Player Wage Prediction

练习网址：https://www.kaggle.com/chirag02/fifa19-player-wage-prediction/notebook

代码练习：

# -*- coding: UTF-8 -*-

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

import sys
import io
sys.stdout = io.TextIOWrapper( sys.stdout.buffer, encoding='utf8')


df = pd.read_csv("data.csv")
# pd.set_option("display.max_columns",None)
# pd.set_option("display.max_rows",None)
# pd.set_option('max_colwidth',1000)

#.iloc根据标签索引值来选取值，打印df的前5行，前3列。head()默认打印前5行
# print(df.head().iloc[:,:4]) 

# 删除某列数据，implace=True改变原始数据
df.drop(columns=["Unnamed: 0"],inplace=True)  
# 返回随机样本
# print(df.sample(n=5))
# print(df.describe())
bins = np.arange(df["Overall"].min(),df["Overall"].max()+1,1)
# print(bins)
# print(df["Overall"])
# print(df.groupby(["Overall"]).size()[70])
#设置图形显示大小
# plt.figure(figsize=[8,5])
# # 绘制直方图,Overall分布情况
# plt.hist(df["Overall"],bins=bins)
# plt.title("Overall Rating Distribution")
# plt.xlabel("Mean Overall Rating")
# plt.ylabel("Count")
# plt.show()

# plt.figure(figsize=[16,5])
# plt.suptitle("Overall Rating Vs Age",fontsize=16)

# plt.subplot(1,2,1)
# bin_x = np.arange(df["Age"].min(),df["Age"].max()+1,1)
# bin_y = np.arange(df["Overall"].min(),df["Overall"].max()+2,2)
# plt.hist2d(x = df["Age"],y=df["Overall"],cmap="YlGnBu",bins=[bin_x,bin_y])
# plt.colorbar()
# plt.xlabel("Age(years)")
# plt.ylabel("Overall Rating")

# plt.subplot(1,2,2)
# plt.scatter(x=df["Age"],y=df["Overall"],alpha=0.25,marker=".")
# plt.xlabel("Age(years)")
# plt.ylabel("Overall")
# plt.show()

# sb.jointplot(x=df.Overall,y=df.Potential,kind="kde")
# plt.show()

# plt.figure(figsize=[8,5])
# plt.scatter(x=df.Overall,y=df.Potential,c=df.Age,alpha=0.25,cmap="rainbow")
# plt.colorbar().set_label("Age")
# plt.xlabel("Overall Rating")
# plt.ylabel("Potential")
# plt.show()

df_opa = df[["ID","Name","Age","Overall","Potential","Value","Wage"]]
# print(df_opa.head())

def currencystrtoint(amount):
    new_amount=[]
    for s in amount:
        list(s)
        abbr = s[-1]
        if abbr is "M":
            s = s[1:-1]
            s = float("".join(s))
            s *= 1000000
        elif abbr is "K":
            s = s[1:-1]
            s = float("".join(s))
            s *= 1000
        else:
            s = 0
        new_amount.append(s)
    return new_amount

df_opa["Value"] = currencystrtoint(list(df_opa["Value"]))
df_opa["Wage"] = currencystrtoint(list(df_opa["Wage"]))
# print(df_opa.describe())
# sb.pairplot(df_opa)
# plt.show()
# sb.lmplot(data=df_opa,x="Overall",y="Wage",order=2,scatter_kws={"alpha":0.3,"color":"y"})
# plt.show()

'''选择最佳度来预测工资'''
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn import metrics

x = df_opa[["Age","Overall","Potential","Value"]]
y = df_opa["Wage"]

Xtrain,Xtest,ytrain,ytest = train_test_split(x,y,test_size=0.3,random_state=101)

def pred_wage(degree,Xtrain,Xtest,ytrain):
    if degree > 1:        
        poly = PolynomialFeatures(degree = degree) 
        Xtrain = poly.fit_transform(Xtrain)
        Xtest = poly.fit_transform(Xtest)
    lm = LinearRegression()
    lm.fit(Xtrain,ytrain)
    wages = lm.predict(Xtest)
    return wages

MAE,MSE,RMSE = [],[],[]
for i in range(1,11):
    predicted_wages = pred_wage(i,Xtrain,Xtest,ytrain)
    # metrics.mean_absolute_error求平均绝对误差回归损失
    MAE.append(metrics.mean_absolute_error(ytest,predicted_wages))
    # metrics.mean_squared_error求均方平均误差回归损失
    MSE.append(metrics.mean_squared_error(ytest,predicted_wages))
    # 均方平均误差回归损失的平方根
    RMSE.append(np.sqrt(metrics.mean_squared_error(ytest,predicted_wages)))
# print(MAE)
# plt.figure(figsize=[11,8])
# plt.subplot(2,2,1)
# plt.plot(MAE,color="red")
# plt.xlabel("Degree")
# plt.ylabel("Mean Absolute Error")
# plt.subplot(2,2,2)
# plt.plot(MSE,color="green")
# plt.xlabel("Degree")
# plt.ylabel("Mean Squared Error")
# plt.subplot(2,2,3)
# plt.plot(RMSE,color="yellow")
# plt.xlabel("Dregree")
# plt.ylabel("Root Mean Squared Error")
# plt.show()


'''degre=2'''

predicted_wages = pred_wage(2,Xtrain,Xtest,ytrain)
# sb.regplot(ytest,predicted_wages,scatter_kws={"alpha":0.3,"color":"y"})
# plt.xlabel("Actual Wage")
# plt.ylabel("Predicted Wage")
# plt.show()

sb.distplot(ytest-predicted_wages)
plt.axis([-50000,50000,0,0.00016])
plt.show()