نحوه ساخت مدل های رگرسیون یادگیری ماشین با پایتون

Species Weight  V-Length    D-Length    X-Length    Height  Width
Bream   290 24  26.3    31.2    12.48   4.3056
Bream   340 23.9    26.5    31.1    12.3778 4.6961
Bream   363 26.3    29  33.5    12.73   4.4555
Bream   430 26.5    29  34  12.444  5.134
Bream   450 26.8    29.7    34.7    13.6024 4.9274
Bream   500 26.8    29.7    34.5    14.1795 5.2785
Bream   390 27.6    30  35  12.67   4.69
Bream   450 27.6    30  35.1    14.0049 4.8438
Bream   500 28.5    30.7    36.2    14.2266 4.9594
Bream   475 28.4    31  36.2    14.2628 5.1042
Bream   500 28.7    31  36.2    14.3714 4.8146
Bream   500 29.1    31.5    36.4    13.7592 4.368
Bream   340 29.5    32  37.3    13.9129 5.0728
Bream   600 29.4    32  37.2    14.9544 5.1708
Bream   600 29.4    32  37.2    15.438  5.58
Bream   700 30.4    33  38.3    14.8604 5.2854
Bream   700 30.4    33  38.5    14.938  5.1975
Bream   610 30.9    33.5    38.6    15.633  5.1338
Bream   650 31  33.5    38.7    14.4738 5.7276
Bream   575 31.3    34  39.5    15.1285 5.5695
Bream   685 31.4    34  39.2    15.9936 5.3704
Bream   620 31.5    34.5    39.7    15.5227 5.2801
Bream   680 31.8    35  40.6    15.4686 6.1306
Bream   700 31.9    35  40.5    16.2405 5.589
Bream   725 31.8    35  40.9    16.36   6.0532
Bream   720 32  35  40.6    16.3618 6.09
Bream   714 32.7    36  41.5    16.517  5.8515
Bream   850 32.8    36  41.6    16.8896 6.1984
Bream   1000    33.5    37  42.6    18.957  6.603
Bream   920 35  38.5    44.1    18.0369 6.3063
Bream   955 35  38.5    44  18.084  6.292
Bream   925 36.2    39.5    45.3    18.7542 6.7497
Bream   975 37.4    41  45.9    18.6354 6.7473
Bream   950 38  41  46.5    17.6235 6.3705
Roach   40  12.9    14.1    16.2    4.1472  2.268
Roach   69  16.5    18.2    20.3    5.2983  2.8217
Roach   78  17.5    18.8    21.2    5.5756  2.9044
Roach   87  18.2    19.8    22.2    5.6166  3.1746
Roach   120 18.6    20  22.2    6.216   3.5742
Roach   0   19  20.5    22.8    6.4752  3.3516
Roach   110 19.1    20.8    23.1    6.1677  3.3957
Roach   120 19.4    21  23.7    6.1146  3.2943
Roach   150 20.4    22  24.7    5.8045  3.7544
Roach   145 20.5    22  24.3    6.6339  3.5478
Roach   160 20.5    22.5    25.3    7.0334  3.8203
Roach   140 21  22.5    25  6.55    3.325
Roach   160 21.1    22.5    25  6.4 3.8
Roach   169 22  24  27.2    7.5344  3.8352
Roach   161 22  23.4    26.7    6.9153  3.6312
Roach   200 22.1    23.5    26.8    7.3968  4.1272
Roach   180 23.6    25.2    27.9    7.0866  3.906
Roach   290 24  26  29.2    8.8768  4.4968
Roach   272 25  27  30.6    8.568   4.7736
Roach   390 29.5    31.7    35  9.485   5.355
Whitefish   270 23.6    26  28.7    8.3804  4.2476
Whitefish   270 24.1    26.5    29.3    8.1454  4.2485
Whitefish   306 25.6    28  30.8    8.778   4.6816
Whitefish   540 28.5    31  34  10.744  6.562
Whitefish   800 33.7    36.4    39.6    11.7612 6.5736
Whitefish   1000    37.3    40  43.5    12.354  6.525
Parkki  55  13.5    14.7    16.5    6.8475  2.3265
Parkki  60  14.3    15.5    17.4    6.5772  2.3142
Parkki  90  16.3    17.7    19.8    7.4052  2.673
Parkki  120 17.5    19  21.3    8.3922  2.9181
Parkki  150 18.4    20  22.4    8.8928  3.2928
Parkki  140 19  20.7    23.2    8.5376  3.2944
Parkki  170 19  20.7    23.2    9.396   3.4104
Parkki  145 19.8    21.5    24.1    9.7364  3.1571
Parkki  200 21.2    23  25.8    10.3458 3.6636
Parkki  273 23  25  28  11.088  4.144
Parkki  300 24  26  29  11.368  4.234
Perch   5.9 7.5 8.4 8.8 2.112   1.408
Perch   32  12.5    13.7    14.7    3.528   1.9992
Perch   40  13.8    15  16  3.824   2.432
Perch   51.5    15  16.2    17.2    4.5924  2.6316
Perch   70  15.7    17.4    18.5    4.588   2.9415
Perch   100 16.2    18  19.2    5.2224  3.3216
