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Basic data analysis and predictions
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Basic data analysis and predictions
You probably only need degree=2, but little matter.
Look at this in the scikit-learn docs -
How good it is can be assessed as the mean squared error and also how well it predicts the validation set.
Hey Jeff,

Im was looking at that link, so I tried to do simular thing with my polynomial regression but i got stuck. Can you maybe give me a hint if Im doing it right ?

Is my code for polynomial regression good ?

array_train = train_dataset.values
y_train = array[:,1].reshape(-1, 1)
X_train = array[:,0].reshape(-1, 1)

array_test = test_dataset.values
y_test = array[:,1].reshape(-1, 1)
X_test = array[:,0].reshape(-1, 1)

poly = PolynomialFeatures(degree = 4) 
X_poly_train = poly.fit_transform(X_train), y_train) 
lin2 = LinearRegression(), y_train)

plt.scatter(X_train, y_train, color = 'blue') 
plt.plot(X_train, lin2.predict(poly.fit_transform(X)), color = 'red') 
plt.title('Polynomial Regression')

y_pred = lin2.predict(X_test)

print (y_pred)

print('Coefficient of determination: %.2f'
      % r2_score(y_test, y_pred))
ValueError Traceback (most recent call last) <ipython-input-10-df595795834c> in <module> 148 149 --> 150 y_pred = lin2.predict(X_test) 151 152 print (y_pred) ~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\linear_model\ in predict(self, X) 219 Returns predicted values. 220 """ --> 221 return self._decision_function(X) 222 223 _preprocess_data = staticmethod(_preprocess_data) ~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\linear_model\ in _decision_function(self, X) 204 X = check_array(X, accept_sparse=['csr', 'csc', 'coo']) 205 return safe_sparse_dot(X, self.coef_.T, --> 206 dense_output=True) + self.intercept_ 207 208 def predict(self, X): ~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\utils\ in safe_sparse_dot(a, b, dense_output) 140 return ret 141 else: --> 142 return, b) 143 144 ValueError: shapes (29,1) and (5,1) not aligned: 1 (dim 1) != 5 (dim 0)
I'm confused regarding your datasets.
Assuming you are starting with a dataset with two columns, one for year and the other for population, and let's make those the column names
You split the dataset into

Try this. I can't test without your full code and csv, but this should get you close.
poly = PolynomialFeatures(2)
X = train_set['year']
y = train_set['population']
lm = linear_model.LinearRegression(), y)
Im sorry, i will upload my csv file and my python file so you can check. I tried to do the polynomial regression as you suggested. In the file you can see how far I´ve come.


Spent some time on this tonight. Note that in my version I read the csv differently, from a different source (my Google drive). It still has errors, but the plot at the end shows the modeled values - next step is to plot the actual vs the modeled, and do stats on them if you want
# Load libraries
from pandas import read_csv
from pandas.plotting import scatter_matrix
from matplotlib import pyplot
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
import pandas as pd
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression

# Load dataset
from google.colab import drive
filename = (r'/content/drive/My Drive/analyza_casovych_radov.csv')
cols = ['Rok', 'Pocet prepravenych cestujucich', ]
dataset = pd.read_csv(filename, names=cols)
df = dataset

trainval_dataset = df.sample(frac=0.8,random_state=42)
test_dataset = df.drop(trainval_dataset.index)
train_dataset = trainval_dataset.sample(frac=0.8, random_state=42)
validate_dataset = trainval_dataset.drop(train_dataset.index)
print ()
print(f"Train {train_dataset.shape} Validate {validate_dataset.shape} Test {test_dataset.shape}")

print ()
print ()

print ('train_dataset= ')  
print (train_dataset)

print ()
print ('test_dataset= ')  
print (test_dataset)

print ()
print ('validate_dataset= ')
print (validate_dataset)


X = train_dataset['Rok']
y = train_dataset['Pocet prepravenych cestujucich']

poly = PolynomialFeatures(2)

X_poly = poly.fit_transform(X.to_frame().values.reshape(-1, 1)), y) 
lin2 = LinearRegression(), y) 

plt.scatter(X.values, y, color = 'blue') 
plt.plot(X.values, lin2.predict(poly.fit_transform(X_poly)), color = 'red') 
plt.title('Polynomial Regression') 
#print (poly.fit_transform(X))

#plt.scatter(X, y, color = 'blue')
#plt.plot(X, (poly.fit_transform(X)), color = 'red')
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-20-b5736948e378> in <module>() 64 plt.scatter(X.values, y, color = 'blue') 65 ---> 66 plt.plot(X.values, lin2.predict(poly.fit_transform(X_poly)), color = 'red') 67 plt.title('Polynomial Regression') 68 plt.xlabel('Rok') 2 frames /usr/local/lib/python3.6/dist-packages/sklearn/utils/ in safe_sparse_dot(a, b, dense_output) 149 ret =, b) 150 else: --> 151 ret = a @ b 152 153 if (sparse.issparse(a) and sparse.issparse(b) ValueError: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 3 is different from 10)
Thank you very much Jef for your time :)

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