程序代写 1/20/2020 don-t-know-why-employees-leave-read-this

1/20/2020 don-t-know-why-employees-leave-read-this
It seems that employees working hard and with many projects have a better evaluation. (corr(number_project,last_evaluation) : 0.35, corr(average_montly_hours,last_evaluation) : 0.34 ).
The most important thing in this correlation matrix is the negative correlation between ‘left’ and ‘satifaction_level’ (-0.39) : employees leave because they are not happy at work ?
Is that the only main reason ? Is there employee patterns that can explained that ?
To adress these questions, i performed a detailed analysis.
4. Detailed data exploration
Firslty, i will perform a dimensionality reduction in order to identify groups.
4.1 Normalisation and dimensionalty reduction
g = sns.heatmap(dataset.corr(),annot=True,cmap=”RdYlGn”)
dataset = dataset.drop(labels=[“sales”],axis = 1)
dataset[“salary”] = dataset[“salary”].astype(“category”,ordered=True, categories = [‘low’,’medium’,’high’]).cat.codes
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1/20/2020 don-t-know-why-employees-leave-read-this
Don’t forget to normalize the data before the demensionality reduction.
satisfaction_level last_evaluation number_project average_montly_hours time_spend_co
0 0.80 0.86 5 262 1 0.11 0.88 7 272 2 0.85 0.91 5 226 3 0.11 0.93 7 308 4 0.10 0.95 6 244
Because of the size of the dataset the isomap algorithm is very memory greedy. So i randomly choosed a 10 000 points in the dataset.
The isomap and pca maps are very similar to the ones obtained from the full dataset and are much faster to compute.
# pca/isomap analysis
N = StandardScaler()
N.fit(dataset)
dataset_norm = N.transform(dataset)
index = np.random.randint(0,dataset_norm.shape[0],size=10000)
dataset.head()
pca = PCA(n_components=2)
pca_representation = pca.fit_transform(dataset_norm[index])
iso = Isomap(n_components=2, n_neighbors=40)
iso_representation = iso.fit_transform(dataset_norm[index])
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1/20/2020 don-t-know-why-employees-leave-read-this
left_colors = dataset[“left”].map(lambda s : “g” if s==0 else “r”)
fig, axes = plt.subplots(1,2,figsize=(15,6))
axes[0].scatter(pca_representation[:,0],pca_representation[:,1],
c = left_colors[index],alpha=0.5,s=20)
axes[0].set_title(“Dimensionality reduction with PCA”)
axes[0].legend([“Left employee”])
axes[1].scatter(iso_representation[:,0],iso_representation[:,1],
c = left_colors[index],alpha=0.5,s=20)
axes[1].set_title(“Dimensionality reduction with Isomap”)
axes[1].legend([“Left employee”])

The red points correspond to employees who left. Here the PCA doesn’t show a great separation between the left and stayed employees. PCA performs a linear demensionality reduction , the components produced by pca is a linear combinaison of the existing features. So it is very good when we have a linear relation between the points.
Here it seems that we need a non linear reduction like isomap does. We can see a great separation between the red and green points. An interesting fact is that we have two groups of employees who stayed (green points).
Let’s represent this with an interactive plot
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1/20/2020 don-t-know-why-employees-leave-read-this
source_dataset = ColumnDataSource(
data = dict(
x = iso_representation[:2000,0],
y = iso_representation[:2000,1],
desc = dataset.loc[index,”left”],
colors = [“#%02x%02x%02x” % (int(c*255), int((1-c)*255), 0) for c in dataset.loc[index,”left”]],
satisfaction_level = dataset.loc[index,’satisfaction_level’],
last_evaluation = dataset.loc[index,’last_evaluation’],
number_project = dataset.loc[index,’number_project’],
time_spend_company = dataset.loc[index,’time_spend_company’],
average_montly_hours = dataset.loc[index,’average_montly_hours’]))
hover = HoverTool(tooltips=[(“Left”,
(“Satisf. level”,
(“#projects”,
(“Last eval.”,
(“Time in Company”,
(“Montly hrs”,
tools_isomap = [hover, “box_zoom”,’pan’, ‘wheel_zoom’, ‘reset’]
plot_isomap = figure(plot_width= 800, plot_height=600, tools=tools_isomap,
title=’Isomap projection of employee data’)
plot_isomap.scatter(‘x’, ‘y’, size=7, fill_color = “colors”, line_color = None, fill_alpha = 0.6, radius=0.1, alpha=0.5, line_width=0,
show(plot_isomap)
source=source_dataset)
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1/20/2020 don-t-know-why-employees-leave-read-this
You can hover the data to see the major features.
4.2 Global Radar Chart
For practical reasons, i separate the left and stay data.
data_stay = dataset[dataset[“left”]==0]
data_left = dataset[dataset[“left”]==1]
file:///home/jfagan/Downloads/don-t-know-why-employees-leave-read-this.html

