363.33333333  9061616.66666667 13572179.16666666 12021128.66666666
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 10090641.         14275880.          9587048.         12341436.66666666
 11538818.33333334 10006900.         10473476.5        10140369.09090909
 12448213.61111111 12733360.         11111056.66666667 12081027.5
 14679230.83333333 10280256.         10617892.54761905 12006900.
 10190624.12698413 12341436.66666666  7262686.66666667 11458130.
  9589652.          7377198.          9134779.30952381 11864354.28571429
 10411850.         14889459.33333333  8076330.          9142831.91666666
 13639977.76190476  9729514.28571429 10532387.42063492 11795469.19047619
 12513479.16666667  9916440.         12728863.38888889  9764151.33333333
 11840185.23809524  9778957.5        10221891.11111111  9235997.88888889
 11731518.33333333  9299040.         10604430.95238095 10120080.
  9458113.83333333  9553470.         10108875.83333333  7061740.
  8236494.11111111 11669331.95238095 10141272.          9365011.33333333
  9647040.         10532387.42063492  9149968.42857143  9350210.
 12570326.66666667 10609477.5        12764556.66666666  9297670.56956932
  8387071.66666667  9403569.         11617391.9047619   9364910.
 12766849.78571429  9031264.         10689325.3968254  11880147.33333333
 10006900.         11647653.33333334  9305803.33333333 12822765.
  9571558.45238095 12675176.66666667 15927539.         10213476.66666667
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 12934583.33333334 11023106.15873016  9982075.          9559041.66666667
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 10586763.33333333 11874740.43176268  9344810.         12938585.4047619
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  9694619.66666667 10895014.66666666 11739857.93650793 15927539.
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 11351682.33333334 12718142.75793651 11283920.55555556 10076734.24603175
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 11314970.         13026050.         10273097.5952381   9969490.
 12707603.33333334 10778218.          9054923.88888889  9816721.33333333
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  9450903.5        12636563.33333333 11136232.          9781157.
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  9254758.00793651 11946786.66666666  9429947.5        10391836.66666667
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  9718507.93650793  9756181.66666667 11332914.4047619   9965238.66666667
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  9516506.66666667  7873620.         13352175.         11465118.
 10964741.16666666 10539134.28571429  9318620.         10185866.66666667
  7045880.          9849463.33333333 11321572.76298701 12848001.66666667
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  9965238.66666667 11746478.         12737484.28571429  9330488.
 11701090.         11376768.88888889  9603300.          7984030.33333333
 10627467.88347764 10268411.66666667 12517788.02991453 12594099.57142857
 11373859.61111111 13059766.66666667 10763051.50793651  9211190.
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 10109455.01190476 10924932.16666666 12397831.33333333 10076734.24603175
  9184596.66666667  9316320.          9464794.76190476 12194494.47619048
 10158260.         11990000.71428571  8743221.66666667 13908406.9920635
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 10689912.          9462180.6547619   9669943.          9698371.66666667
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  9372120.         13175510.         11941123.33333334  9958925.10714286
  9172896.66666667 11416553.82936508 16196130.66666667  8586520.
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  9383006.66666667  9830851.83333333 12355503.33333333 10715761.61904762
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  9547650.60714286 12033890.          9863847.04761905  9853064.04761905
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 11880147.33333333 10385240.28571429 10442113.         10435721.66666667
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  9119000.          8872130.          9968363.9047619  10307402.
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 10392044.16666667 10106411.63636364 12114984.41666666 10061476.82539682
 11275520.          8119960.         13377820.         10011435.23809524
 10414780.          9074403.33333333 11513832.         19157525.
 12382313.33333333 15523153.76190476 13020860.         13600773.91666666
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  9217940.          8590180.75       11976525.          8726180.
  9808741.66666667 13196845.         12154000.42857143  8900880.
  9341740.         11259527.5        13317332.14285714 15311012.42857143
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 10957847.29731053 11353688.14285714 10007473.          9235997.88888889
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 13282992.71428571 10573830.         10223290.         11729502.38095238
 15806340.         10221298.80952381 10193839.40909091 11958289.45970696
  9567363.33333333 11434567.28571429 15732551.23809524  7200250.
 11677541.66666666 10471574.16666667  9774926.66666667 12556342.28571429
  9923201.42857143 11373588.47619048 12450305.83333334 12448213.61111111
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  9096570.          8591626.         12570326.66666667 12267711.66666667
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 18803715.55555556  8968733.33333333 10718559.66666667  8923613.33333333
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  9538760.         12223682.6984127  12547583.33333333 10817654.28571429
 13631587.97619048 10162891.14285714  9842903.17460318 11308850.39213564
 10118500.         11878190.09163059 10675496.62837163 11857710.
 12317223.33333334 13517793.33333333 10419672.95238095 10967343.3015873
  8819524.38095238 12892793.33333334 12448213.61111111 13282580.
  8514940.         10620797.61904762 11641710.4448052  12028454.
 12950673.07622933 12977172.5        12712115.71428572 10335711.
 12439898.57142857  9703664.35714286  9638178.33333333  8926072.
 11682270.         13228214.66666666 10765220.83333333 13694435.
 12238987.61904762  8926146.13636364 12046626.66666666 12287593.33333334
 11407900.          9939913.98809524 12622620.         13035961.36363637
 13547145.         10963749.81746032  8226428.57142857 12401963.33333334]


r2 score is: 0.30763242758792075
MAE:1244970.2878049996
MSE:4267821088437.6025
RMSE:2065870.5400962576
In [52]:
predict(LogisticRegression())