Perch   78  16.8    18.7    19.4    5.1992  3.1234
Perch   80  17.2    19  20.2    5.6358  3.0502
Perch   85  17.8    19.6    20.8    5.1376  3.0368
Perch   85  18.2    20  21  5.082   2.772
Perch   110 19  21  22.5    5.6925  3.555
Perch   115 19  21  22.5    5.9175  3.3075
Perch   125 19  21  22.5    5.6925  3.6675
Perch   130 19.3    21.3    22.8    6.384   3.534
Perch   120 20  22  23.5    6.11    3.4075
Perch   120 20  22  23.5    5.64    3.525
Perch   130 20  22  23.5    6.11    3.525
Perch   135 20  22  23.5    5.875   3.525
Perch   110 20  22  23.5    5.5225  3.995
Perch   130 20.5    22.5    24  5.856   3.624
Perch   150 20.5    22.5    24  6.792   3.624
Perch   145 20.7    22.7    24.2    5.9532  3.63
Perch   150 21  23  24.5    5.2185  3.626
Perch   170 21.5    23.5    25  6.275   3.725
Perch   225 22  24  25.5    7.293   3.723
Perch   145 22  24  25.5    6.375   3.825
Perch   188 22.6    24.6    26.2    6.7334  4.1658
Perch   180 23  25  26.5    6.4395  3.6835
Perch   197 23.5    25.6    27  6.561   4.239
Perch   218 25  26.5    28  7.168   4.144
Perch   300 25.2    27.3    28.7    8.323   5.1373
Perch   260 25.4    27.5    28.9    7.1672  4.335
Perch   265 25.4    27.5    28.9    7.0516  4.335
Perch   250 25.4    27.5    28.9    7.2828  4.5662
Perch   250 25.9    28  29.4    7.8204  4.2042
Perch   300 26.9    28.7    30.1    7.5852  4.6354
Perch   320 27.8    30  31.6    7.6156  4.7716
Perch   514 30.5    32.8    34  10.03   6.018
Perch   556 32  34.5    36.5    10.2565 6.3875
Perch   840 32.5    35  37.3    11.4884 7.7957
Perch   685 34  36.5    39  10.881  6.864
Perch   700 34  36  38.3    10.6091 6.7408
Perch   700 34.5    37  39.4    10.835  6.2646
Perch   690 34.6    37  39.3    10.5717 6.3666
Perch   900 36.5    39  41.4    11.1366 7.4934
Perch   650 36.5    39  41.4    11.1366 6.003
Perch   820 36.6    39  41.3    12.4313 7.3514
Perch   850 36.9    40  42.3    11.9286 7.1064
Perch   900 37  40  42.5    11.73   7.225
Perch   1015    37  40  42.4    12.3808 7.4624
Perch   820 37.1    40  42.5    11.135  6.63
Perch   1100    39  42  44.6    12.8002 6.8684
Perch   1000    39.8    43  45.2    11.9328 7.2772
Perch   1100    40.1    43  45.5    12.5125 7.4165
Perch   1000    40.2    43.5    46  12.604  8.142
Perch   1000    41.1    44  46.6    12.4888 7.5958
Pike    200 30  32.3    34.8    5.568   3.3756
Pike    300 31.7    34  37.8    5.7078  4.158
Pike    300 32.7    35  38.8    5.9364  4.3844
Pike    300 34.8    37.3    39.8    6.2884  4.0198
Pike    430 35.5    38  40.5    7.29    4.5765
Pike    345 36  38.5    41  6.396   3.977
Pike    456 40  42.5    45.5    7.28    4.3225
Pike    510 40  42.5    45.5    6.825   4.459
Pike    540 40.1    43  45.8    7.786   5.1296
Pike    500 42  45  48  6.96    4.896
Pike    567 43.2    46  48.7    7.792   4.87
Pike    770 44.8    48  51.2    7.68    5.376
Pike    950 48.3    51.7    55.1    8.9262  6.1712
Pike    1250    52  56  59.7    10.6863 6.9849
Pike    1600    56  60  64  9.6 6.144
Pike    1550    56  60  64  9.6 6.144
Pike    1650    59  63.4    68  10.812  7.48
Smelt   6.7 9.3 9.8 10.8    1.7388  1.0476
Smelt   7.5 10  10.5    11.6    1.972   1.16
Smelt   7   10.1    10.6    11.6    1.7284  1.1484
Smelt   9.7 10.4    11  12  2.196   1.38
Smelt   9.8 10.7    11.2    12.4    2.0832  1.2772
Smelt   8.7 10.8    11.3    12.6    1.9782  1.2852
Smelt   10  11.3    11.8    13.1    2.2139  1.2838
Smelt   9.9 11.3    11.8    13.1    2.2139  1.1659
Smelt   9.8 11.4    12  13.2    2.2044  1.1484
Smelt   12.2    11.5    12.2    13.4    2.0904  1.3936
Smelt   13.4    11.7    12.4    13.5    2.43    1.269
Smelt   12.2    12.1    13  13.8    2.277   1.2558
Smelt   19.7    13.2    14.3    15.2    2.8728  2.0672
Smelt   19.9    13.8    15  16.2    2.9322  1.8792

