tldr
KggleのStudents' Academic Performance DatasetをPredicting Performance in School - Data Every Day #046に沿ってやっていきます。
実行環境はGoogle Colaboratorです。
インポート
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn.preprocessing as sp
from sklearn.model_selection import train_test_split
import sklearn.linear_model as slm
import tensorflow as tf
データのダウンロード
Google Driveをマウントします。
from google.colab import drive
drive.mount('/content/drive')
Mounted at /content/drive
KaggleのAPIクライアントを初期化し、認証します。
認証情報はGoogle Drive内（/content/drive/My Drive/Colab Notebooks/Kaggle）にkaggle.jsonとして置いてあります。
import os
kaggle_path = "/content/drive/My Drive/Colab Notebooks/Kaggle"
os.environ['KAGGLE_CONFIG_DIR'] = kaggle_path
from kaggle.api.kaggle_api_extended import KaggleApi
api = KaggleApi()
api.authenticate()
Kaggle APIを使ってデータをダウンロードします。
dataset_id = 'aljarah/xAPI-Edu-Data'
dataset = api.dataset_list_files(dataset_id)
file_name = dataset.files[0].name
file_path = os.path.join(api.get_default_download_dir(), file_name)
file_path
'/content/xAPI-Edu-Data.csv'
api.dataset_download_file(dataset_id, file_name, force=True, quiet=False)
100%|██████████| 37.1k/37.1k [00:00<00:00, 19.1MB/s]
Downloading xAPI-Edu-Data.csv to /content
True
データの読み込み
data = pd.read_csv(file_path)
data
| gender | NationalITy | PlaceofBirth | StageID | GradeID | SectionID | Topic | Semester | Relation | raisedhands | VisITedResources | AnnouncementsView | Discussion | ParentAnsweringSurvey | ParentschoolSatisfaction | StudentAbsenceDays | Class | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | M | KW | KuwaIT | lowerlevel | G-04 | A | IT | F | Father | 15 | 16 | 2 | 20 | Yes | Good | Under-7 | M |
| 1 | M | KW | KuwaIT | lowerlevel | G-04 | A | IT | F | Father | 20 | 20 | 3 | 25 | Yes | Good | Under-7 | M |
| 2 | M | KW | KuwaIT | lowerlevel | G-04 | A | IT | F | Father | 10 | 7 | 0 | 30 | No | Bad | Above-7 | L |
| 3 | M | KW | KuwaIT | lowerlevel | G-04 | A | IT | F | Father | 30 | 25 | 5 | 35 | No | Bad | Above-7 | L |
| 4 | M | KW | KuwaIT | lowerlevel | G-04 | A | IT | F | Father | 40 | 50 | 12 | 50 | No | Bad | Above-7 | M |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 475 | F | Jordan | Jordan | MiddleSchool | G-08 | A | Chemistry | S | Father | 5 | 4 | 5 | 8 | No | Bad | Above-7 | L |
| 476 | F | Jordan | Jordan | MiddleSchool | G-08 | A | Geology | F | Father | 50 | 77 | 14 | 28 | No | Bad | Under-7 | M |
| 477 | F | Jordan | Jordan | MiddleSchool | G-08 | A | Geology | S | Father | 55 | 74 | 25 | 29 | No | Bad | Under-7 | M |
| 478 | F | Jordan | Jordan | MiddleSchool | G-08 | A | History | F | Father | 30 | 17 | 14 | 57 | No | Bad | Above-7 | L |
| 479 | F | Jordan | Jordan | MiddleSchool | G-08 | A | History | S | Father | 35 | 14 | 23 | 62 | No | Bad | Above-7 | L |
480 rows × 17 columns
下準備
data.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 480 entries, 0 to 479
Data columns (total 17 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 gender 480 non-null object
1 NationalITy 480 non-null object
2 PlaceofBirth 480 non-null object
3 StageID 480 non-null object
4 GradeID 480 non-null object
5 SectionID 480 non-null object
6 Topic 480 non-null object
7 Semester 480 non-null object
8 Relation 480 non-null object
9 raisedhands 480 non-null int64
10 VisITedResources 480 non-null int64
11 AnnouncementsView 480 non-null int64
12 Discussion 480 non-null int64
13 ParentAnsweringSurvey 480 non-null object
14 ParentschoolSatisfaction 480 non-null object
15 StudentAbsenceDays 480 non-null object
16 Class 480 non-null object
dtypes: int64(4), object(13)
memory usage: 63.9+ KB
def get_uniques(df, columns):
return {column: list(df[column].unique()) for column in columns}
def get_categorical_columns(df):
return [column for column in df.columns if df.dtypes[column] == 'object']
