# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in 

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import seaborn as sns

# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory

from subprocess import check_output


# Any results you write to the current directory are saved as output.

## Great - You have go monthly income vs monthly rate - makes it easy to see performance.

data = pd.read_csv('data/attrition.csv')
data.head()

pd.options.display.max_columns = None

data.describe()

pd.options.mode.chained_assignment = None

data['RealHours'] = data['DailyRate'] / data['HourlyRate'] * 10
data['HoursDelta'] = data['RealHours'] - data['StandardHours']
print(data['HoursDelta'][:15])

data['PaidOverTime'] = data['HoursDelta'] - 80
print(data['PaidOverTime'][:15])
for row, value in enumerate(data['PaidOverTime']):
    if value < 0:
        data['PaidOverTime'][row] = 0
    if value > 0:
        data['PaidOverTime'][row] = data['PaidOverTime'][row] / 1.5
    if data['OverTime'][row] == 'No':
        data['PaidOverTime'][row] = 0
        
print(data['PaidOverTime'][:15])

data['OT'] = 0
data['OT'][data['OverTime'] == 'Yes'] = 1


_ = plt.scatter(data['MonthlyRate'], data['MonthlyIncome'], c=data['OT'])
_ = plt.xlabel('Monthly Rate')
_ = plt.ylabel('Monthly Income')
_ = plt.title('Monthly Rate vs. Monthly Income')
plt.show()
print(np.corrcoef(data['MonthlyRate'], data['MonthlyIncome']))

_ = plt.scatter(data['DailyRate'], data['MonthlyRate'], c=data['OT'])
_ = plt.xlabel('Daily Rate')
_ = plt.ylabel('Monthly Rate')
_ = plt.title('Daily Rate vs. Monthly Rate')
plt.show()
print(np.corrcoef(data['DailyRate'], data['MonthlyRate']))

data['left'] = 0
data['left'][data['Attrition'] == 'Yes'] = 1
x = data['left']
print('Monthly Rate:', np.corrcoef(data['MonthlyRate'], x))
print('Daily Rate', np.corrcoef(data['DailyRate'], x))
print('Hourly Rate', np.corrcoef(data['HourlyRate'], x))
print('Monthly Income', np.corrcoef(data['MonthlyIncome'], x))
print('Log Rate Ratio', np.corrcoef(data['lograteratio'] ** 35, x))

del data['RealHours']
del data['HoursDelta']
del data['PaidOverTime']

data['lograteratio'] = data['lograteratio'] ** 35

data.head()

## In red you have those that left
from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = data['YearsAtCompany']
y = data['Age']
z = data['YearsInCurrentRole']
s = data['WorkLifeBalance']
c = data['left']
cmap = plt.get_cmap('seismic')
_ = ax.scatter(xs=x, ys=y, zs=z, c=c, cmap=cmap)
_ = ax.set_xlabel('Years at Company')
_ = ax.set_ylabel('Age')
_ = ax.set_zlabel('Years in Current Role')
_ = plt.title('')
plt.show()

young = data[(data['Age'] < 30) & (data['YearsAtCompany'] <= 2) & (data['YearsInCurrentRole'] <= 1)]
data['young'] = 0
data['young'][(data['Age'] < 30) & (data['YearsAtCompany'] <= 2) & (data['YearsInCurrentRole'] <= 1)] = 1

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = young['YearsAtCompany']
y = young['Age']
z = young['YearsInCurrentRole']
s = young['WorkLifeBalance']
c = young['left']
cmap = plt.get_cmap('seismic')
_ = ax.scatter(xs=x, ys=y, zs=z, c=c, cmap=cmap)
_ = ax.set_xlabel('Years at Company')
_ = ax.set_ylabel('Age')
_ = ax.set_zlabel('Years in Current Role')
_ = plt.title('')
plt.show()

_ = sns.boxplot(young['left'], young['Age'])
plt.show()

print(np.corrcoef(young['left'], young['Age']))
print(np.count_nonzero(young['left']) / len(young['left']))
percent1 = np.round(np.count_nonzero(young['left']) / len(young['left']) * 100, decimals=2)
print('{}% of workers aged under 30 leaves the firm'.format(percent1))
print(np.corrcoef(data['left'], data['Age']))
percent = np.round(np.count_nonzero(data['left']) / len(data['left']) * 100, decimals=2)
print('{}% of the total population leaves the firm.'.format(percent))
corr = np.corrcoef(data['young'], data['left'])
for item in corr[1]:
    print(np.round(item * 100, decimals=2),'%')

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = data['YearsAtCompany']
y = data['Age']
z = data['YearsInCurrentRole']
s = data['WorkLifeBalance']
c = data['left']
cmap = plt.get_cmap('seismic')
_ = ax.scatter(xs=x, ys=y, zs=z, c=c, cmap=cmap, s=s ** 3)
_ = ax.set_xlabel('Years at Company')
_ = ax.set_ylabel('Age')
_ = ax.set_zlabel('Years in Current Role')
_ = plt.title('')
plt.show()

mid = data[(data['Age'] > 35) & (data['Age'] <= 40) & (data['YearsAtCompany'] <= 10) & (data['YearsAtCompany'] > 2) & (data['YearsInCurrentRole'] <= 7)]
data['mid'] = 0
data['mid'][(data['Age'] > 35) & (data['Age'] <= 40) & (data['YearsAtCompany'] <= 10) & (data['YearsAtCompany'] > 2) & (data['YearsInCurrentRole'] <= 7)] = 1

