In [1]:

from sklearn.linear_model import LinearRegression
model = LinearRegression()

In [2]:

import pandas as pd
dataset = pd.DataFrame([
    [1, 2, 3],
    [4, 5, 6],
    [7, 8, 9],
], columns=['x1', 'x2', 'y'])
dataset

Out[2]:

	x1	x2	y
0	1	2	3
1	4	5	6
2	7	8	9

In [3]:

X = dataset[['x1', 'x2']].values
X

Out[3]:

array([[1, 2],
       [4, 5],
       [7, 8]])

In [4]:

y = dataset['y'].values
y

Out[4]:

array([3, 6, 9])

In [5]:

model.fit(X, y)

Out[5]:

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,
         normalize=False)

In [6]:

model.predict([[8, 9]])

Out[6]:

array([10.])

In [7]:

model.predict([
    [0, 1],
    [8, 9],
])

Out[7]:

array([ 2., 10.])

In [8]:

# Save using pickle
from pickle import dump
dump(model, open('dummy-model.pkl', 'wb'))

In [9]:

# Save using joblib which is another option
import subprocess
subprocess.call('pip install joblib'.split())
from joblib import dump
dump(model, '/tmp/dummy-model.joblib')

Out[9]:

['/tmp/dummy-model.joblib']

In [10]:

from pickle import load
model = load(open('dummy-model.pkl', 'rb'))
model

Out[10]:

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,
         normalize=False)

In [11]:

# Load using joblib which is another option
# import subprocess
# subprocess.call('pip install joblib'.split())
from joblib import load
model = load('/tmp/dummy-model.joblib')
model

Out[11]:

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,
         normalize=False)

In [12]:

ls

'1. First Training Set - used as a training model.ipynb'
'2. Train model using dataset.ipynb'
 dummy-model.pkl
'First Training Set - used as a training model.ipynb'
 model.pkl
'predicts air pollution from tree count.ipynb'
'TOOL TEMPLATE - Predict Metrics by Zipcode 20190201.ipynb'
 Untitled1.ipynb
 Untitled.ipynb
 zipCodeAndTreeCountDataset.csv
 zipCodeAndTreeCountDataset.proj4
 zipcode-Copy1.csv
 zipcode.csv

In [19]:

import pandas as pd
t = pd.read_csv('zipCodeAndTreeCountDataset.csv')
t

Out[19]:

	ZIPCODE	BLDGZIP	PO_NAME	POPULATION	AREA	STATE	COUNTY	ST_FIPS	CTY_FIPS	URL	SHAPE_AREA	SHAPE_LEN	Area in Sq Km	Tree Count	pollutant_sum	Tree Count Per Sq Ft	Tree Count Per Sq km	wkt
0	11436	0	Jamaica	18681.0	2.269930e+07	NY	Queens	36	81	http://www.usps.com/	0.0	0.0	2.107694	347	545.928101	1.528682e-05	164.634885	POLYGON ((-73.80584847647393 40.68290932644246...
1	11213	0	Brooklyn	62426.0	2.963100e+07	NY	Kings	36	47	http://www.usps.com/	0.0	0.0	2.751168	570	762.125000	1.923661e-05	207.184697	POLYGON ((-73.9373976313981 40.6797295892508, ...
2	11212	0	Brooklyn	83866.0	4.197210e+07	NY	Kings	36	47	http://www.usps.com/	0.0	0.0	3.897041	596	988.006592	1.419991e-05	152.936545	POLYGON ((-73.90294132545436 40.67083977590008...
3	11225	0	Brooklyn	56527.0	2.369863e+07	NY	Kings	36	47	http://www.usps.com/	0.0	0.0	2.200345	393	587.316772	1.658324e-05	178.608324	POLYGON ((-73.9579731604348 40.67065695897565,...
4	11218	0	Brooklyn	72280.0	3.686880e+07	NY	Kings	36	47	http://www.usps.com/	0.0	0.0	3.423119	702	919.425720	1.904049e-05	205.076101	POLYGON ((-73.97208109564255 40.65059658727607...
5	11226	0	Brooklyn	106132.0	3.940860e+07	NY	Kings	36	47	http://www.usps.com/	0.0	0.0	3.658957	578	960.029358	1.466685e-05	157.968525	POLYGON ((-73.96190027968613 40.65487064531367...
6	11219	0	Brooklyn	92561.0	4.200274e+07	NY	Kings	36	47	http://www.usps.com/	0.0	0.0	3.899752	651	1029.860352	1.549899e-05	166.933702	POLYGON ((-73.98905871487798 40.64411892870269...
7	11210	0	Brooklyn	67067.0	4.788702e+07	NY	Kings	36	47	http://www.usps.com/	0.0	0.0	4.446157	802	1120.218262	1.674775e-05	180.380495	POLYGON ((-73.95839960027368 40.63632662546082...
8	11230	0	Brooklyn	80857.0	4.992670e+07	NY	Kings	36	47	http://www.usps.com/	0.0	0.0	4.635499	1119	1157.793823	2.241286e-05	241.397965	POLYGON ((-73.96451268730159 40.63668725312468...
9	11204	0	Brooklyn	77354.0	4.355518e+07	NY	Kings	36	47	http://www.usps.com/	0.0	0.0	4.043899	692	1035.828003	1.588789e-05	171.121992	POLYGON ((-73.98108387453883 40.63528985591707...
10	10471	0	Bronx	23477.0	8.965141e+07	NY	Bronx	36	5	http://www.usps.com/	0.0	0.0	8.324106	257	1637.815796	2.866659e-06	30.874187	POLYGON ((-73.88191900199823 40.90666565537614...
11	10470	0	Bronx	14740.0	2.154346e+07	NY	Bronx	36	5	http://www.usps.com/	0.0	0.0	2.000330	213	384.988373	9.886991e-06	106.482450	POLYGON ((-73.87790261636154 40.90555190505731...
12	10466	0	Bronx	68942.0	5.526249e+07	NY	Bronx	36	5	http://www.usps.com/	0.0	0.0	5.131210	760	1169.651855	1.375255e-05	148.113218	POLYGON ((-73.84463568238043 40.90475077819391...
13	10467	0	Bronx	97932.0	6.933617e+07	NY	Bronx	36	5	http://www.usps.com/	0.0	0.0	6.437914	665	1399.311523	9.590954e-06	103.294332	POLYGON ((-73.88010836796937 40.89519521108614...
