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#์˜ค๋Š˜์˜ ํŒŒ์ด์ฌ #1์ผ1์˜คํŒŒ #ํŒŒ์ด์ฌ # python

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๐Ÿ“ฃ ์˜ค๋Š˜์˜ ํŒŒ์ด์ฌ ๋ฆฌ์ŠคํŠธ

2021.06.07 12:14 5,436 ์กฐํšŒ

DACON_101: ๋จธ์‹ ๋Ÿฌ๋‹์ด ์ฒ˜์Œ์ด๋ผ๋ฉด ๐Ÿค”

๐Ÿƒโ€โ™€๏ธ [๋ฐ์ดํ„ฐ ๋‹ค์šด๋กœ๋“œ] ๋ถ€ํ„ฐ ๏ธ[๋Œ€ํšŒ ์ œ์ถœ] ๊นŒ์ง€ ๐Ÿƒโ€โ™‚



Lv1. ์˜์‚ฌ๊ฒฐ์ •ํšŒ๊ท€๋‚˜๋ฌด๋กœ ๋”ฐ๋ฆ‰์ด ๋ฐ์ดํ„ฐ ์˜ˆ์ธกํ•˜๊ธฐ


๐Ÿƒโ€โ™‚๏ธLv1 | EDA | ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ (import)

โฌ‡๏ธLv1 | EDA | ํŒŒ์ผ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ (read_csv())

๐Ÿ”Lv1 | EDA | ํ–‰์—ด๊ฐฏ์ˆ˜ ๊ด€์ฐฐํ•˜๊ธฐ (shape)

โœ…Lv1 | EDA | ๋ฐ์ดํ„ฐ ํ™•์ธํ•˜๊ธฐ (head())

๐Ÿ’ฃLv1 | EDA | ๊ฒฐ์ธก์น˜ ํ™•์ธํ•˜๊ธฐ (is_null())


๐ŸงฒLv1 | ์ „์ฒ˜๋ฆฌ | ๋ฐ์ดํ„ฐ ๊ฒฐ์ธก์น˜ ํ™•์ธํ•˜๊ธฐ (info())

๐Ÿ› Lv1 | ์ „์ฒ˜๋ฆฌ | ๊ฒฐ์ธก์น˜ ์‚ญ์ œํ•˜๊ธฐ, ๋Œ€์ฒดํ•˜๊ธฐ (dropna(), fillna())


๐ŸŒฒLv1 | ๋ชจ๋ธ๋ง | scikit-learn (DecisionTreeClassifier)

๐Ÿ‘จโ€๐ŸซLv1 | ๋ชจ๋ธ๋ง | ๋ชจ๋ธ๊ฐœ๋… (์˜์‚ฌ๊ฒฐ์ •๋‚˜๋ฌด)

๐ŸŒณLv1 | ๋ชจ๋ธ๋ง | ๋ชจ๋ธ์„ ์–ธ (DecisionTreeClassifier())

๐ŸƒLv1 | ๋ชจ๋ธ๋ง | ๋ชจ๋ธํ›ˆ๋ จ (fit())

โœˆ๏ธLv1 | ๋ชจ๋ธ๋ง | ํ…Œ์ŠคํŠธ์˜ˆ์ธก(predict())

๐Ÿ™‹Lv1 | ๋ชจ๋ธ๋ง | ์ œ์ถœํŒŒ์ผ์ƒ์„ฑ(to_csv())


๐Ÿ“Lv1 | ๋ณต์Šต



Lv2. ๊ฒฐ์ธก์น˜ ๋ณด๊ฐ„๋ฒ•๊ณผ ๋žœ๋คํฌ๋ ˆ์ŠคํŠธ๋กœ ๋”ฐ๋ฆ‰์ด ๋ฐ์ดํ„ฐ ์˜ˆ์ธกํ•˜๊ธฐ


๐Ÿค” Lv2 | ์ „์ฒ˜๋ฆฌ | ๊ฒฐ์ธก์น˜ ํ‰๊ท ์œผ๋กœ ๋Œ€์ฒด (fillna({mean}))