Model is: LogisticRegression()
Training score: 0.13173381620642824
Predictions are: [10191000  9500000 10191000 10191000 13671000 11000000 12500000 10191000
 10191000 10191000 10191000 10191000 10191000 13152000 10191000 10191000
  6963000 10191000  6963000 13152000 13152000 10191000 12000000 10191000
 10191000 10191000 10191000 10191000 10191000  9357000 10138000 10191000
 12000000  6963000 13030000 13671000  9289000  9908000 10191000 10191000
 11900000 10191000  8862000 12500000 10191000 10191000 11900000 10191000
 12000000  9844000 10191000 13152000 10191000 10191000 11000000  8900000
 12500000  9357000 10191000 10191000 10191000 11000000 10167000 10191000
 13500000  9289000 10191000 10191000 10191000 10191000 11000000  9289000
 11000000 11900000 10191000 10191000  9500000  9357000 13030000 10191000
  9357000 10191000 10191000 12000000 11000000 10191000 10191000  8287000
 11000000 13152000 11000000 10191000 12000000 10191000 10191000 10191000
  9357000 13152000 10191000 10191000 10191000 11900000  8287000  9289000
 13152000 13030000 11900000 10191000 10191000 10191000  9844000  9357000
  9357000 10191000  9289000  9289000 10191000 11900000 13030000  9357000
 10191000 10191000 11000000 10191000 13152000 10191000 10191000  9357000
  9357000 11900000 10191000 10191000 11900000 10191000 10191000  6963000
  6963000 10191000 10191000 10191000  9357000  9844000  9357000 10191000
 10191000 11000000 13152000  6963000 13152000 10191000 11000000 10240000
 10191000  9289000 13030000 10191000  9357000  6963000 10191000 10191000
 10191000  9357000  6963000 13152000 13152000  9844000 10191000  9357000
 13030000 13030000  9844000  9289000 11000000 10191000 10191000 10191000
 10191000  9289000 10191000 11900000  6963000 10191000  9289000  6963000
 10191000 10191000 13152000 10191000 12500000  9500000  9289000  9289000
 10191000 10240000 10191000 10191000 10191000 10191000 13671000 10191000
 13152000 13152000  9500000 10191000 11900000 10191000  8862000  9221000
 10191000 10191000 10191000 10191000 10191000  9357000 10191000 10191000
 10191000 10191000 10191000 10191000 10191000 10167000 11900000  9357000
 10191000 10191000 13152000 13671000  6963000 10138000  9289000  9500000
 13152000 13415000 10167000 10191000 10191000 13671000 13030000  6963000
 10191000 10191000 10191000 10191000 12500000 10191000  9357000 13152000
 10191000 10191000 10191000 13671000 10191000  6963000 10191000 10191000
  9357000 11000000 11900000 12500000 10191000  9357000 10191000  9357000
 10191000 10191000  9357000 10191000 13152000  9289000 10191000 13671000
  9500000  6963000 10191000 10191000 10191000  6963000 10191000  9357000
 10191000 12000000 10191000 10191000  9844000  9289000  9357000 13152000
 11000000 12500000 10191000 10191000 12000000 10191000 10191000 11000000
  9289000  9357000  6963000 10191000 10191000 10191000  9357000 10191000
 10191000 10191000 13152000 10191000 10191000 10191000 11900000  8287000
 10191000  9844000  9289000 10191000 10191000 13152000 10191000  9357000
 10191000 10191000 13030000 10191000 13030000 10191000  9357000  9500000
 13152000 12500000  9357000 10191000  9357000 10191000 10191000 10191000
 10191000 10191000 10191000 13671000 11000000 10191000 13152000 11900000
 10191000 13415000 18856000  9500000 18856000 10191000 10191000 10191000
 10240000 10191000 10191000  6963000  9357000 10191000  9844000 12000000
 10191000 11900000 10191000 10167000  9289000 11000000 10191000  9357000
 10191000 10191000  9357000 13152000 10191000 13152000 10191000 10191000
 10191000 10191000 12000000 10191000 10191000 10191000 10191000 10191000
 10191000 13152000 10191000 12500000  9289000 10191000  9357000 10191000
 10191000 10191000  9844000 10191000 10191000 10191000 10191000 18856000
 18856000  9289000 10191000  9289000  9289000 10167000  9357000 10191000
 10191000  9357000  6963000 10191000  9357000 10191000 10191000 10191000
 10191000 10240000 10191000 10191000 10240000 10191000 10191000  9357000
  6963000 10191000 10191000 10191000 13152000  9289000 11000000  9289000
 10191000  9500000 10191000  9289000 13152000 11900000 10191000 10191000
  9289000 12000000 10167000 10191000 13152000 10191000 10191000 10191000
 13152000 11900000 10191000 13152000 10191000 10191000 10191000 10191000
 10191000 13152000  9357000  9289000 10191000 10191000 18074000 11900000
