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In [16]:



    


import pandas as pd
data_df = pd.read_csv('https://query.data.world/s/afb7vfbdtzot2kgsqa3obq2jqra5y2')



    











In [17]:



    


import numpy as np
np.random.seed(12345)
%matplotlib inline
import seaborn as sns

import matplotlib.pyplot as plt
plt.style.use('ggplot')



    











In [18]:



    


data_df.head()



    


















    
Out[18]:











  
    

      
      Name
      GP
      MIN
      PTS
      FGM
      FGA
      FG%
      3P Made
      3PA
      3P%
      ...
      FTA
      FT%
      OREB
      DREB
      REB
      AST
      STL
      BLK
      TOV
      TARGET_5Yrs
    

  
  
    

      0
      Brandon Ingram
      36
      27.4
      7.4
      2.6
      7.6
      34.7
      0.5
      2.1
      25.0
      ...
      2.3
      69.9
      0.7
      3.4
      4.1
      1.9
      0.4
      0.4
      1.3
      0.0
    

    

      1
      Andrew Harrison
      35
      26.9
      7.2
      2.0
      6.7
      29.6
      0.7
      2.8
      23.5
      ...
      3.4
      76.5
      0.5
      2.0
      2.4
      3.7
      1.1
      0.5
      1.6
      0.0
    

    

      2
      JaKarr Sampson
      74
      15.3
      5.2
      2.0
      4.7
      42.2
      0.4
      1.7
      24.4
      ...
      1.3
      67.0
      0.5
      1.7
      2.2
      1.0
      0.5
      0.3
      1.0
      0.0
    

    

      3
      Malik Sealy
      58
      11.6
      5.7
      2.3
      5.5
      42.6
      0.1
      0.5
      22.6
      ...
      1.3
      68.9
      1.0
      0.9
      1.9
      0.8
      0.6
      0.1
      1.0
      1.0
    

    

      4
      Matt Geiger
      48
      11.5
      4.5
      1.6
      3.0
      52.4
      0.0
      0.1
      0.0
      ...
      1.9
      67.4
      1.0
      1.5
      2.5
      0.3
      0.3
      0.4
      0.8
      1.0
    

  



5 rows × 21 columns



















In [19]:



    


data_df.info()



    


















    






<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1340 entries, 0 to 1339
Data columns (total 21 columns):
Name           1340 non-null object
GP             1340 non-null int64
MIN            1340 non-null float64
PTS            1340 non-null float64
FGM            1340 non-null float64
FGA            1340 non-null float64
FG%            1340 non-null float64
3P Made        1340 non-null float64
3PA            1340 non-null float64
3P%            1329 non-null float64
FTM            1340 non-null float64
FTA            1340 non-null float64
FT%            1340 non-null float64
OREB           1340 non-null float64
DREB           1340 non-null float64
REB            1340 non-null float64
AST            1340 non-null float64
STL            1340 non-null float64
BLK            1340 non-null float64
TOV            1340 non-null float64
TARGET_5Yrs    1340 non-null float64
dtypes: float64(19), int64(1), object(1)
memory usage: 219.9+ KB

Class Distribution





In [20]:



    


ax = data_df.TARGET_5Yrs.value_counts().plot(kind='bar')

Mean and STD of Both Groups`





In [21]:



    


data_df = data_df.drop('Name', axis=1)



    











In [22]:



    


_, ax = plt.subplots(1, 1, figsize=(12, 4))
data_df.groupby('TARGET_5Yrs', as_index=False).mean().T.plot(kind='bar', ax=ax, rot=30)



    


















    
Out[22]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f12ae1a0e80>










    
























In [23]:



    


_, ax = plt.subplots(1, 1, figsize=(12, 4))
data_df.groupby('TARGET_5Yrs', as_index=False).std().T.plot(kind='bar', ax=ax, rot=30)



    


















    
Out[23]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f12adafe208>

Boxplot Per Feature





In [24]:



    