๐Ÿ˜ฒ Lv2 | ์ „์ฒ˜๋ฆฌ | ๊ฒฐ์ธก์น˜ ๋ณด๊ฐ„๋ฒ•์œผ๋กœ ๋Œ€์ฒด (interpolate())


๐Ÿ”จ Lv2 | ๋ชจ๋ธ๋ง | ๋žœ๋คํฌ๋ ˆ์ŠคํŠธ ๊ฐœ๋…, ์„ ์–ธ (RandomForestRegressor())

โœ๏ธ Lv2 | ๋ชจ๋ธ๋ง | ๋žœ๋คํฌ๋ ˆ์ŠคํŠธ๋ฅผ ํ‰๊ฐ€์ฒ™๋„์— ๋งž๊ฒŒ ํ•™์Šต (criterion='mse')


๐Ÿ”Ž Lv2 | ํŠœ๋‹ | ๋žœ๋คํฌ๋ ˆ์ŠคํŠธ ๋ณ€์ˆ˜์ค‘์š”๋„ ํ™•์ธ (feature_importances_)

โŒ Lv2 | ํŠœ๋‹ | ๋ณ€์ˆ˜ ์ œ๊ฑฐ (drop())

๐Ÿš† Lv2 | ํŠœ๋‹ | ํ•˜์ดํผํŒŒ๋ผ๋ฏธํ„ฐ, GridSearch ๊ฐœ๋… (์ •์ง€๊ทœ์น™)

โš’ Lv2 | ํŠœ๋‹ | GridSearch ๊ตฌํ˜„ (GridSearchCV())


๐Ÿ“ Lv2 | ๋ณต์Šต



Lv3. ๊ต์ฐจ๊ฒ€์ฆ๊ณผ LGBM ๋ชจ๋ธ์„ ํ™œ์šฉํ•œ ์™€์ธ ํ’ˆ์งˆ ๋ถ„๋ฅ˜ํ•˜๊ธฐ


๐Ÿ”Ž Lv3 | EDA | read_csv(), info(), shape, head()

๐Ÿค” Lv3 | EDA | ๊ฒฐ์ธก์น˜ ์œ ๋ฌด ํ™•์ธํ•˜๊ธฐ isnull().sum()

๐Ÿ—‚ Lv3 | EDA | ์ˆ˜์น˜๋ฐ์ดํ„ฐ ํŠน์„ฑ ๋ณด๊ธฐ (describe())

โœ… Lv3 | EDA | ํƒ€๊นƒ ๋ณ€์ˆ˜ ๋ถ„ํฌ ์‹œ๊ฐํ™”  seaborn distplot()

๐Ÿ“ˆLv3 | EDA | Matplotlib ์„  ๊ทธ๋ž˜ํ”„ ๊ทธ๋ฆฌ๊ธฐ (plot())

๐Ÿ”ฒ Lv3 | EDA | Matplotlib ํžˆ์Šคํ† ๊ทธ๋žจ ๊ทธ๋ฆฌ๊ธฐ (hist())



๐Ÿ‘ Lv3 | ์ „์ฒ˜๋ฆฌ | ์ด์ƒ์น˜ ํƒ์ง€ seaborn_boxplot()

๐ŸŽ Lv3 | ์ „์ฒ˜๋ฆฌ | ์ด์ƒ์น˜ ์ œ๊ฑฐ IQR

๐Ÿ’•Lv3 | ์ „์ฒ˜๋ฆฌ | ์ˆ˜์น˜ํ˜• ๋ฐ์ดํ„ฐ ์ •๊ทœํ™” MinMaxScaler()