 13671000 11400000  9357000 12000000 13152000 10191000 10191000  6963000
 10191000 10167000  9357000  6963000  9289000 10191000 12957000 10191000
  9357000  9357000  9357000 13152000 10191000 10191000  9844000 10191000
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  9357000 10191000 10191000 10191000 13152000  6963000 10191000 11900000
 12000000 10191000  9357000 10191000 10191000 10191000 10191000  9357000
  9357000  9357000 10191000 10191000 10191000  9357000  9357000 10191000
 13030000 10191000 10191000 10191000 10240000  9289000 13415000 10191000
  9357000 10191000  9500000 11900000 11000000 10191000 10191000 10191000
 13030000 13152000 10191000 10191000 10191000 13030000 13671000  6963000
  9357000 10191000  9289000 10240000 10191000  9289000 10191000 10167000
 10191000 10191000 10191000 11900000 10191000  9289000 13152000  9357000
 18856000 10191000 13152000 10191000  6963000 10191000 10191000 11000000
 10191000 10191000 10191000 10191000  6963000 10191000  6963000  9357000
 10191000 10191000  9289000 10191000  9289000 10191000 13152000 10167000
 13152000 10240000 12500000  9357000 10191000 10191000 10191000 10191000
 10191000 13415000 10191000  9844000 10191000 10191000  9289000 10191000
 12500000 11000000 12000000 10191000 10191000 10191000 10191000  9289000
 10191000 10191000 10191000  9289000 13152000 13152000  9357000 10191000
  9357000  9844000  9289000  9289000 10191000 13152000 10191000 10175000
  9357000  9289000 11000000  9357000 10191000 11900000  9357000 10191000
  9357000 13152000  8287000 13415000 10191000 11000000 10191000  9500000
 12000000 10191000 10191000 10191000 10191000  9357000 10167000 10191000
  9357000 10240000 12500000 10191000 13152000 13030000  9357000 10191000
 13030000 10191000 10191000  9844000 12957000 11000000  6963000 10191000
 10191000 11000000 18856000 10191000 10191000 10191000 10191000 10191000
  9500000 10191000 12000000  9357000 10191000  9357000 10191000 10191000
  9908000  6963000 10191000  9289000 13030000 10191000 10191000 10191000
  6963000  9844000 10240000 12000000 10191000 10191000 11900000  9357000
 10191000  9357000 10191000 10167000  9500000  9289000 10191000  9289000
 10191000 10191000 10191000  9844000 11900000 10191000  9289000 10138000
 10191000  9357000 10191000 10191000  9357000 12957000  9357000 10191000
 10191000 11900000 13030000  9500000  9289000  9289000 10191000 10191000
  9289000  9357000 10191000  9844000  9289000 13152000 12000000 10240000
 12500000 10191000 10191000  9357000 11000000 11900000 12500000  9844000
 10191000 10191000 10191000 13152000  9289000 10191000 10191000 11900000
 10191000 10191000  9357000 10191000 11900000 11900000  9357000  9357000
  9844000  8862000 11000000 10191000  6963000 10191000 11000000  9357000
 11000000 11400000  6963000  9357000 10191000 11000000 12500000 10191000
 13030000  9357000  9357000 10191000 10191000 13152000 13000000 11900000
 11000000  9908000 13671000  9357000  6963000 10191000 10191000 10191000
 10191000  9908000  9357000 13152000  6963000 18074000 10191000 12500000
 10191000 13671000 11900000 10191000  9357000 10191000  9289000 10240000
 11900000  8862000 13415000 10191000 10191000 13152000 10191000 10191000
 11900000 10191000 10191000  6963000 11000000 10191000 13152000 10191000
 11000000 10191000 10191000 10191000 12500000  6963000  9357000  6963000
  9357000 10191000 11000000  9357000 13152000 13152000 10191000 10191000
 10191000 10191000 10191000 10191000 10191000 13152000 10191000 10191000
 10191000  9357000  9289000 10191000  9357000 13152000  6963000 12500000
 10191000 10191000  9289000 12500000 10191000 11000000 10191000 10191000
 10191000 10191000 10191000 10191000 10191000  9357000  9289000  9844000
 11000000 10191000 18856000  9357000 11900000 10191000  6963000  9357000
 10191000 13152000 10191000 10191000 10191000  9357000 10191000 12500000
 10167000 10191000 10191000 10191000 10191000  9500000 10191000 10191000
 12000000 10191000 10191000 10191000 10191000 10191000 10191000 10191000
 10191000  9289000 10191000 10191000 10191000 10191000 10191000 13671000
 13000000 10191000 13415000 13152000  9357000 10191000 10191000 11000000
 10191000  9357000 10191000 10240000 10191000  6963000 10191000 13671000
 10191000 10191000 10191000 10191000 10191000 10191000 10191000  9357000
 13671000 10191000 10191000 10191000]