# Step 1: Importing libraries 
import pandas as pd

# Step 2: Defining the columns of and reading our DataFrame 
columns = ['Species', 'Weight', 'V-Length', 'D-Length', 'X-Length', 'Height', 'Width']
Fish = pd.read_csv('Fish.txt', sep='\t', usecols=columns)

# Printing the head of our DataFrame
print(Fish.head())

  Species  Weight  V-Length  D-Length  X-Length   Height   Width
0   Bream   290.0      24.0      26.3      31.2  12.4800  4.3056
1   Bream   340.0      23.9      26.5      31.1  12.3778  4.6961
2   Bream   363.0      26.3      29.0      33.5  12.7300  4.4555
3   Bream   430.0      26.5      29.0      34.0  12.4440  5.1340
4   Bream   450.0      26.8      29.7      34.7  13.6024  4.9274

# Step 1: Importing libraries 
import pandas as pd

# Step 2: Defining the columns of and reading our data frame 
columns = ['Species', 'Weight', 'V-Length', 'D-Length', 'X-Length', 'Height', 'Width']
Fish = pd.read_csv('Fish.txt', sep='\t', usecols=columns)

# Finding the cross-correlation matrix
print(Fish.corr())

            Weight  V-Length  D-Length  X-Length    Height     Width
Weight    1.000000  0.915691  0.918625  0.923343  0.727260  0.886546
V-Length  0.915691  1.000000  0.999519  0.992155  0.627425  0.867002
D-Length  0.918625  0.999519  1.000000  0.994199  0.642392  0.873499
X-Length  0.923343  0.992155  0.994199  1.000000  0.704628  0.878548
Height    0.727260  0.627425  0.642392  0.704628  1.000000  0.794810
Width     0.886546  0.867002  0.873499  0.878548  0.794810  1.000000

# Step 3: Separating the data into features and labels
X = Fish[['X-Length']]
y = Fish['Weight']

from sklearn.model_selection import train_test_split

# Step 4: Dividing the dataset into test and train data
X_train, X_test, y_train, y_test = 
    train_test_split(
                X, y, 
                test_size=0.3, 
                random_state=10, 
                shuffle=True
                )

# Step 1: Importing libraries 
import pandas as pd

# 1.2
from sklearn.model_selection import train_test_split

# Step 2: Defining the columns of and reading our data frame 
columns = ['Species', 'Weight', 'V-Length', 'D-Length', 'X-Length', 'Height', 'Width']
Fish = pd.read_csv('Fish.txt', sep='\t', usecols=columns)