๐ŸŒLv3 | ์ „์ฒ˜๋ฆฌ |  ์›-ํ•ซ ์ธ์ฝ”๋”ฉ OneHotEncoder()


๐Ÿ˜ŽLv3 | ๋ชจ๋ธ๋ง | ๋ชจ๋ธ ์ •์˜ RandomForestClassifier()

๐Ÿฑโ€๐ŸLv3 | ๋ชจ๋ธ๋ง | ๋ชจ๋ธ ์‹ค์Šต RandomForestClassifier()

๐Ÿ‘Lv3 | ๋ชจ๋ธ๋ง | ๊ต์ฐจ ๊ฒ€์ฆ ์ •์˜ K-Fold

๐Ÿ‘Lv3 | ๋ชจ๋ธ๋ง | ๊ต์ฐจ๊ฒ€์ฆ ์‹ค์Šต K-Fold


๐ŸฆLv3 | ํŠœ๋‹ | Bayesian Optimization

๐ŸงLv3 | ํŠœ๋‹ | ๊ทธ๋ฆฌ๋“œ, ๋žœ๋ค ์„œ์น˜ vs Bayesian Optimization

๐ŸจLv3 | ํŠœ๋‹ | Bayesian Optimization ์‹ค์Šต


๐Ÿ“ Lv3 | ๋ณต์Šต



Lv4. ๊ต์ฐจ๊ฒ€์ฆ๊ณผ ๋ชจ๋ธ ์•™์ƒ๋ธ”์„ ํ™œ์šฉํ•œ ์™€์ธ ํ’ˆ์งˆ ๋ถ„๋ฅ˜ํ•˜๊ธฐ



๐Ÿฆ Lv4 | EDA | 1/5 | seaborn pairplot

๐Ÿจ Lv4 | EDA | 2/5 | seaborn distplot

๐Ÿง Lv4 | EDA | 3/5 | seaborn | heatamp

๐ŸฅLv4 | EDA | 4/5 | ๋‹ค์ค‘๊ณต์„ ์„ฑ Scatter plot

๐ŸŽLv4 | EDA | 5/5 | ๋‹ค์ค‘๊ณต์„ ์„ฑ VIF(๋ถ„์‚ฐ ํŒฝ์ฐฝ ์š”์ธ)

๐Ÿฅ•Lv4 | EDA | ๋ณต์Šต ๐Ÿง“๐Ÿ‘ด

๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ

๐ŸงธLv4 | ์ „์ฒ˜๋ฆฌ | 1/9 | ๋‹ค์ค‘๊ณต์„ ์„ฑ ํ•ด๊ฒฐ | ๋ณ€์ˆ˜ ์ •๊ทœํ™”

๐ŸŽจLv4 | ์ „์ฒ˜๋ฆฌ | 2/9 | ๋‹ค์ค‘๊ณต์„ ์„ฑ ํ•ด๊ฒฐ | ๋ณ€์ˆ˜ ์ œ๊ฑฐ

๐ŸงตLv4 | ์ „์ฒ˜๋ฆฌ | 3/9 | ๋‹ค์ค‘๊ณต์„ ์„ฑ ํ•ด๊ฒฐ - PCA (1)

๐Ÿช€Lv4 | ์ „์ฒ˜๋ฆฌ | 4/9 | ๋‹ค์ค‘๊ณต์„ ์„ฑ ํ•ด๊ฒฐ - PCA (2)

๐ŸฅŒLv4 | ์ „์ฒ˜๋ฆฌ | 5/9 | ๋‹ค์ค‘๊ณต์„ ์„ฑ ํ•ด๊ฒฐ - PCA (3)

๐ŸLv4 | ์ „์ฒ˜๋ฆฌ | 6/9 | ์—ฐ์†ํ˜• ๋ณ€์ˆ˜ ๋ณ€ํ™˜ (1)