r2 score is: 0.9999732795124189
MAE:1990928.270042194
MSE:164707685.53586498
RMSE:12833.849209643418
In [53]:
predict(KNeighborsRegressor())

Model is: KNeighborsRegressor()
Training score: 0.46169451387003313
Predictions are: [14940400. 12250600. 12628200.  9273400.  9706600.  9981000. 11171000.
  9728800. 11624600. 12155800.  9686200.  9860000. 10783600. 12004600.
 12246800. 12060000.  9351800. 11936400.  8956800. 11680000. 10985400.
 14177600. 10405600.  9469400. 13814200.  8622600. 10060000. 10229400.
  7978000.  9302000. 10900200. 11059000. 13373600. 10174600. 10769600.
 10540000.  9030200. 10970800.  7815800.  9469400. 11507000. 15292400.
  9216200. 12278000. 11141200.  9460000. 10466400. 11724000. 12433200.
  9284200. 13373600. 10572000. 13826800.  9769600. 11493400. 11360000.
 11527400.  9749200.  9164400. 10752000. 11200000. 11493400. 11414000.
  8352400. 13373600. 11376000. 12100000. 11749400. 13265600. 11337800.
 11195000. 10701800. 12537200. 11520000. 11806000.  9738800. 13159400.
 10691400. 10869600. 12100000. 11349400. 10445000. 11220800. 12880000.
 11095000.  7782200.  9548400. 12583000.  9565400. 11345200.  9986800.
 12918200. 13006200. 11479000. 10110000.  9289000. 12667400. 12475000.
  8158360. 10042200. 11937800. 10408200. 13307600. 10120400. 12027800.
 12107200. 12020000. 11578200. 10635000. 11140800. 10077600. 10768600.
  9302000. 11378000.  9026800. 10476000. 10790000.  8489600. 10927200.
  9049600.  8896000.  9526800.  9739000. 11564200. 11905800. 10783600.
  7711000.  8857800. 11059000. 11800000. 12518000. 10500000. 11520000.
  8908400. 11698200.  9695800. 10172400. 11481400.  8080000. 11498000.
  9286200. 22470800. 12762000. 10501200. 11365400. 10871400.  7787800.
 11771400. 11406600.  8621000.  9000400.  9031200. 12260000.  9813200.
 12378200.  7954400.  8599000.  6963000.  9054000. 11698200. 11433200.
  9256800. 13106200. 13320000. 12350000. 12369000.  9416000.  9142800.
 11596000. 10869600. 10051600. 10120400.  9986800.  6942200. 11897800.
 12000000.  7337000. 10358600.  9469400. 13316400.  9520000. 10374400.
 13976400. 15001000. 11398200. 11038200. 10365200. 10191000. 10152800.
  9114200.  9289000. 13124600.  9640000. 14940400. 10338400. 12866200.
 10060000. 11924000. 13038600. 10073200.  9501000. 13838000. 10522000.
 12255800. 10479000. 10892000.  8862000. 12385000.  9618200. 11792000.
 13492200.  9350200. 11636400. 10690000. 12758000. 10945400. 15292400.
 12020000. 10907000. 11824600. 10907000. 22636000.  9940000. 11789800.
 12329400. 12180000. 10790600. 13038600.  9076400.  9772800. 10088000.
  9920400. 12595800. 10904800. 22636000. 13373600. 10194600. 13671000.
 11596000. 13009600. 12628200. 11215000.  9461000. 10722360.  8846800.
  8908400.  9336600. 12428400.  8613800. 11280400.  9806600. 11206200.
 11182600.  7875400.  9273400.  9838200. 14417600.  9986800. 10861400.
 10961000. 13030000. 13034600. 10526600. 13373600.  9289000.  9469400.
 13373600. 14317800. 10661000. 13976400. 10955000. 11206200.  8982200.
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  8245800.  8356400. 12336000.  9326400.  8856400.  8899800.  8111800.
  9984600.  9545000.  7337000.  8764000. 13373600. 12147400. 10338400.
  9221200. 10582000.  9257000.  9526200. 14147600. 11479000. 13799200.
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 11796800. 10408200. 10540000.  9491800. 12762000.  8845400.  8399400.
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 10361200. 12391800. 12189800. 12921200.  9286200. 13533400. 13373600.
 10528600.  9269200. 12581000.  9169400.  8249600. 11940000. 11466400.
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  8824200. 13236400. 14135600. 11380000.  9378000. 10800000. 13030000.
  9286200.  8851000. 10030600. 15292400. 11314600. 13740000. 10166600.
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 11143000.  8245800.  9356800.  9391200. 11001600. 10999000.  9797800.
  8599000. 12877000. 11806000.  8998000. 13456800. 10500000.  8866200.