14	10463	0	Bronx	70641.0	3.670338e+07	NY	Bronx	36	5	http://www.usps.com/	0.0	0.0	3.407886	521	779.620972	1.419488e-05	152.880681	POLYGON ((-73.92064636495626 40.88723726005072...
15	10475	0	Bronx	40931.0	3.863330e+07	NY	Bronx	36	5	http://www.usps.com/	0.0	0.0	3.587193	280	736.172729	7.247634e-06	78.055454	POLYGON ((-73.82722404306942 40.89092964709067...
16	10464	0	Bronx	4438.0	7.625748e+07	NY	Bronx	36	5	http://www.usps.com/	0.0	0.0	7.080754	0	1035.359863	0.000000e+00	0.000000	POLYGON ((-73.81538709355007 40.88939419910433...
17	10469	0	Bronx	65101.0	6.804089e+07	NY	Bronx	36	5	http://www.usps.com/	0.0	0.0	6.317708	1008	1498.477539	1.481462e-05	159.551535	POLYGON ((-73.85588386083408 40.88386140903516...
18	10468	0	Bronx	72877.0	3.444760e+07	NY	Bronx	36	5	http://www.usps.com/	0.0	0.0	3.198451	447	921.837952	1.297623e-05	139.755172	POLYGON ((-73.88701149926719 40.88247162427221...
19	10463	0	Bronx	70641.0	3.119702e+06	NY	New York	36	61	http://www.usps.com/	0.0	0.0	0.289662	46	65.406052	1.474500e-05	158.805709	POLYGON ((-73.91544105058509 40.87559063041356...
20	10458	0	Bronx	79362.0	3.596881e+07	NY	Bronx	36	5	http://www.usps.com/	0.0	0.0	3.339711	481	808.135498	1.337270e-05	144.024426	POLYGON ((-73.88480469568044 40.87845532589476...
21	10034	0	New York	39149.0	2.450389e+07	NY	New York	36	61	http://www.usps.com/	0.0	0.0	2.275160	237	546.599487	9.671933e-06	104.168505	POLYGON ((-73.92062094886884 40.87300145746758...
22	10033	0	New York	54284.0	1.615605e+07	NY	New York	36	61	http://www.usps.com/	0.0	0.0	1.500064	262	489.539307	1.621683e-05	174.659185	POLYGON ((-73.93213126543029 40.86945031866858...
23	10462	0	Bronx	75674.0	5.302251e+07	NY	Bronx	36	5	http://www.usps.com/	0.0	0.0	4.923200	605	1186.329346	1.141025e-05	122.887564	POLYGON ((-73.87051512140579 40.8569744976658,...
24	10040	0	New York	41033.0	1.634074e+07	NY	New York	36	61	http://www.usps.com/	0.0	0.0	1.517216	226	403.592285	1.383046e-05	148.957014	POLYGON ((-73.93378898733546 40.86307153891337...
25	10453	0	Bronx	77576.0	2.574851e+07	NY	Bronx	36	5	http://www.usps.com/	0.0	0.0	2.390729	461	656.619141	1.790395e-05	192.828195	POLYGON ((-73.89994956018222 40.85741964281875...
26	10465	0	Bronx	42012.0	1.084237e+08	NY	Bronx	36	5	http://www.usps.com/	0.0	0.0	10.067423	720	2197.093262	6.640612e-06	71.517805	POLYGON ((-73.8391534647799 40.83567473125792,...
27	10464	0	Bronx	4438.0	4.512531e+06	NY	Bronx	36	5	http://www.usps.com/	0.0	0.0	0.419009	0	75.528351	0.000000e+00	0.000000	POLYGON ((-73.77200435714323 40.85711605937528...
28	10464	0	Bronx	4438.0	1.158795e+07	NY	Bronx	36	5	http://www.usps.com/	0.0	0.0	1.075987	146	166.049622	1.259929e-05	135.689310	POLYGON ((-73.79234936875058 40.85607420876183...