๐ŸŽฃLv4 | ์ „์ฒ˜๋ฆฌ | 7/9 | ์—ฐ์†ํ˜• ๋ณ€์ˆ˜ ๋ณ€ํ™˜ (2)

๐ŸŽขLv4 | ์ „์ฒ˜๋ฆฌ | 8/9 | Polynomial Features (1)

๐ŸŽชLv4 | ์ „์ฒ˜๋ฆฌ | 9/9 | Polynomial Features (2)

๐Ÿฅ•Lv4 | ์ „์ฒ˜๋ฆฌ | ๋ณต์Šต - (1) ๐Ÿง“๐Ÿ‘ด

๐Ÿ‡Lv4 | ์ „์ฒ˜๋ฆฌ | ๋ณต์Šต - (2) ๐Ÿ‘ธ๐Ÿคด


๐Ÿ–Lv4 | ๋ชจ๋ธ๋ง | 1/8 | XGBoost ๊ฐœ๋…

๐Ÿ—Lv4 | ๋ชจ๋ธ๋ง | 2/8 | XGBoost ์‹ค์Šต

๐ŸฅฉLv4 | ๋ชจ๋ธ๋ง | 3/8 | LightGBM ๊ฐœ๋…

๐ŸคLv4 | ๋ชจ๋ธ๋ง | 4/8 | LightGBM ์‹ค์Šต

๐ŸฉLv4 | ๋ชจ๋ธ๋ง | 5/8 | stratified k-fold ์ •์˜

๐ŸชLv4 | ๋ชจ๋ธ๋ง | 6/8 | stratified k-fold ์‹ค์Šต

๐Ÿ˜Lv4 | ๋ชจ๋ธ๋ง | 7/8 | Voting Classifier ์ •์˜

๐Ÿ™LV4 | ๋ชจ๋ธ๋ง | 8/8 | Voting Classifier ์‹ค์Šต

๐Ÿฅ›LV4 | ๋ชจ๋ธ๋ง | ๋ณต์Šต - (1)

๐Ÿงด Lv4 | ๋ชจ๋ธ๋ง | ๋ณต์Šต - (2)


๐Ÿ›นLv4 | ํŠœ๋‹ | 1/6 | Bayesian Optimization ๋ณต์Šต

๐ŸงทLv4 | ํŠœ๋‹ | 2/6 | Bayesian Optimization ์‹ค์Šต

๐ŸšงLv4 | ํŠœ๋‹ | 3/6 | XGBoost ํŠœ๋‹

๐Ÿš€Lv4 | ํŠœ๋‹ | 4/6 | Light GBM ํŠœ๋‹

๐ŸšLv4 | ํŠœ๋‹ | 5/6 | ๋ชจ๋ธ ํŠœ๋‹ / Voting Classifier(1)

๐ŸšฒLv4 | ํŠœ๋‹ | 6/6 | ๋ชจ๋ธ ํŠœ๋‹ / Voting Classifier(2)


๐ŸŒ€Lv4 | ๋ณต์Šต


๐Ÿ›ซ์ •ํ˜• ๋ฐ์ดํ„ฐ ๋ถ„์„ ํŒŒ์ดํ”„๋ผ์ธ - (1)

๐Ÿ›ฌ์ •ํ˜• ๋ฐ์ดํ„ฐ ๋ถ„์„ ํŒŒ์ดํ”„๋ผ์ธ - (2)



Lv1. LGBM๋ชจ๋ธ๋กœ ์ฒญ์™€๋Œ€ ์ฒญ์› ๋ฐ์ดํ„ฐ ๋ถ„๋ฅ˜ํ•˜๊ธฐ


๐Ÿ’ซLv1 | EDA  | 1/12 | ํŒŒ์ผ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ - (nrows, n๋ฒˆ์งธ ํ–‰๊นŒ์ง€ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ)

๐Ÿš—Lv1 | EDA | 2/12 | ํŒŒ์ผ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ - (header, ์›ํ•˜๋Š” ํ–‰์„ ์ปฌ๋Ÿผ์œผ๋กœ ์ง€์ •ํ•˜์—ฌ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ)