 13373600.  9916200. 13368000.  8613800.  9034200. 10900000.  9920400.
 11520000. 11060000. 13544000. 10401800. 10190600. 10048400. 10788000.
 12049800. 13319400. 12277200. 11745800. 13038600.  7920600. 14749600.
 10190600. 12626000. 13373600. 10325800. 11010400. 11140000. 22636000.
 12036000. 11626000. 14168000.  9940000.  8317200. 11481200. 11500000.
 12035600. 11715800. 12866200.  9387200. 11220800. 14177600.  9558200.
 12794600. 10048600.  9545400. 11657800. 13504800. 10191000. 12961200.
 11924000.  9987600. 10050600. 11824400.  9269200. 10088000. 10089000.
 11826000. 12168600. 10301000. 11001400. 10382600. 11806800.  7788000.
  9142800.  8744000. 10254200.  9918000. 12094400.  9273400. 11995800.
 12363800. 17033600. 11363000. 11735000. 10008600. 11288800. 11818000.
  7963000.  9930800.  9467000. 10320000. 13024000. 10752000. 10008600.
 15598400. 10183200.  9770800.  9673000. 13922400. 12917200. 13034600.
 13030000.  9239200. 13410000.  9469400.  9916200. 11976600. 11597600.
 11924000. 16457000. 10672600. 10704000.  9565400.  8037600.  9686200.
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  9860000.  9137400.  9903600.  9920400. 10688200. 11175600.  9144200.
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 12404000. 10738200. 11500000. 10048400.  9346800. 11628000.  7767000.
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  9797800. 10405000. 24619200. 10320000. 12391800. 11792000. 10778000.
 11363000.  9920400. 11976600. 12156200.  8878000. 12175400. 11596800.
 11385600.  9468800. 10325400. 10175000. 12456200. 11336200. 10869600.
 11936400. 10060000.  9431200. 12594600. 10864800. 14940400. 11650400.
 10969000. 10778000. 12999000. 10050600. 10447000.  9194200. 10060000.
 16656200.  9920400. 10361200.  9412000. 13976400. 12085000. 10240200.
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  9297000. 12277200. 11331800.  9073600. 11915800. 12162400. 10152800.
  9378000. 10764800. 13307600.  9114200. 14405800. 11398200. 11335600.
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 10358600. 10191000. 11537800. 11554200. 14317800. 13805000.  8969400.
 11628000. 12999000. 10764800. 12594600.  8599000.  9284200.  8862000.
  9986800. 11766400. 11771400.  8120000.  9565400.  9602800.  9972400.
  9491800. 12594600. 12272000. 11038200.  8830200. 13166800.  9164400.
 11596000.  9142800.  9170600. 14486000. 13371200. 14640600. 13236400.
 13316400. 11560000. 10554200. 13038600. 12373000. 14177600. 12756000.
 10398600.  9377800.  9164400. 11010800. 12272000. 10427200.  9221000.
  8380000. 10924000.  9814600. 10593200. 13038600. 11275800. 11505600.
  8850400. 11001600. 14396600. 16507200. 12069600.  8862000. 12538800.
  8938800. 12198000. 10631400.  8850800. 11182600. 11520000. 12363800.
  9472400.  9939600. 11060000. 11481200. 10622600.  8356400. 11060000.
 11780600. 10900000. 12581000. 15292400. 10191000.  9712400. 11771400.
 13373600. 10500000. 12760000. 11432000.  9428600. 11697800. 14168000.
  7879600. 11890000. 11924000. 10467600. 14096200. 10116800.  9632800.
 11972800. 12427200. 13814200.  7884000. 10260200. 11990000.  8990600.
  8594800. 14147600. 12404000. 13855800. 10360000. 12842400. 10625400.
 10924000. 11493400. 12456200.  9014400. 13622400. 10076400. 11902400.
 11265400. 10623000.  9256800.  7718000. 12572200.  9981000. 10230800.
 11924000.  8296000. 11035200. 10732800.  9357000.  7867200. 10191000.
 11500000. 12604400. 10191000. 16245400.  9060000. 12546200.  7832600.
 11194000. 13191000.  9769600. 10783600.  8613800. 11537800. 11940000.
 11562800. 11347600. 11058600. 11275000. 11365400.  8254200. 11876600.
 10600000. 12735600. 12924000. 14072000. 10219400. 10614400. 10194800.
 12278200. 12427200. 13671000.  9805600. 10900000. 11435800. 12256000.
 12272000. 11636400. 13805000. 11035400. 13307600.  9602800.  9268200.
  8352000. 12580000. 11549600. 12254600. 13582200. 12156200.  8915600.
 10465000. 11760000. 11618600. 10820000.  9796200. 12292800. 13671000.
 10783600.  8100000. 13371200.]


