⭐ Learn how to import Python library
# comment : Show me how machine learning algorithm do the classification # comment : Machine learning application: image recognition, spam detection # comment : credit to the following open source creator (sci-kitlearn.org) # Code source: Gaël Varoquaux # Andreas Müller # Modified for documentation by Jaques Grobler # License: BSD 3 clause import warnings import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.datasets import make_moons, make_circles, make_classification from sklearn.neural_network import MLPClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from sklearn.gaussian_process import GaussianProcessClassifier from sklearn.gaussian_process.kernels import RBF from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier from sklearn.naive_bayes import GaussianNB from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis # FIXED: modern ROC curve import from sklearn.metrics import RocCurveDisplay # FIXED: modern partial dependence import from sklearn.inspection import PartialDependenceDisplay from sklearn.inspection import permutation_importance from sklearn.ensemble import HistGradientBoostingRegressor # comment Do shift + enter
☑ Output of Python code in Jupyter.
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked 0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S 1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C 2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S 3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S 4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S
📘 compute the mean of the passenger age
# comment : compute the mean of the passenger age titanic["Age"].mean() # comment # do shift + enter
☑ Output of Python code in Jupyter
29.69911764705882
📘 compute median age and fare of the passenger
# comment : compute median age and fare of the passenger titanic[["Age", "Fare"]].median() # comment # do shift + enter
☑ Output of Python code in Jupyter.
Age 28.0000 Fare 14.4542 dtype: float64
📘 show me the descriptive statistics information
# comment : show me the descriptive statistics information titanic[["Age", "Fare"]].describe() # comment # do shift + enter
☑ Output of Python code in Jupyter.
Age Fare count 714.000000 891.000000 mean 29.699118 32.204208 std 14.526497 49.693429 min 0.420000 0.000000 25% 20.125000 7.910400 50% 28.000000 14.454200 75% 38.000000 31.000000 max 80.000000 512.329200
📘 show the aggregate statistic data
# comment: show the aggregate statistic data
titanic.agg({'Age': ['min', 'max', 'median', 'skew'], 'Fare': ['min', 'max', 'median', 'mean']})
# comment # do shift + enter
☑ Output of Python code in Jupyter.
Age Fare max 80.000000 512.329200 mean NaN 32.204208 median 28.000000 14.454200 min 0.420000 0.000000 skew 0.389108 NaN
📘 What is the average age between male vs female
# comment : What is the average age between male vs female
titanic[["Sex", "Age"]].groupby("Sex").mean()
# comment # do shift + enter
☑ Output of Python code in Jupyter
Age Sex female 27.915709 male 30.726645
📘 Compute the average group by sex
# comment : Compute the average group by sex
titanic.groupby("Sex").mean()
# comment # do shift + enter
☑ Output of Python code in Jupyter
PassengerId Survived Pclass Age SibSp Parch Fare Sex female 431.028662 0.742038 2.159236 27.915709 0.694268 0.649682 44.479818 male 454.147314 0.188908 2.389948 30.726645 0.429809 0.235702 25.523893
📘 What is the mean ticket fare price for each of the sex and cabin class combinations?
# comment : What is the mean ticket fare price for each of the sex and cabin class combinations? titanic.groupby(["Sex", "Pclass"])["Fare"].mean() # comment # do shift + enter
☑ Output of Python code in Jupyter
Sex Pclass
female 1 106.125798
2 21.970121
3 16.118810
male 1 67.226127
2 19.741782
3 12.661633
Name: Fare, dtype: float64
📘 What is the number of passengers in each of the cabin classes?
# comment : What is the number of passengers in each of the cabin classes? titanic["Pclass"].value_counts() # comment # Do shift + enter
☑ Output of Python code in Jupyter
3 491 1 216 2 184 Name: Pclass, dtype: int64
📘 sort the passenger age from younger to older
# comment : sort the passenger age from younger to older titanic.sort_values(by="Age").head() # comment # do shift + enter
☑ Output of Python code in Jupyter
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked 803 804 1 3 Thomas, Master. Assad Alexander male 0.42 0 1 2625 8.5167 NaN C 755 756 1 2 Hamalainen, Master. Viljo male 0.67 1 1 250649 14.5000 NaN S 644 645 1 3 Baclini, Miss. Eugenie female 0.75 2 1 2666 19.2583 NaN C 469 470 1 3 Baclini, Miss. Helene Barbara female 0.75 2 1 2666 19.2583 NaN C 78 79 1 2 Caldwell, Master. Alden Gates male 0.83 0 2 248738 29.0000 NaN S
📘 sort the passenger age from oldest to youngest
# comment : sort the passenger age from oldest to youngest titanic.sort_values(by=['Pclass', 'Age'], ascending=False).head() # comment # do shift + enter
☑ Output of Python code in Jupyter
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked 851 852 0 3 Svensson, Mr. Johan male 74.0 0 0 347060 7.7750 NaN S 116 117 0 3 Connors, Mr. Patrick male 70.5 0 0 370369 7.7500 NaN Q 280 281 0 3 Duane, Mr. Frank male 65.0 0 0 336439 7.7500 NaN Q 483 484 1 3 Turkula, Mrs. (Hedwig) female 63.0 0 0 4134 9.5875 NaN S 326 327 0 3 Nysveen, Mr. Johan Hansen male 61.0 0 0 345364 6.2375 NaN S