๐Ÿš“Lv1 | EDA | 3/12 | ํŒŒ์ผ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ - (index_col, ์›ํ•˜๋Š” ์ปฌ๋Ÿผ์„ ์ธ๋ฑ์Šค๋กœ ์ง€์ •ํ•˜์—ฌ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ)

๐Ÿš•Lv1 | EDA | 4/12 | ํŒŒ์ผ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ - (na_filter, ๊ฒฐ์ธก์น˜๋ฅผ ์ œ์™ธํ•˜๊ณ  ๋ถˆ๋Ÿฌ์˜ค๊ธฐ)

๐Ÿช‚ Lv1 | EDA | 5/12 | ํŒŒ์ผ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ - (skipfooter, ๋’ค์—์„œ n๊ฐœ ํ–‰ ์ œ์™ธํ•˜๊ณ  ๋ถˆ๋Ÿฌ์˜ค๊ธฐ)

๐ŸšLv1 | EDA | 6/12 | ํŒŒ์ผ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ - (encoding, ๋ฐ์ดํ„ฐ์˜ ์ธ์ฝ”๋”ฉ ํ˜•์‹์„ ๋งž์ถฐ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ)

๐Ÿš†Lv1 | EDA | 7/12 | ํŒŒ์ผ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ - (names, ๋ฐ์ดํ„ฐ๋ฅผ ๋ถˆ๋Ÿฌ์˜ฌ ๋•Œ ์ปฌ๋Ÿผ๋ช…์„ ์ง€์ •ํ•ด์„œ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ)

๐ŸˆLv1 | EDA | 8/12 | ํŒŒ์ผ ๋‚ด๋ณด๋‚ด๊ธฐ - (index=False, ๋ฐ์ดํ„ฐ๋ฅผ ์ €์žฅํ•  ๋•Œ index ์ œ์™ธํ•˜๊ณ  ์ €์žฅ)

๐Ÿ‰Lv1 | EDA | 9/12 | ๋ฐ์ดํ„ฐ ํ™•์ธํ•˜๊ธฐ - (head(),tail())

๐ŸŠLv1 | EDA | 10/12 | ๋ฐ์ดํ„ฐ ๊ฒฐ์ธก์น˜ ํ™•์ธํ•˜๊ธฐ - (isnull().sum())

๐Ÿ‹Lv1| EDA | 11/12 | unique value ๊ฐ’ ๊ฐœ์ˆ˜ ์ถœ๋ ฅ(value_counts()

๐ŸŒLv1 | EDA | 12/12 | ๋ฐ์ดํ„ฐ ๊ธฐ๋ณธ ์ •๋ณด ๋ณด๊ธฐ(info())


๐ŸฅงLv1 | ์ „์ฒ˜๋ฆฌ | 1/14 | ํ…์ŠคํŠธ ๋ฐ์ดํ„ฐ ์ „์ฒ˜๋ฆฌ ๊ฐœ์š”

๐ŸฆLv1 | ์ „์ฒ˜๋ฆฌ | 2/14 | ํ…์ŠคํŠธ ๋ฐ์ดํ„ฐ ํŠน์ • ํ…์ŠคํŠธ ์ œ๊ฑฐ(replace())

๐ŸงLv1 | ์ „์ฒ˜๋ฆฌ | 3/14 | ํ…์ŠคํŠธ ๋ฐ์ดํ„ฐ ํŠน์ • ํ…์ŠคํŠธ ์ œ๊ฑฐ(isalpha())

๐ŸจLv1 | ์ „์ฒ˜๋ฆฌ | 4/14 | ํ…์ŠคํŠธ ๋ฐ์ดํ„ฐ ํŠน์ • ํ…์ŠคํŠธ ์ œ๊ฑฐ(isalnum())