29	10461	0	Bronx	50549.0	6.282406e+07	NY	Bronx	36	5	http://www.usps.com/	0.0	0.0	5.833332	830	1337.014160	1.321150e-05	142.285751	POLYGON ((-73.83739648053641 40.8567843873281,...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
233	10120	1	New York	0.0	3.517927e+04	NY	New York	36	61	http://www.usps.com/	0.0	0.0	0.003266	0	0.000000	0.000000e+00	0.000000	POLYGON ((-73.98938483295653 40.75000537600457...
234	10278	1	New York	0.0	2.067060e+05	NY	New York	36	61	http://www.usps.com/	0.0	0.0	0.019192	0	0.000000	0.000000e+00	0.000000	POLYGON ((-74.0047581751106 40.71507032820959,...
235	10155	1	New York	0.0	2.478469e+04	NY	New York	36	61	http://www.usps.com/	0.0	0.0	0.002301	0	0.000000	0.000000e+00	0.000000	POLYGON ((-73.96843628441711 40.76110640977814...
236	10043	1	New York	0.0	3.826236e+04	NY	New York	36	61	http://www.usps.com/	0.0	0.0	0.003552	0	0.000000	0.000000e+00	0.000000	POLYGON ((-74.00708359659971 40.70386978270482...
237	10081	1	New York	0.0	3.024051e+04	NY	New York	36	61	http://www.usps.com/	0.0	0.0	0.002808	0	0.000000	0.000000e+00	0.000000	POLYGON ((-74.008714773259 40.7074893755098, -...
238	10096	1	New York	0.0	4.210611e+04	NY	New York	36	61	http://www.usps.com/	0.0	0.0	0.003909	0	0.000000	0.000000e+00	0.000000	POLYGON ((-73.98301659173394 40.75485102653172...
239	10097	1	New York	0.0	6.582618e+04	NY	New York	36	61	http://www.usps.com/	0.0	0.0	0.006112	0	41.044369	0.000000e+00	0.000000	POLYGON ((-73.98521646115499 40.76265216013989...
240	10196	1	New York	0.0	3.250244e+04	NY	New York	36	61	http://www.usps.com/	0.0	0.0	0.003018	0	0.000000	0.000000e+00	0.000000	POLYGON ((-73.9824301517118 40.75680011816393,...
241	10196	1	New York	0.0	3.154825e+03	NY	New York	36	61	http://www.usps.com/	0.0	0.0	0.000293	0	0.000000	0.000000e+00	0.000000	POLYGON ((-73.98133175627783 40.75673242462563...
242	10275	1	New York	0.0	4.828042e+04	NY	New York	36	61	http://www.usps.com/	0.0	0.0	0.004483	0	0.000000	0.000000e+00	0.000000	POLYGON ((-74.01450964009967 40.70555208010413...
243	10265	1	New York	0.0	1.722915e+04	NY	New York	36	61	http://www.usps.com/	0.0	0.0	0.001600	0	0.000000	0.000000e+00	0.000000	POLYGON ((-74.00806000177114 40.70489246189538...
244	10045	1	New York	0.0	4.780899e+04	NY	New York	36	61	http://www.usps.com/	0.0	0.0	0.004439	0	0.000000	0.000000e+00	0.000000	POLYGON ((-74.00910380250482 40.7087678695075,...
245	10047	1	New York	0.0	1.014978e+04	NY	New York	36	61	http://www.usps.com/	0.0	0.0	0.000942	0	0.000000	0.000000e+00	0.000000	POLYGON ((-73.98633231048143 40.73957889013666...
246	10047	1	New York	0.0	1.014978e+04	NY	New York	36	61	http://www.usps.com/	0.0	0.0	0.000942	0	0.000000	0.000000e+00	0.000000	POLYGON ((-73.98633231048143 40.73957889013666...
247	10080	1	New York	0.0	7.711145e+04	NY	New York	36	61	http://www.usps.com/	0.0	0.0	0.007159	0	0.000000	0.000000e+00	0.000000	POLYGON ((-74.01287666105986 40.71051873297913...