_, axes = plt.subplots(5, 4, sharey=False, figsize=(16, 16))

for i, col in enumerate(data_df.columns):
    row_i, col_i = divmod(i, 4)
    _ = data_df.boxplot(col, ax=axes[row_i][col_i], by='TARGET_5Yrs')
    
plt.tight_layout()

First Model: Naive Bayes





In [25]:



    


from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import StratifiedKFold, cross_validate
from sklearn.pipeline import Pipeline
from sklearn.feature_selection import SelectKBest, f_classif


pipeline = Pipeline([
    ('selector', SelectKBest(f_classif, k=5)),
    ('model', GaussianNB(priors=[0.5, 0.5]))
])

cv = StratifiedKFold(n_splits=10, random_state=12345)

results = cross_validate(
    pipeline,
    data_df.drop(['TARGET_5Yrs', '3P%'], axis=1).values,
    data_df.TARGET_5Yrs.values,
    cv=cv,
    scoring='f1',
    return_train_score=True
)

results = pd.DataFrame.from_dict(results)



    











In [26]:



    


results.loc[:, ['train_score', 'test_score']].plot(kind='bar')



    


















    
Out[26]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f12ad328eb8>










    
























In [27]:



    


results.mean()



    


















    
Out[27]:








fit_time       0.002678
score_time     0.001037
test_score     0.628162
train_score    0.630542
dtype: float64

Logistic Regression`





In [28]:



    


from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import StratifiedKFold, cross_validate
from sklearn.pipeline import Pipeline
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.preprocessing import StandardScaler


pipeline = Pipeline([
    ('selector', SelectKBest(f_classif, k=10)),
    ('scaler', StandardScaler()),
    ('model', LogisticRegression(solver='lbfgs'))
])

cv = StratifiedKFold(n_splits=10, random_state=12345)

results = cross_validate(
    pipeline,
    data_df.drop(['TARGET_5Yrs', '3P%'], axis=1).values,
    data_df.TARGET_5Yrs.values,
    scoring='f1',
    cv=cv,
    return_train_score=True
)

results = pd.DataFrame.from_dict(results)



    











In [29]:



    


results.loc[:, ['train_score', 'test_score']].plot(kind='bar')



    


















    
Out[29]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f12ad26e208>










    
























In [30]:



    


results.mean()



    


















    
Out[30]:








fit_time       0.018507
score_time     0.000974
test_score     0.774837
train_score    0.779661
dtype: float64

Hyperparameter Optimization





In [38]:



    


from sklearn.model_selection import GridSearchCV

pipeline = Pipeline([
    ('selector', SelectKBest(f_classif, k=10)),
    ('scaler', StandardScaler()),
    ('model', LogisticRegression(solver='lbfgs'))
])

cv = StratifiedKFold(n_splits=10, random_state=12345)

param_grid = {'model__C': [0.01, 0.1, 1, 10, 100]}
gs = GridSearchCV(pipeline, param_grid, scoring='f1', cv=cv, return_train_score=True)
gs = gs.fit(
    data_df.drop(['TARGET_5Yrs', '3P%'], axis=1).values,
    data_df.TARGET_5Yrs.values
)


gs.best_params_



    


















    
Out[38]:








{'model__C': 0.1}

Best Model





In [39]:



    


pipeline = Pipeline([
    ('selector', SelectKBest(f_classif, k=10)),
    ('scaler', StandardScaler()),
    ('model', LogisticRegression(solver='lbfgs', C=0.1))
])

cv = StratifiedKFold(n_splits=10, random_state=12345)

results = cross_validate(
    pipeline,
    data_df.drop(['TARGET_5Yrs', '3P%'], axis=1).values,
    data_df.TARGET_5Yrs.values,
    scoring='f1',
    cv=cv,
    return_train_score=True
)

results = pd.DataFrame.from_dict(results)



    











In [40]:



    


results.loc[:, ['train_score', 'test_score']].plot(kind='bar')



    


















    
Out[40]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f12acfef7b8>










    
























In [41]:



    


results.mean()



    


















    
Out[41]:








fit_time       0.011130
score_time     0.000856
test_score     0.777136
train_score    0.781080
dtype: float64

Content source: dipanjank/ml

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