r2 score is: 0.02178032644261385
MAE:1602561.434599156
MSE:6029841255257.384
RMSE:2455573.508420667
In [54]:
predict(DecisionTreeRegressor())

Model is: DecisionTreeRegressor()
Training score: 0.8286279132200205
Predictions are: [18074000.         10240000.          9642500.         10738000.
  6963000.          9600000.         11000000.         11134000.
 11951666.66666667  9700000.         10738000.          9860000.
 10931750.         10000000.         10931750.         13593000.
  9384500.         13000000.          7900000.          9200000.
 12000000.         18074000.          9567500.          9289000.
 10190000.          6940000.          9150000.         10738000.
  6940000.          9356000.         10167000.         10363000.
 10240000.         10181000.         11827000.         10987833.33333333
  9000000.         13227000.          8669666.66666667  9289000.
 13000000.         18074000.          8862000.         11900000.
 13792000.         11948000.         11400000.         12948333.33333333
  8900000.          8911000.         10240000.          6963000.
 13016000.         10191500.         10191000.         11200000.
 13592000.          9357000.         10600000.         10415000.
 10000000.         10191000.         10190000.          6963000.
 10500000.         10700000.         12000000.         11400000.
 13500000.         18903000.          9540000.         13415000.
 13562000.         11900000.          9150000.          8832000.
 13150000.          9800000.         10000000.         12000000.
 14267000.          6963000.          9221000.         10425000.
 13000000.         10191000.          9105500.         11500000.
  8600000.         10000000.          9941750.         10191000.
  6535000.          9275000.          9800000.          9289000.
 13546500.         10000000.          8833000.          8833000.
 12000000.          8425000.         10167000.         10008666.66666667
 11400000.         12500000.         11900000.         12000000.
 10191000.         11000000.          6940000.          9340000.
  9356000.         13030000.          9357000.         11864000.
 10450000.          8862000.         13792000.          8862000.
 10000000.         15680000.          9289000.         10191000.
 14049000.         10931750.          8833000.          9357000.
  9675000.         11400000.         12000000.          9000000.
 11400000.          9289000.         10191000.         10191000.
 10181000.         12621500.         10191000.         10167000.
  9306000.         10180000.         12275500.         13593000.
 11304500.         10191000.          8807000.         11766666.66666667
 13792000.          8833000.          8600000.          7300000.
 12133333.33333333  9033000.         13350000.          6940000.
 10621000.          6963000.         10191000.         10191000.
 10175000.         20150000.         15720000.         13900000.
 13050000.         13150000.          9820000.          9100000.
 10240000.         11510500.         10793000.         10008666.66666667
  9941750.         10191000.         12000000.         12500000.
  8833000.         14049000.          6940000.         13248750.
  9300000.          9000000.         13976400.         16845000.
 10191000.         10515000.         10138000.         10191000.
 10000000.          8533333.33333333  9289000.          8287000.
  9800000.         15462666.66666667  9289000.         13016000.
  9150000.         10191000.         13671000.          9288000.
  6963000.         13900000.          9540000.         13593000.
 12707000.         10240000.          8862000.         11155000.
  8340000.         11666666.66666667 14208333.33333333 10191000.
 13300000.          8295000.         12200000.         11079142.85714286
 15462666.66666667  9200000.         12500000.         18903000.
 12500000.          9200000.          6960000.         13152000.
 13590000.         12000000.          9500000.         13671000.
  7420000.          9500000.          8505000.         10191000.
 12000000.          9844000.          9200000.         18074000.
 10000000.         13671000.         10240000.          9880000.
  9642500.         10191000.          9275000.         15666000.
  6950000.          9289000.          9357000.         11343000.
  9289000.         10175000.         10191000.         11500000.
 13590000.          8807000.         10738000.          8000000.
 10191000.          9941750.          8600000.          8417000.
 13030000.         13086333.33333333  8833000.         18074000.
 10191000.          9289000.         10240000.         13000000.
 12439000.         13976400.         12000000.         12081333.33333333
  8020000.         12386166.66666667 13792000.         10000000.
 18856000.          6963000.         16248000.         13150000.
 11860000.         13593000.         13532000.          7540000.
 12000000.          8904000.         10240000.         13152000.
 10962500.         10000000.         10558333.33333333 14045000.
 10191000.         10191000.         10666666.66666667 18856000.
  9781250.          7900000.         13152000.         10191000.
  8666666.66666667  9780000.         10240000.         12000000.
 10191000.         11531000.         10191500.         11000000.
  6940000.          9289000.         12325500.         10167000.
 13152000.          8911000.         13976400.         11900000.
 10700000.         10191500.         13590000.         13152000.
 10738000.         10191000.         10191000.         11948000.
 10400000.         14019666.66666667  8295000.          8533333.33333333
 10694000.         12995000.          8599000.         11400000.
 13151000.          8460000.         10000000.          8460000.
 13532000.         13450000.          9289000.          9844000.
 11827000.         10190500.         14437333.33333333 12988400.
  6130000.         11744750.         15967666.66666667  8500000.
 11200000.         13000000.         13030000.          9129666.66666667
  6940000.         13450000.         11000000.         13152000.
 13213000.         10425000.         10181000.          8862000.
  8833000.         12800000.         13500000.         13227000.
 13152000.         11610800.         10191000.         18074000.
 10191000.         15790000.          9306000.         12500000.
 10500000.         10000000.         10000000.         10000000.
 12427200.         13414000.          9275000.         13000000.
 14813333.33333333 10191000.         11285000.         11500000.
 10000000.         12500000.          6940000.         16845000.
  9680000.          6963000.          6963000.         11948000.
 10191000.         18074000.          6963000.          9105500.
 14019666.66666667 10191000.         10700000.         11000000.
 11400000.          9289000.         12000000.         10191000.
 11330333.33333333 10138000.          9357000.          8669666.66666667
 11000000.          9306000.         11000000.         10191000.
  9357000.          8911000.         10190500.          6963000.
  7900000.         10240000.          9844000.          9289000.
  9844000.         10700000.          9289000.          9357000.
 12200000.         10191000.         13532000.          9328400.
  6963000.          9289000.         11827000.          9289000.
 12763000.          9900000.         10700000.         14049000.
 10000000.         11400000.          9289000.         13414000.
  9781250.         12500000.         11200000.         10191000.
  9500000.         10191000.         10605000.         10735000.
 13152000.         13248750.         11800000.         11400000.
 13000000.         10175000.         10191000.          9100000.
 12000000.         11343000.         10086250.          9289000.
 11100000.          9860000.         10190000.          8425000.
 10987833.33333333 10363000.         12275500.          6130000.