248	10203	1	New York	0.0	3.722688e+04	NY	New York	36	61	http://www.usps.com/	0.0	0.0	0.003456	0	0.000000	0.000000e+00	0.000000	POLYGON ((-74.01193550921882 40.70719828992588...
249	10259	1	New York	0.0	2.106431e+04	NY	New York	36	61	http://www.usps.com/	0.0	0.0	0.001956	0	0.000000	0.000000e+00	0.000000	POLYGON ((-74.01036216523759 40.70876776311058...
250	10260	1	New York	0.0	5.251474e+04	NY	New York	36	61	http://www.usps.com/	0.0	0.0	0.004876	0	0.000000	0.000000e+00	0.000000	POLYGON ((-74.00908463330282 40.70626028948341...
251	10285	1	New York	0.0	6.735039e+04	NY	New York	36	61	http://www.usps.com/	0.0	0.0	0.006253	0	0.000000	0.000000e+00	0.000000	POLYGON ((-74.01476100812657 40.7130865913403,...
252	10286	1	New York	0.0	1.126441e+04	NY	New York	36	61	http://www.usps.com/	0.0	0.0	0.001046	0	0.000000	0.000000e+00	0.000000	POLYGON ((-74.00936106898759 40.7064177757752,...
253	10035	0	New York	34884.0	2.349487e+07	NY	New York	36	61	http://www.usps.com/	0.0	0.0	2.181470	0	477.783386	0.000000e+00	0.000000	POLYGON ((-73.92741771645892 40.79839394486886...
254	11371	0	Flushing	0.0	3.055847e+07	NY	Queens	36	81	http://www.usps.com/	0.0	0.0	2.837371	17	644.740417	5.563105e-07	5.991461	POLYGON ((-73.88509634398015 40.77846404500434...
255	11361	0	Bayside	28496.0	5.016352e+07	NY	Queens	36	81	http://www.usps.com/	0.0	0.0	4.657903	932	1064.254639	1.857924e-05	200.090043	POLYGON ((-73.76641719803655 40.77595272856865...
256	10036	0	New York	23543.0	1.139511e+07	NY	New York	36	61	http://www.usps.com/	0.0	0.0	1.057995	127	441.509674	1.114513e-05	120.038330	POLYGON ((-73.98134106854107 40.75864499066453...
257	11414	0	Howard Beach	26148.0	6.392882e+07	NY	Queens	36	81	http://www.usps.com/	0.0	0.0	5.935843	683	1256.953125	1.068376e-05	115.063688	POLYGON ((-73.85068172809338 40.67164854067394...
258	10310	0	Staten Island	25003.0	5.346328e+07	NY	Richmond	36	85	http://www.usps.com/	0.0	0.0	4.963611	507	1160.368408	9.483145e-06	102.143369	POLYGON ((-74.12064635323901 40.64104377738636...
259	11693	0	Far Rockaway	11052.0	3.497516e+06	NY	Kings	36	47	http://www.usps.com/	0.0	0.0	0.324747	0	0.000000	0.000000e+00	0.000000	POLYGON ((-73.8407585999925 40.62536368150901,...
260	11249	0	Brooklyn	28481.0	1.777221e+07	NY	Kings	36	47	http://www.usps.com/	0.0	0.0	1.650097	262	452.354218	1.474212e-05	158.778526	POLYGON ((-73.95805126567375 40.72442275986043...
261	10162	1	New York	0.0	2.103489e+04	NY	New York	36	61	http://www.usps.com/	0.0	0.0	0.001953	0	0.000000	0.000000e+00	0.000000	POLYGON ((-73.95132807302133 40.76931006893747...
262	10119	1	New York	0.0	1.263930e+05	NY	New York	36	61	http://www.usps.com/	0.0	0.0	0.011735	0	0.000000	0.000000e+00	0.000000	POLYGON ((-73.99354429600449 40.75145227487895...

