In [ ]:
sns.pairplot(train_dataset[['mpg', 'cylinders', 'displacement', 'weight']], diag_kind='kde')
Out[ ]:
<seaborn.axisgrid.PairGrid at 0x7cbcc0308df0>
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
sns.get_dataset_names()
['anagrams', 'anscombe', 'attention', 'brain_networks', 'car_crashes', 'diamonds', 'dots', 'dowjones', 'exercise', 'flights', 'fmri', 'geyser', 'glue', 'healthexp', 'iris', 'mpg', 'penguins', 'planets', 'seaice', 'taxis', 'tips', 'titanic']
df = sns.load_dataset('mpg')
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 398 entries, 0 to 397 Data columns (total 9 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 mpg 398 non-null float64 1 cylinders 398 non-null int64 2 displacement 398 non-null float64 3 horsepower 392 non-null float64 4 weight 398 non-null int64 5 acceleration 398 non-null float64 6 model_year 398 non-null int64 7 origin 398 non-null object 8 name 398 non-null object dtypes: float64(4), int64(3), object(2) memory usage: 28.1+ KB
df = df.dropna()
df.info()
<class 'pandas.core.frame.DataFrame'> Index: 392 entries, 0 to 397 Data columns (total 9 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 mpg 392 non-null float64 1 cylinders 392 non-null int64 2 displacement 392 non-null float64 3 horsepower 392 non-null float64 4 weight 392 non-null int64 5 acceleration 392 non-null float64 6 model_year 392 non-null int64 7 origin 392 non-null object 8 name 392 non-null object dtypes: float64(4), int64(3), object(2) memory usage: 30.6+ KB
train_dataset = df.sample(frac=0.8, random_state=0)
test_dataset = df.drop(train_dataset.index)
train_dataset
| mpg | cylinders | displacement | horsepower | weight | acceleration | model_year | origin | name | |
|---|---|---|---|---|---|---|---|---|---|
| 146 | 28.0 | 4 | 90.0 | 75.0 | 2125 | 14.5 | 74 | usa | dodge colt |
| 282 | 22.3 | 4 | 140.0 | 88.0 | 2890 | 17.3 | 79 | usa | ford fairmont 4 |
| 69 | 12.0 | 8 | 350.0 | 160.0 | 4456 | 13.5 | 72 | usa | oldsmobile delta 88 royale |
| 378 | 38.0 | 4 | 105.0 | 63.0 | 2125 | 14.7 | 82 | usa | plymouth horizon miser |
| 331 | 33.8 | 4 | 97.0 | 67.0 | 2145 | 18.0 | 80 | japan | subaru dl |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 281 | 19.8 | 6 | 200.0 | 85.0 | 2990 | 18.2 | 79 | usa | mercury zephyr 6 |
| 229 | 16.0 | 8 | 400.0 | 180.0 | 4220 | 11.1 | 77 | usa | pontiac grand prix lj |
| 150 | 26.0 | 4 | 108.0 | 93.0 | 2391 | 15.5 | 74 | japan | subaru |
| 145 | 32.0 | 4 | 83.0 | 61.0 | 2003 | 19.0 | 74 | japan | datsun 710 |
| 182 | 28.0 | 4 | 107.0 | 86.0 | 2464 | 15.5 | 76 | europe | fiat 131 |
314 rows × 9 columns
sns.pairplot(train_dataset[['mpg', 'cylinders', 'displacement', 'weight']], diag_kind='kde')
<seaborn.axisgrid.PairGrid at 0x7cbcc0308df0>
train_features = train_dataset.copy()
test_features = test_dataset.copy()
train_labels = train_features.pop('mpg')
test_labels = test_features.pop('mpg')
import matplotlib.pyplot as plt
import matplotlib
import pandas as pd
import numpy as np
import seaborn as sns
from datetime import timedelta,date
from scipy.stats import linregress
import statsmodels.api as sm
Y = train_dataset['displacement']
X = train_dataset['mpg']
plt.plot(X,Y,'k.')
print(type(X))
<class 'pandas.core.series.Series'>
import statsmodels.api as sm
X = sm.add_constant(X)
X.head()
| const | mpg | |
|---|---|---|
| 146 | 1.0 | 28.0 |
| 282 | 1.0 | 22.3 |
| 69 | 1.0 | 12.0 |
| 378 | 1.0 | 38.0 |
| 331 | 1.0 | 33.8 |
model = sm.OLS(Y, X, missing='drop')
model_result = model.fit()
print(model_result.summary())
OLS Regression Results
==============================================================================
Dep. Variable: displacement R-squared: 0.641
Model: OLS Adj. R-squared: 0.639
Method: Least Squares F-statistic: 556.1
Date: Mon, 11 Nov 2024 Prob (F-statistic): 2.62e-71
Time: 06:05:54 Log-Likelihood: -1743.7
No. Observations: 314 AIC: 3491.
Df Residuals: 312 BIC: 3499.
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const 447.1798 11.250 39.750 0.000 425.045 469.315
mpg -10.8046 0.458 -23.583 0.000 -11.706 -9.903
==============================================================================
Omnibus: 6.356 Durbin-Watson: 2.134
Prob(Omnibus): 0.042 Jarque-Bera (JB): 6.109
Skew: 0.316 Prob(JB): 0.0472
Kurtosis: 3.260 Cond. No. 78.3
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
xt = np.linspace(10,48,50)
Xt = train_dataset['mpg']
yt = xt*-10.8046+477.1798
plt.plot(Xt,Y,'k.')
plt.plot(xt,yt,'b--')
[<matplotlib.lines.Line2D at 0x7fcd885728f0>]
from sklearn import svm, metrics
print(X.shape)
print(X)
(314, 2)
const mpg
146 1.0 28.0
282 1.0 22.3
69 1.0 12.0
378 1.0 38.0
331 1.0 33.8
.. ... ...
281 1.0 19.8
229 1.0 16.0
150 1.0 26.0
145 1.0 32.0
182 1.0 28.0
[314 rows x 2 columns]
svr_model = svm.SVR(C=100, epsilon=1, kernel='rbf')
# train the SVM model with training dataset.
svr_model.fit(X,Y)
#xt = sm.add_constant(xt)
# use the trained model for testing dataset.
predicted = svr_model.predict(X)
print('R-sqaure of SVR is ',svr_model.score(X,Y))
print('It is better than simple regression (0.641)')
plt.plot(X['mpg'],Y,'k.')
plt.plot(X['mpg'],predicted,'b.')
R-sqaure of SVR is 0.8017585387171895 It is better than simple regression (0.641)
[<matplotlib.lines.Line2D at 0x7c11e4fd51e0>]
