Python机器学习实战-建立随机森林模型预测肾脏疾病（附源码和实现效果）

实现功能

建立随机森林模型预测肾脏疾病

实现代码

import pandas as pd
import warnings
warnings.filterwarnings("ignore")
pd.set_option('display.max_columns', 26)

#==========================读取数据======================================
df = pd.read_csv("E:\数据杂坛\datasets\kidney_disease.csv")
df=pd.DataFrame(df)
pd.set_option('display.max_rows', None)
pd.set_option('display.width', None)
df.drop("id",axis=1,inplace=True)
print(df.head())
print(df.dtypes)
df["classification"] = df["classification"].apply(lambda x: x if x == "notckd" else "ckd")
# 分类型变量名
cat_cols = [col for col in df.columns if df[col].dtype == "object"]
# 数值型变量名
num_cols = [col for col in df.columns if df[col].dtype != "object"]

# ========================缺失值处理============================
def random_value_imputate(col):
    """
    函数：随机填充方法（缺失值较多的字段）
    """

    # 1、确定填充的数量；在取出缺失值随机选择缺失值数量的样本
    random_sample = df[col].dropna().sample(df[col].isna().sum())
    # 2、索引号就是原缺失值记录的索引号
    random_sample.index = df[df[col].isnull()].index
    # 3、通过loc函数定位填充
    df.loc[df[col].isnull(), col] = random_sample


def mode_impute(col):
    """
    函数：众数填充缺失值
    """
    # 1、确定众数
    mode = df[col].mode()[0]
    # 2、fillna函数填充众数
    df[col] = df[col].fillna(mode)

for col in num_cols:
    random_value_imputate(col)

for col in cat_cols:
    if col in ['rbc','pc']:
        # 随机填充
        random_value_imputate('rbc')
        random_value_imputate('pc')
    else:
        mode_impute(col)

# ======================特征编码============================
from sklearn.preprocessing import MinMaxScaler
mms = MinMaxScaler()
df[num_cols] = mms.fit_transform(df[num_cols])

from sklearn.preprocessing import LabelEncoder
led = LabelEncoder()
for col in cat_cols:
    df[col] = led.fit_transform(df[col])

print(df.head())

#===========================数据集划分===============================
X = df.drop("classification",axis=1)
y = df["classification"]
from sklearn.utils import shuffle
df = shuffle(df)
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)

#===========================建模=====================================
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

def create_model(model):
    # 模型训练
    model.fit(X_train, y_train)
    # 模型预测
    y_pred = model.predict(X_test)
    # 准确率acc
    acc = accuracy_score(y_test, y_pred)
    # 混淆矩阵
    cm = confusion_matrix(y_test, y_pred)
    # 分类报告
    cr = classification_report(y_test, y_pred)

    print(f"Test Accuracy of {model} : {acc}")
    print(f"Confusion Matrix of {model}: \n{cm}")
    print(f"Classification Report of {model} : \n {cr}")

from sklearn.ensemble import RandomForestClassifier

rd_clf = RandomForestClassifier(criterion = 'entropy',
                                max_depth = 11,
                                max_features = 'auto',
                                min_samples_leaf = 2, min_samples_split = 3, n_estimators = 130)
create_model(rd_clf)

实现效果

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