<p>263 rows × 18 columns</p>

In [20]:

X1 = t[[
    'Tree Count Per Sq km']].values
X1

Out[20]:

array([[1.64634885e+02],
       [2.07184697e+02],
       [1.52936545e+02],
       [1.78608324e+02],
       [2.05076101e+02],
       [1.57968525e+02],
       [1.66933702e+02],
       [1.80380495e+02],
       [2.41397965e+02],
       [1.71121992e+02],
       [3.08741867e+01],
       [1.06482450e+02],
       [1.48113218e+02],
       [1.03294332e+02],
       [1.52880681e+02],
       [7.80554541e+01],
       [0.00000000e+00],
       [1.59551535e+02],
       [1.39755172e+02],
       [1.58805709e+02],
       [1.44024426e+02],
       [1.04168505e+02],
       [1.74659185e+02],
       [1.22887564e+02],
       [1.48957014e+02],
       [1.92828195e+02],
       [7.15178049e+01],
       [0.00000000e+00],
       [1.35689310e+02],
       [1.42285751e+02],
       [1.69102508e+02],
       [1.42147312e+02],
       [1.44164253e+02],
       [1.91163294e+02],
       [2.13722581e+02],
       [1.64176086e+02],
       [2.37681009e+02],
       [1.47116555e+02],
       [2.16337904e+02],
       [1.40654971e+02],
       [1.22676687e+02],
       [2.76279583e+02],
       [1.87971297e+02],
       [1.00237889e+02],
       [1.72565835e+02],
       [1.66295809e+02],
       [2.23624804e+02],
       [9.36125569e+01],
       [2.69920926e+02],
       [1.95941108e+02],
       [3.32156234e+01],
       [3.05043653e+02],
       [1.95739380e+02],
       [1.64156536e+02],
       [4.10556938e+01],
       [1.16844323e+02],
       [0.00000000e+00],
       [9.70612364e+01],
       [1.17643413e+02],
       [3.53087009e+02],
       [2.17352715e+02],
       [1.98127476e+02],
       [2.77956574e+02],
       [1.44384601e+02],
       [1.57753716e+02],
       [1.99883317e+02],
       [3.04788249e+02],
       [1.94207683e+02],
       [1.23094182e+02],
       [0.00000000e+00],
       [1.72331431e+02],
       [1.98122036e+02],
       [1.36737124e+02],
       [9.55316746e+01],
       [1.20397296e+02],
       [1.68798901e+02],
       [6.83354458e+01],
       [1.36376615e+02],
       [8.55136010e+01],
       [2.47135620e+02],
       [3.44122054e+01],
       [1.37456482e+02],
       [8.95779715e+01],
       [1.84874469e+02],
       [1.91173340e+02],
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       [8.58851961e+01],
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       [5.64680520e+01],
       [1.55754178e+02],
       [9.30771324e+01],
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       [1.49435298e+02],
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       [1.68733137e+02],
       [1.26096614e+00],
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       [2.27783297e+02],
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       [6.80995023e+01],
       [7.88759661e+01],
       [2.06749856e+02],
       [1.64465537e+02],
       [1.47960650e+02],
       [0.00000000e+00],
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       [0.00000000e+00],
       [0.00000000e+00],
       [0.00000000e+00],
       [0.00000000e+00],
       [0.00000000e+00],
       [0.00000000e+00],
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       [0.00000000e+00],
       [0.00000000e+00],
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       [0.00000000e+00],
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       [0.00000000e+00],
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       [0.00000000e+00],
       [5.99146103e+00],
       [2.00090043e+02],
       [1.20038330e+02],
       [1.15063688e+02],
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       [0.00000000e+00],
       [1.58778526e+02],
       [0.00000000e+00],
       [0.00000000e+00]])

In [21]:

y = t['pollutant_sum']
y

Out[21]:

0       545.928101
1       762.125000
2       988.006592
3       587.316772
4       919.425720
5       960.029358
6      1029.860352
7      1120.218262
8      1157.793823
9      1035.828003
10     1637.815796
11      384.988373
12     1169.651855
13     1399.311523
14      779.620972
15      736.172729
16     1035.359863
17     1498.477539
18      921.837952
19       65.406052
20      808.135498
21      546.599487
22      489.539307
23     1186.329346
24      403.592285
25      656.619141
26     2197.093262
27       75.528351
28      166.049622
29     1337.014160
          ...     
233       0.000000
234       0.000000
235       0.000000
236       0.000000
237       0.000000
238       0.000000
239      41.044369
240       0.000000
241       0.000000
242       0.000000
243       0.000000
244       0.000000
245       0.000000
246       0.000000
247       0.000000
248       0.000000
249       0.000000
250       0.000000
251       0.000000
252       0.000000
253     477.783386
254     644.740417
255    1064.254639
256     441.509674
257    1256.953125
258    1160.368408
259       0.000000
260     452.354218
261       0.000000
262       0.000000
Name: pollutant_sum, Length: 263, dtype: float64

In [22]:

from sklearn.model_selection import cross_val_score
models = []
scores = []

def train(model, X):
    model.fit(X, y)
    models.append(model)
    score = cross_val_score(
        model, X, y, cv=3,
        scoring='neg_mean_absolute_error',
    ).mean()
    scores.append(score)
    return score

In [23]:

from sklearn.linear_model import LinearRegression
train(LinearRegression(), X1)

Out[23]:

-677.2937511233264

In [ ]:

In [24]:

import numpy as np
best_index = np.argmax(scores)
best_index

Out[24]:

In [25]:

best_model = models[best_index]
best_model

Out[25]:

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,
         normalize=False)

In [26]:

import pickle
pickle.dump(best_model, open('model.pkl', 'wb'))

In [ ]:

Pay Notebook Creator: Rocky Singh	0
Set Container: Numerical CPU with TINY Memory for 10 Minutes	0
Total	0

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