  6963000.         10200000.         10621000.          9221000.
 10191000.         11879714.28571429  9356000.         13152000.
 12120000.         13200000.         10950000.         10190000.
  9306000.         12000000.         18074000.         10191000.
  9780000.         12948333.33333333  7695000.          6940000.
 10000000.         12825000.         10191000.          8979000.
 13227000.         13000000.          6963000.          9844000.
  9357000.          9289000.         12970000.          8660000.
 11380000.          6940000.         11000000.         13030000.
  9306000.         10000000.         10191000.         18074000.
 10660000.         11864000.          6900000.         11277000.
 10191000.         11000000.         12223000.          8980000.
 18856000.         12641000.         10191000.          9357000.
  9357000.          9306000.         10191000.         11430000.
  9700000.          8599000.          9221000.          9150000.
 10190000.          9288000.         10000000.          7548000.
 14157000.          9800000.         14672500.          9289000.
  8909666.66666667  9500000.         10191000.         12400000.
 10962500.         11860000.         11000000.         10191000.
  9400000.          9000000.          8904000.          8000000.
  9400000.         10288000.         13671000.          6963000.
 18903000.         10190666.66666667 11330333.33333333 14157000.
 11430000.         10621000.         12030000.         11200000.
 11000000.         12948333.33333333 18856000.          6960000.
  6963000.         13590000.         13543000.          6960000.
 11000000.         11948000.          9889428.57142857  9221000.
 14157000.         10191000.         10830000.         10167000.
 10191000.         10000000.         11500000.         10191000.
 10240000.         13079333.33333333  6940000.         10086250.
 13593000.          9780000.          8505000.         10845000.
 12950000.          9246000.          9844000.         13227000.
 10971000.         10240000.          8807000.          9100000.
 10191000.          9780000.         11800000.          9246000.
 10738000.         13000000.         12641000.         13543000.
 12000000.         11330333.33333333  9790000.         10191000.
 11200000.          8303000.          6940000.          8951000.
 11400000.         10167000.         10415000.         10191000.
 13000000.         10240000.          8900000.          8505000.
 13152000.         13485000.         11400000.         13030000.
  9300000.         12000000.          9289000.          9800000.
 12120000.         10167000.         10191000.          8295000.
 10672600.         11100000.          8600000.          9357000.
  6940000.         11400000.          9357000.         10600000.
 10086250.         10167000.          9866666.66666667 14672500.
  9860000.          7540000.         11827000.         10191000.
 10191000.         13304666.66666667  8460000.         10191000.
 14045000.          8862000.         10167000.         12168000.
  8862000.          7353000.         13592000.         11000000.
 10750000.          9400000.          9306000.          9820000.
  6963000.         10191000.         11315200.         14500000.
 10950000.          9540000.         15080000.          9289000.
  9700000.          9890000.         11200000.         11400000.
 13500000.         13304666.66666667  8287000.         12000000.
 10191000.         12120000.         10694000.          9356000.
 11500000.         10893000.          8911000.          6963000.
 10607714.28571429 10181000.         12386166.66666667 13415000.
 11510500.         13150000.          9150000.          6940000.
 12223000.         10180000.         18074000.         17620000.
 10191000.          8287000.         12386000.         10086250.
  6963000.          9357000.          9150000.         13229000.
 10191000.         12120000.          8340000.         13976400.
 12500000.          9289000.         12000000.         10180000.
 10709666.66666667  8833000.         10138800.         10191000.
 11000000.          9357000.          9400000.         10191000.
  9357000.         13979000.         11333333.33333333 10000000.
  8833000.         11384250.         20150000.          8533333.33333333
 14500000.         13590000.         11335600.         15562000.
  6940000.          9340000.         12450000.         10167000.
 13976400.         13152000.         13150000.         10240000.
 11000000.         12000000.         18074000.         12007500.
  9594000.         11900000.          9833333.33333333 10524000.
 13000000.         13000000.         11827000.         10900000.
 14049000.         10191000.         10191000.          9844000.
 13000000.         14208333.33333333  9357000.         13400000.
 12325500.         11384250.         12223000.          8599000.
  8911000.          8862000.          9941750.         10190000.
 11766666.66666667  9000000.          8600000.          9357000.
 10191000.         10363000.         12386166.66666667  9357000.
 10515000.          7980000.         13590000.         10191000.
 10240000.          9100000.         13213000.         21660000.
 12250000.         15551000.         12970000.         13587000.
 12900000.          7900000.         13671000.         12373000.
 14157000.         13152000.         12500000.         10000000.
 10191000.         13227000.         12853333.33333333  6963000.
  9221000.          6963000.         11500000.          9500000.
  9275000.         13671000.         12265000.          8807000.
  9356000.         10191000.         13587000.         15462666.66666667
 13152000.          8862000.         13000000.          7540000.
 13590000.         12663000.         10000000.         13590000.
 11900000.         12641000.         10000000.          9289000.
 10962500.         10508000.         10181000.          8669666.66666667
 10962500.         11000000.         10750000.         12948333.33333333
 10987833.33333333 10738000.         13000000.         11766666.66666667
 18074000.         10000000.         10000000.         12007500.
  7920000.         10694000.         18856000.          6940000.
 11000000.         10191000.          9275000.         10240000.
  9820000.         13593000.         13900000.         12427200.
 10190000.          8700000.         12440000.         10200000.
  9357000.          6963000.         12200000.         13592000.
 13000000.         11864000.         13592000.         11277000.
 11500000.         10191000.         12386166.66666667 15666000.
 10500000.         10000000.         10240000.         11000000.
 10175000.          9207000.          9000000.         13592000.
  9600000.         10718000.         10191000.          9289000.
 12386000.          8807000.          9357000.          9357000.
 10191000.         10750000.         10191000.         10191000.
 18966666.66666667  6960000.         10191000.          9500000.
 13500000.         14019666.66666667 10191500.         10931750.
  9289000.         12435666.66666667 10000000.         10605000.
 13000000.          9500000.          9275000.         11304500.
 10191000.         11876600.         10666666.66666667 13200000.
 12500000.         13361000.         10735000.         11000000.
  8460000.         13200000.         12427200.         10240000.
  8060000.         10750000.         11744750.         12300000.
 12853333.33333333 13300000.         14208333.33333333 10175000.
 12657000.          9766666.66666667  9210000.         20150000.
 11400000.         14478000.         10621000.         13671000.
 10694000.          8833000.         12500000.         12500000.
 10515000.          9200000.         11864000.         13152000.
 13671000.         10931750.          8166666.66666667 12400000.        ]


