#code adapted from https://github.com/thomasjpfan/ml-workshop-intro

import seaborn as sns
import sklearn
sns.set_theme(context="notebook", font_scale=1.2,
              rc={"figure.figsize": [10, 6]})
sklearn.set_config(display="diagram")

import pandas as pd
url = 'https://raw.githubusercontent.com/davidrkearney/Kearney_Data_Science/master/_notebooks/df_panel_fix.csv'
df = pd.read_csv(url, error_bad_lines=False)
df
Unnamed: 0 province specific general year gdp fdi rnr rr i fr reg it
0 0 Anhui 147002.0 NaN 1996 2093.30 50661 0.000000 0.000000 0.000000 1128873 East China 631930
1 1 Anhui 151981.0 NaN 1997 2347.32 43443 0.000000 0.000000 0.000000 1356287 East China 657860
2 2 Anhui 174930.0 NaN 1998 2542.96 27673 0.000000 0.000000 0.000000 1518236 East China 889463
3 3 Anhui 285324.0 NaN 1999 2712.34 26131 NaN NaN NaN 1646891 East China 1227364
4 4 Anhui 195580.0 32100.0 2000 2902.09 31847 0.000000 0.000000 0.000000 1601508 East China 1499110
... ... ... ... ... ... ... ... ... ... ... ... ... ...
355 355 Zhejiang 391292.0 260313.0 2003 9705.02 498055 1.214286 0.035714 0.035714 6217715 East China 2261631
356 356 Zhejiang 656175.0 276652.0 2004 11648.70 668128 1.214286 0.035714 0.035714 NaN East China 3162299
357 357 Zhejiang 656175.0 NaN 2005 13417.68 772000 1.214286 0.035714 0.035714 NaN East China 2370200
358 358 Zhejiang 1017303.0 394795.0 2006 15718.47 888935 1.214286 0.035714 0.035714 11537149 East China 2553268
359 359 Zhejiang 844647.0 0.0 2007 18753.73 1036576 0.047619 0.000000 0.000000 16494981 East China 2939778

360 rows × 13 columns

import pandas as pd
import sklearn
from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split

df=df.dropna()

df.isnull().sum()

df.columns

sklearn.set_config(display='diagram')

X, y = df.drop(['it', 'Unnamed: 0'], axis = 1), df['it']


X = X.select_dtypes(include='number')
X

_ = X.hist(figsize=(30, 15), layout=(5, 8))

url = 'https://raw.githubusercontent.com/davidrkearney/colab-notebooks/main/datasets/strokes_training.csv'
df = pd.read_csv(url, error_bad_lines=False)
df

df=df.dropna()

df.isnull().sum()

df.columns

sklearn.set_config(display='diagram')

X, y = df.drop(['stroke', 'id'], axis = 1), df['stroke']


X = X.select_dtypes(include='number')
X

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, stratify=y)

Train DummyClassifer 

from sklearn.dummy import DummyClassifier

dc = DummyClassifier(strategy='prior').fit(X_train, y_train)
dc.score(X_test, y_test)

0.9811502476609797

Train KNN based model 

from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.neighbors import KNeighborsClassifier

knc = make_pipeline(
    StandardScaler(),
    KNeighborsClassifier()
)
knc.fit(X_train, y_train)

Pipeline(steps=[('standardscaler', StandardScaler()),
                ('kneighborsclassifier', KNeighborsClassifier())])
StandardScaler()
KNeighborsClassifier()
knc.score(X_test, y_test)

0.9801871216290589
# %load solutions/00-ex01-solutions.py
from sklearn.datasets import load_breast_cancer

cancer = load_breast_cancer(as_frame=True)

X, y = cancer.data, cancer.target

y.value_counts()

X_train, X_test, y_train, y_test = train_test_split(
    X, y, random_state=42, stratify=y)

from sklearn.linear_model import LogisticRegression

log_reg = make_pipeline(
    StandardScaler(),
    LogisticRegression()
)

log_reg.fit(X_train, y_train)

log_reg.score(X_test, y_test)

from sklearn.metrics import f1_score
y_pred = log_reg.predict(X_test)

y_pred

f1_score(y_test, y_pred)

0.9888888888888889