Employee Retention Prediction in Logistic Regression in ML
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Program 1
import pandas as pd from matplotlib import pyplot as plt df = pd.read_csv("D://scikit_data/Employee/HR_comma_sep.csv") df.head(10) left = df[df.left==1] left.shape left.shape left retained = df[df.left==0] retained.shape retained pd.crosstab(df.salary,df.left).plot(kind='bar') pd.crosstab(df.Department,df.left).plot(kind='bar') subdf = df[['satisfaction_level','average_montly_hours','promotion_last_5years','salary']] subdf.head() salary_dummies = pd.get_dummies(subdf.salary, prefix="salary") salary_dummies df_with_dummies = pd.concat([subdf,salary_dummies],axis='columns') df_with_dummies.head() df_with_dummies.drop('salary',axis='columns',inplace=True) df_with_dummies.head() x = df_with_dummies # Independed variables x.head() y = df.left # Depended variable from sklearn.model_selection import train_test_split x_train,x_test, y_train, y_test = train_test_split(x,y,train_size=0.3) len(x_train) len(x_test) from sklearn.linear_model import LogisticRegression model=LogisticRegression() model.fit(x_train,y_train) model.predict(x_test) model x=model.predict(x_test) print(x) model.score(x_test,y_test) model.score(x_train,y_train) x_test model.predict(x_test) model.predict([[0,0,0,0,0,1]]) model.predict([[0,0,0,1,0,0]]) model.predict([[1,0,1,0,0,1]]) model.predict([[0,0,1,1,0,0]]) model.predict([[1,0,0,0,1,0]])
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