1/20/2020 don-t-know-why-employees-leave-read-this
def _scale_data(data, ranges):
(x1, x2) = ranges[0]
d = data[0]
return [(d – y1) / (y2 – y1) * (x2 – x1) + x1 for d, (y1, y2) in zip(data, r
class RadarChart():
def __init__(self, fig, variables, ranges, n_ordinate_levels = 6):
angles = np.arange(0, 360, 360./len(variables))
axes = [fig.add_axes([0.1,0.1,0.8,0.8],polar = True,
label = “axes{}”.format(i)) for i in range(len(variables))] _, text = axes[0].set_thetagrids(angles, labels = variables)
for txt, angle in zip(text, angles): txt.set_rotation(angle – 90)
for ax in axes[1:]: ax.patch.set_visible(False) ax.xaxis.set_visible(False) ax.grid(“off”)
for i, ax in enumerate(axes):
grid = np.linspace(*ranges[i],num = n_ordinate_levels) grid_label = [“”]+[“{:.1f}”.format(x) for x in grid[1:]] ax.set_rgrids(grid, labels = grid_label, angle = angles[i]) ax.set_ylim(*ranges[i])
self.angle = np.deg2rad(np.r_[angles, angles[0]])
self.ranges = ranges
self.ax = axes[0]
def plot(self, data, *args, **kw):
sdata = _scale_data(data, self.ranges) self.ax.plot(self.angle, np.r_[sdata, sdata[0]], *args, **kw)
def fill(self, data, *args, **kw):
sdata = _scale_data(data, self.ranges) self.ax.fill(self.angle, np.r_[sdata, sdata[0]], *args, **kw)
def legend(self, *args, **kw): self.ax.legend(*args, **kw)
attributes = [‘satisfaction_level’,’last_evaluation’,’number_project’,
‘average_montly_hours’,’time_spend_company’]
data_stay_mean = data_stay[attributes].mean().values.reshape(1,-1)
data_left_mean = data_left[attributes].mean().values.reshape(1,-1)
datas = np.concatenate((data_stay_mean,data_left_mean),axis = 0)
ranges = [[1e-2, dataset[attr].max()] for attr in attributes] colors = [“green”,”red”]
left_types = [“Stayed”,”Left”]
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1/20/2020 don-t-know-why-employees-leave-read-this
fig = plt.figure(figsize=(8, 8))
radar = RadarChart(fig, attributes, ranges)
for data, color, left_type in zip(datas, colors, left_types): radar.plot(data, color = color, label = left_type, linewidth=2.0) radar.fill(data, alpha = 0.2, color = color)
radar.legend(loc = 1, fontsize = ‘medium’)
plt.title(‘Stats of employees who stayed and left’)
plt.show()
This radar chart doesn’t show so many diffrences between left and stayed employees. At first glance the main difference seems to be the satisfaction level.
As we demonstrate above, employees who left are less happy than the others.
However this radar chart is build on the mean of each feature, so it could hide some sub-distributions in the data.
Let’s investigate this in further analysis.
4.3 Left and other features
file:///home/jfagan/Downloads/don-t-know-why-employees-leave-read-this.html

1/20/2020 don-t-know-why-employees-leave-read-this
fig, axs = plt.subplots(nrows=1,ncols=2,figsize=(10,6))
sns.factorplot(y=”satisfaction_level”,x=”left”,data=dataset,kind=”box”, ax=axs[0
axs[1].hist(data_stay[“satisfaction_level”],bins=6,label=”Stay”,alpha=0.7)
axs[1].hist(data_left[“satisfaction_level”],bins=6,label=”Left”,alpha=0.7)
axs[1].set_xlabel(“Satifaction level”)
axs[1].set_ylabel(“Count”)
axs[1].legend()
plt.tight_layout()
plt.gcf().clear()