商业数据分析--金融行业收入表

前期准备

导入一些必要库

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

查看数据

df.head()

df.isnull().sum()

	工龄	薪水
0	0.0	9534
1	0.1	11667
2	0.2	11015
3	0.3	10347
4	0.4	11110

工龄 0
薪水 0
dtype: int64

plt.scatter(df['工龄'], df['薪水'])
plt.show()

plt.figure(figsize=(10, 4))
plt.subplot(1,2,1)
plt.boxplot(df['薪水'])

plt.subplot(1,2,2)
plt.boxplot(df['工龄'])
plt.show()

发现数据无异常值和缺失值，接下来我们直接使用各种模型进行应用

拆分数据

from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score

x_train, x_test, y_train, y_test = train_test_split(df[['工龄']], df['薪水'], test_size=0.2, random_state=42)

线性回归

from sklearn.linear_model import LinearRegression

lr = LinearRegression()
lr.fit(x_train, y_train)
y_pred = lr.predict(x_test)

print('mse:', mean_squared_error(y_test, y_pred))
print('r2:', r2_score(y_test, y_pred))

mse: 10883546.846848322
r2: 0.9251860541317487

随机森林

from sklearn.ensemble import RandomForestRegressor

rm = RandomForestRegressor()
rm.fit(x_train, y_train)
y_pred = rm.predict(x_test)

print('mse:', mean_squared_error(y_test, y_pred))
print('r2:', r2_score(y_test, y_pred))

mse: 3772918.5643306905
r2: 0.9740648035783578

XGBoost

from xgboost import XGBRegressor

xgb = XGBRegressor(random_state=42)
xgb.fit(x_train, y_train)
y_pred_xgb = xgb.predict(x_test)

print("MSE:", mean_squared_error(y_test, y_pred_xgb))
print("r2:", r2_score(y_test, y_pred_xgb))

MSE: 4343677.356352377
r2: 0.9701413843133868

LGBM

from lightgbm import LGBMRegressor

lgb = LGBMRegressor(random_state=42)
lgb.fit(x_train, y_train)
y_pred_lgb = lgb.predict(x_test)

print("MSE:", mean_squared_error(y_test, y_pred_lgb))
print("r2:", r2_score(y_test, y_pred_lgb))

MSE: 11092174.291805526
r2: 0.9237519405478871

SVM

from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVR
from sklearn.pipeline import Pipeline

svr_pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('svr', SVR(kernel='rbf', C=100, gamma='scale'))
])

svr_pipeline.fit(x_train, y_train)
y_pred = svr_pipeline.predict(x_test)

print("MSE:", mean_squared_error(y_test, y_pred))
print("r2:", r2_score(y_test, y_pred))

MSE: 94261363.28793661
r2: 0.3520435360157056
发现其效果并不好 SVM需要进行调参 使用网格优化

from sklearn.model_selection import GridSearchCV

param_grid = {
    'svr__C': [0.1, 1, 10, 100, 1000],
    'svr__gamma': ['scale', 'auto', 0.001, 0.01, 0.1, 1],
    'svr__kernel': ['rbf', 'poly', 'linear']
}

grid = GridSearchCV(svr_pipeline, param_grid, cv=5, scoring='r2', n_jobs=-1)
grid.fit(x_train, y_train)

print("最佳参数:", grid.best_params_)
print("最佳交叉验证r2:", grid.best_score_)

y_pred = grid.predict(x_test)

最佳参数: {‘svr__C’: 1000, ‘svr__gamma’: ‘scale’, ‘svr__kernel’: ‘linear’}
最佳交叉验证r2: 0.8618895959605389