# Step 3: Seperating the data into features and labels
X = Fish[['X-Length']]
y = Fish['Weight']

# Step 4: Dividing the data into test and train set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=10, shuffle=True)

from sklearn.linear_model import LinearRegression

# Step 5: Selecting the linear regression method from scikit-learn library
model = LinearRegression().fit(X_train, y_train)

# Step 1: Importing libraries 
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn import metrics

# Step 2: Defining the columns of and reading the DataFrame 
columns = ['Species', 'Weight', 'V-Length', 'D-Length', 'X-Length', 'Height', 'Width']
Fish = pd.read_csv('Fish.txt', sep='\t', usecols=columns)

# Step 3: Seperating the data into features and labels
X = Fish[['X-Length']]
y = Fish['Weight']

# Step 4: Dividing the dataset into test and train data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=10, shuffle=True)

# Step 5: Selecting the linear regression method from the scikit-learn library
model = LinearRegression().fit(X_train, y_train)

# Step 6: Validation
# Evaluating the trained model on training data
y_prediction = model.predict(X_train)
print("MAE on train data= " , metrics.mean_absolute_error(y_train, y_prediction))
# Evaluating the trained model on test data
y_prediction = model.predict(X_test)
print("MAE on test data = " , metrics.mean_absolute_error(y_test, y_prediction))

('MAE on train data=", 105.08242420291623)
("MAE on test data=", 108.7817508976745)

# Step 3: Separating the data into features and labels
X = Fish[["V-Length', 'D-Length', 'X-Length', 'Height', 'Width']]
y = Fish['Weight']

# Step 1: Importing libraries 
import pandas as pd

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn import metrics

# Step 2: Defining the columns and reading the DataFrame 
columns = ['Species', 'Weight', 'V-Length', 'D-Length', 'X-Length', 'Height', 'Width']
Fish = pd.read_csv('Fish.txt', sep='\t', usecols=columns)

# Step 3: Seperating the data into features and labels
X = Fish[['V-Length', 'D-Length', 'X-Length', 'Height', 'Width']]
y = Fish['Weight']

# Step 4: Dividing the dataset into test and train data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=10, shuffle=True)

# Step 5: Selecting the linear regression method from the scikit-learn library
model = LinearRegression().fit(X_train, y_train)

# Step 6: Validation
# Evaluating the trained model on training data
y_prediction = model.predict(X_train)
print("MAE on train data= " , metrics.mean_absolute_error(y_train, y_prediction))
# Evaluating the trained model on test data
y_prediction = model.predict(X_test)
print("MAE on test data = " , metrics.mean_absolute_error(y_test, y_prediction))

('MAE on train data=", 88.6176233769433)
("MAE on test data=", 104.71922684746642)

# Step 1: Importing libraries 
import pandas as pd

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn import metrics
from sklearn.preprocessing import PolynomialFeatures

# Step 2: Defining the columns and reading the DataFrame 
columns = ['Species', 'Weight', 'V-Length', 'D-Length', 'X-Length', 'Height', 'Width']
Fish = pd.read_csv('Fish.txt', sep='\t', usecols=columns)

# Step 3: Seperating the data into features and labels
X = Fish[['V-Length', 'D-Length', 'X-Length', 'Height', 'Width']]
y = Fish['Weight']

# Step 4: Generating polynomial features 
Z = PolynomialFeatures(degree=2, include_bias=False).fit_transform(X)
# Dividing the dataset into test and train data
X_train, X_test, y_train, y_test = train_test_split(Z, y, test_size=0.3, random_state=10)

# Step 5: Selecting the linear regression method from the scikit-learn library
model = LinearRegression().fit(X_train, y_train)

# Step 6: Validation
# Evaluating the trained model on training data
y_prediction = model.predict(X_train)
print("MAE on train data= " , metrics.mean_absolute_error(y_train, y_prediction))
# Evaluating our trained model on test data
y_prediction = model.predict(X_test)
print("MAE on test data = " , metrics.mean_absolute_error(y_test, y_prediction))

('MAE on train data=", 30.44121990999409)
("MAE on test data=", 32.558434580499224)