r2 score is: 0.1333704497598801
MAE:1204078.8903958208
MSE:5341988876649.26
RMSE:2311274.2971463297
In [55]:
from sklearn.svm import SVR
predict(SVR())

Model is: SVR()
Training score: -0.10142340508156522
Predictions are: [10239983.89042958 10239983.75085534 10240000.46946491 10239983.62683526
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r2 score is: -0.10686130024957996
MAE:1844664.2279733596
MSE:6822800759896.2
RMSE:2612049.14959428
In [56]:
predict(GradientBoostingRegressor())

Model is: GradientBoostingRegressor()
Training score: 0.4345350703949775
Predictions are: [12278379.25486124 11987443.77203916 10578117.74595412 10821026.43045443
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r2 score is: 0.3348606685125829
MAE:1441325.8357415097
MSE:4099983561884.336
RMSE:2024841.6140242515
In [57]:
predict(GradientBoostingRegressor())

Model is: GradientBoostingRegressor()
Training score: 0.4345350703949773
Predictions are: [12278379.25486124 11987443.77203915 10578117.74595412 10821026.43045443
  9515729.69618113  9867731.8971204  11860187.699257   11217865.74928253
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r2 score is: 0.3348872370364766
MAE:1441325.83574151
MSE:4099819790917.7563
RMSE:2024801.1731816425
In [58]:
from sklearn.model_selection import RandomizedSearchCV
In [59]:
random_grid = {
    'n_estimators' : [100, 120, 150, 180, 200,220],
    'max_features':['auto','sqrt'],
    'max_depth':[5,10,15,20],
    }
In [60]:
rf=RandomForestRegressor()
rf_random=RandomizedSearchCV(estimator=rf,param_distributions=random_grid,cv=3,verbose=2,n_jobs=-1,)

rf_random.fit(X_train,y_train)

# best parameter
rf_random.best_params_

Fitting 3 folds for each of 10 candidates, totalling 30 fits
Out[60]:
{'n_estimators': 120, 'max_features': 'sqrt', 'max_depth': 10}
In [61]:
#predicting the values
prediction = rf_random.predict(X_test)

#distribution plot between actual value and predicted value
sns.displot(y_test-prediction)
Out[61]:
<seaborn.axisgrid.FacetGrid at 0x279914cfb20>
In [62]:
r2_score(y_test,prediction)
Out[62]:
0.34372722318228377

DecisionTree Algorithm performed the best with a training score of 81.34%, however due to the size of our data-set, it probably created some deadly biases in order to acheive this accuracy by overfitting and not generalizing, therefore it probably wont have the same successrate on new data. Other Models may outperform it once we increase our dataset¶