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = mid['YearsAtCompany']
y = mid['Age']
z = mid['YearsInCurrentRole']
s = mid['WorkLifeBalance']
c = mid['left']
cmap = plt.get_cmap('seismic')
_ = ax.scatter(xs=x, ys=y, zs=z, c=c, cmap=cmap)
_ = ax.set_xlabel('Years at Company')
_ = ax.set_ylabel('Age')
_ = ax.set_zlabel('Years in Current Role')
_ = plt.title('')
plt.show()

print(np.count_nonzero(mid['left']) / len(mid['left']))
percent1 = np.round(np.count_nonzero(mid['left']) / len(mid['left']) * 100, decimals=2)
print('{}% of my mid-career workers leave the firm'.format(percent1))
print('There are {} mid-career employees with exceptionally low average attrition in this firm.'.format(len(mid)))
corr = np.corrcoef(data['mid'], data['left'])
for item in corr[1]:
    print(np.round(item * 100, decimals=2),'%')

## density inclusion
_ = sns.kdeplot(data = data['Age'], data2 = data['TotalWorkingYears'])
_ = plt.scatter(data['Age'], data['TotalWorkingYears'], alpha=.5, s=20, c=data['left'])
_ = plt.xlabel('Age')
_ = plt.ylabel('Tenure (in years)')
_ = plt.title('Age vs. Tenure')
plt.show()

_ = sns.kdeplot(data=data['MonthlyIncome'], data2=data['Age'])
_ = plt.scatter(data['MonthlyIncome'], data['Age'], alpha=0.5, s=20, c=data['left'])
_ = plt.xlabel('Monthly Income')
_ = plt.ylabel('Age')
_ = plt.title('Monthly Income vs. Age')
plt.show()

data['high_income'] = 0
data['high_income'][(data['Age'] >= 25) & (data['MonthlyIncome'] > 13000)] = 1

count = np.count_nonzero(data['high_income'])
print('There are {} highly paid employees with low average attrition'.format(count))
corr = np.corrcoef(data['high_income'], data['left'])
for item in corr[0]:
    l = []
    l.append(np.round(item * 100, decimals=2))
print('Correlation between this group and attrition is {}%'.format(l[0]))

from sklearn.preprocessing import LabelEncoder as LE

data['Attrition'] = LE().fit_transform(data['Attrition'])
data['Department'] = LE().fit_transform(data['Department'])
data['EducationField'] = LE().fit_transform(data['EducationField'])
data['Gender'] = LE().fit_transform(data['Gender'])
data['JobRole'] = LE().fit_transform(data['JobRole'])
data['MaritalStatus'] = LE().fit_transform(data['MaritalStatus'])
data['Over18'] = LE().fit_transform(data['Over18'])
data['OverTime'] = LE().fit_transform(data['OverTime'])
data['BusinessTravel'] = LE().fit_transform(data['BusinessTravel'])
del data['left']
del data['OT']
del data['EmployeeNumber']
del data['EmployeeCount']

from sklearn.model_selection import train_test_split
from sklearn.cluster import KMeans

X = data
y = data['Attrition']
del X['Attrition']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)


cluster = KMeans(n_clusters=80, random_state=42).fit_predict(X_train)
X_train['cluster'] = cluster
X_train['cluster'].plot(kind='hist', bins=80)
_ = plt.xlabel('Cluster')
_ = plt.ylabel('Count')
_ = plt.title('Histogram of Clusters')
plt.show()

_ = plt.scatter(x=X_train['Age'], y=X_train['DailyRate'], c=X_train['cluster'], cmap='Blues')
_ = sns.kdeplot(data=X_train['Age'], data2=X_train['DailyRate'])
_ = plt.xlabel('Age')
_ = plt.ylabel('Daily Rate')
_ = plt.title('Clusters within Age/Daily Rate')
plt.show()

x = np.corrcoef(X_train['cluster'], y_train)
print(x)

cluster = KMeans(n_clusters=80, random_state=42).fit_predict(X_test)
X_test['cluster'] = cluster

from sklearn.ensemble import RandomForestClassifier as RFC
from sklearn.metrics import accuracy_score

fmodel = RFC(n_estimators=100, random_state=42, max_depth=11, max_features=11).fit(X_train, y_train)
prediction = fmodel.predict(X_test)
score = accuracy_score(y_test, prediction)
print(score)

from sklearn.svm import SVC

model = SVC(random_state=42).fit(X_train, y_train)
prediction = model.predict(X_test)
score = accuracy_score(y_test, prediction)
print(score)

from sklearn.ensemble import AdaBoostClassifier as ABC

model = ABC(n_estimators=100, random_state=42, learning_rate=.80).fit(X_train, y_train)
prediction = model.predict(X_test)
score = accuracy_score(y_test, prediction)
print(score)

from sklearn.ensemble import BaggingClassifier as BC

model = BC(n_estimators=100, random_state=42).fit(X_train, y_train)
prediction = model.predict(X_test)
score = accuracy_score(y_test, prediction)
print(score)

from sklearn.ensemble import ExtraTreesClassifier as XTC

model = XTC(n_estimators=100, random_state=42, criterion='entropy', max_depth=20).fit(X_train, y_train)
prediction = model.predict(X_test)
score = accuracy_score(y_test, prediction)
print(score)