import load_baseline_train_data
from sklearn.model_selection import train_test_split
import xgboost as xgb
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.metrics.classification import accuracy_score,classification_report


df = load_baseline_train_data.get_baseline_data()

Y = df['label']
X = df.drop(columns= ['label'])


X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42,stratify=Y)

LR = LogisticRegression(random_state=0, solver='lbfgs', multi_class='ovr').fit(X_train,y_train)
print(X.columns.values)
print(LR.coef_[0])
predictions = LR.predict(X_test)
score = accuracy_score(y_test,predictions)
#score = LR.score(X_test, y_test)
print(score)
print(classification_report(y_test,predictions))


a86fcdc6492b7cd5053db720c8bbdb5fbb7b8b90
<mask token>

<mask token>
print(X.columns.values)
print(LR.coef_[0])
<mask token>
print(score)
print(classification_report(y_test, predictions))

<mask token>
df = load_baseline_train_data.get_baseline_data()
Y = df['label']
X = df.drop(columns=['label'])
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2,
    random_state=42, stratify=Y)
LR = LogisticRegression(random_state=0, solver='lbfgs', multi_class='ovr').fit(
    X_train, y_train)
print(X.columns.values)
print(LR.coef_[0])
predictions = LR.predict(X_test)
score = accuracy_score(y_test, predictions)
print(score)
print(classification_report(y_test, predictions))

import load_baseline_train_data
from sklearn.model_selection import train_test_split
import xgboost as xgb
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.metrics.classification import accuracy_score, classification_report
df = load_baseline_train_data.get_baseline_data()
Y = df['label']
X = df.drop(columns=['label'])
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2,
    random_state=42, stratify=Y)
LR = LogisticRegression(random_state=0, solver='lbfgs', multi_class='ovr').fit(
    X_train, y_train)
print(X.columns.values)
print(LR.coef_[0])
predictions = LR.predict(X_test)
score = accuracy_score(y_test, predictions)
print(score)
print(classification_report(y_test, predictions))

import load_baseline_train_data
from sklearn.model_selection import train_test_split
import xgboost as xgb
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.metrics.classification import accuracy_score,classification_report


df = load_baseline_train_data.get_baseline_data()

Y = df['label']
X = df.drop(columns= ['label'])


X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42,stratify=Y)

LR = LogisticRegression(random_state=0, solver='lbfgs', multi_class='ovr').fit(X_train,y_train)
print(X.columns.values)
print(LR.coef_[0])
predictions = LR.predict(X_test)
score = accuracy_score(y_test,predictions)
#score = LR.score(X_test, y_test)
print(score)
print(classification_report(y_test,predictions))