get_uniques(data, get_categorical_columns(data))
{'Class': ['M', 'L', 'H'],
'GradeID': ['G-04',
'G-07',
'G-08',
'G-06',
'G-05',
'G-09',
'G-12',
'G-11',
'G-10',
'G-02'],
'NationalITy': ['KW',
'lebanon',
'Egypt',
'SaudiArabia',
'USA',
'Jordan',
'venzuela',
'Iran',
'Tunis',
'Morocco',
'Syria',
'Palestine',
'Iraq',
'Lybia'],
'ParentAnsweringSurvey': ['Yes', 'No'],
'ParentschoolSatisfaction': ['Good', 'Bad'],
'PlaceofBirth': ['KuwaIT',
'lebanon',
'Egypt',
'SaudiArabia',
'USA',
'Jordan',
'venzuela',
'Iran',
'Tunis',
'Morocco',
'Syria',
'Iraq',
'Palestine',
'Lybia'],
'Relation': ['Father', 'Mum'],
'SectionID': ['A', 'B', 'C'],
'Semester': ['F', 'S'],
'StageID': ['lowerlevel', 'MiddleSchool', 'HighSchool'],
'StudentAbsenceDays': ['Under-7', 'Above-7'],
'Topic': ['IT',
'Math',
'Arabic',
'Science',
'English',
'Quran',
'Spanish',
'French',
'History',
'Biology',
'Chemistry',
'Geology'],
'gender': ['M', 'F']}
binary_feayures = ['gender', 'Semester', 'Relation', 'ParentAnsweringSurvey', 'ParentschoolSatisfaction', 'StudentAbsenceDays']
ordianl_features = ['StageID', 'GradeID']
nominal_features = ['NationalITy', 'PlaceofBirth', 'SectionID', 'Topic']
target_column = 'Class'
binary_positive = ['M', 'S', 'Father', 'Yes', 'Good', 'Above-7']
stage_ordering = ['lowerlevel', 'MiddleSchool', 'HighSchool']
grade_ordering = [
'G-02',
'G-04',
'G-05',
'G-06',
'G-07',
'G-08',
'G-09',
'G-10',
'G-11',
'G-12',
]
nominal_prefixes = ['N', 'B', 'S', 'T']
def binary_encode(df, column, positive_value):
df = df.copy()
df[column] = df[column].apply(lambda x: 1 if x == positive_value else 0)
return df
def ordinal_encode(df, column, ordering):
df = df.copy()
df[column] = df[column].apply(lambda x: ordering.index(x))
return df
def onehot_encode(df, column, prefix):
df = df.copy()
dummies = pd.get_dummies(df[column], prefix=prefix)
df = pd.concat([df, dummies], axis=1)
df = df.drop(column, axis=1)
return df
for feature, positive_value in zip(binary_feayures, binary_positive):
data = binary_encode(data, feature, positive_value)
data = ordinal_encode(data, 'StageID', stage_ordering)
data = ordinal_encode(data, 'GradeID', grade_ordering)
for feature, prefix in zip(nominal_features, nominal_prefixes):
data = onehot_encode(data, feature, prefix)
target_ordering = ['L', 'M', 'H']
data = ordinal_encode(data, target_column, target_ordering)
分割とスケーリング
y = data[target_column]
X = data.drop([target_column], axis=1)
scaler = sp.StandardScaler()
X = scaler.fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8)
トレーニング
X.shape
(480, 55)
model = tf.keras.Sequential([
tf.keras.layers.Dense(64, activation='relu', input_shape=(55,)),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(3, activation='softmax'),
])
model.summary()
model.compile(
optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'],
)
batch_size = 64
epochs = 100
history = model.fit(
X_train,
y_train,
validation_split=0.2,
batch_size=batch_size,
epochs=epochs,
verbose=0,
)
Model: "sequential_4"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_12 (Dense) (None, 64) 3584
_________________________________________________________________
dense_13 (Dense) (None, 64) 4160
_________________________________________________________________
dense_14 (Dense) (None, 3) 195
=================================================================
Total params: 7,939
Trainable params: 7,939
Non-trainable params: 0
_________________________________________________________________
plt.figure(figsize=(14, 10))
epochs_range = range(1, epochs+1)
train_loss = history.history['loss']
val_loss = history.history['val_loss']
plt.plot(epochs_range, train_loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.title('Training and Validation Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.show()
np.argmin(val_loss)
25