๐ŸฉLv1 | ์ „์ฒ˜๋ฆฌ | 5/14 | ํ…์ŠคํŠธ ๋ฐ์ดํ„ฐ ํŠน์ • ํ…์ŠคํŠธ ์ œ๊ฑฐ(isdecimal())

๐ŸชLv1 | ์ „์ฒ˜๋ฆฌ | 6/14 | ํŠน์ • ํ…์ŠคํŠธ ์ œ๊ฑฐ - apply(),lambda()

๐ŸŽ‚Lv1 | ์ „์ฒ˜๋ฆฌ | 7/14 | ํ˜•ํƒœ์†Œ ๋ถ„์„๊ธฐ - (1)

๐ŸงLv1 | ์ „์ฒ˜๋ฆฌ | 8/14 | ํ˜•ํƒœ์†Œ ๋ถ„์„๊ธฐ - (2)

๐ŸซLv1 | ์ „์ฒ˜๋ฆฌ | 9/14 | ํ˜•ํƒœ์†Œ ๋ถ„์„๊ธฐ - (3)

๐ŸฌLv1 | ์ „์ฒ˜๋ฆฌ | 10/14 | Bag of Words

๐ŸญLv1 | ์ „์ฒ˜๋ฆฌ | 11/14 | CountVectorizer - (1)

๐ŸกLv1 | ์ „์ฒ˜๋ฆฌ | 12/14 | CountVectorizer - (2)

๐ŸบLv1 | ์ „์ฒ˜๋ฆฌ | 13/14 | TF-IDF(Term Frequency - Inverse Document Frequency) - (1)

๐ŸปLv1 | ์ „์ฒ˜๋ฆฌ | 14/14 | TF-IDF(Term Frequency - Inverse Document Frequency) - (2)


๐ŸŒบLv1 | ๋ชจ๋ธ๋ง | 1/6 | train_test_split - (1)

๐ŸŒปLv1 | ๋ชจ๋ธ๋ง | 2/6 | train_test_split - (2)

๐ŸŒผLv1 | ๋ชจ๋ธ๋ง | 3/6 | train_test_split - (3)

๐ŸŒทLv1 | ๋ชจ๋ธ๋ง | 4/6 | train_test_split - (4)

๐Ÿฅ€Lv1 | ๋ชจ๋ธ๋ง | 5/6 | train_test_split / LGBM - (1)

๐ŸŒฑLv1 | ๋ชจ๋ธ๋ง | 6/6 | train_test_split / LGBM - (2)


๐ŸŒดLv1 | ํŠœ๋‹ | 1/2 | ํŒŒ๋ผ๋ฏธํ„ฐ / ํ•˜์ดํผ ํŒŒ๋ผ๋ฏธํ„ฐ

๐ŸŒพLv1 | ํŠœ๋‹ | 2/2 | ํ•˜์ดํผ ํŒŒ๋ผ๋ฏธํ„ฐ ํŠœ๋‹ / gird search



๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ๏ปฟ

#๋ฐ์ด์ฝ˜ #๋ฐ์ด์ฝ˜_101 #ํŒŒ์ด์ฌ #๋จธ์‹ ๋Ÿฌ๋‹ #๋”ฅ๋Ÿฌ๋‹ #์ธ๊ณต์ง€๋Šฅ #์•™์ƒ๋ธ” #์˜์‚ฌ๊ฒฐ์ •๋‚˜๋ฌด #์ฝ”๋žฉ #๋ฐ์ดํ„ฐ #๋ฐ์ดํ„ฐ๋ถ„์„ #ํŒ๋‹ค์Šค #๋„˜ํŒŒ์ด #๋ฐ์ดํ„ฐ์‚ฌ์ด์–ธํ‹ฐ์ŠคํŠธ #AI #Python # Pandas #Numpy #lightgbm #read_csv #DACON #kaggle #sckit-learn

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