项目前瞻策略布局与风险评估
一、项目前瞻：策略布局与风险评估
二、项目可行性报告范文模板概述
三、项目目标与预期收益分析
四、市场需求与竞争对手分析
五、技术和资源准备情况评估
六、财务计划与投资回报率预测
七、法律法规遵守情况及潜在风险探讨
八、实施计划与时间表制定建议
九、管理团队及其角色分配规划
十、高级别决策支持系统设计方案
```python
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
# 数据加载
data = pd.read_csv("house.csv")
X = data.drop('price', axis=1)
y = data['price']
# 特征选择
features_to_use = ['rooms', 'area']
X_selected_features = X[features_to_use]
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X_selected_features, y, test_size=0.2)
# 创建线性回归模型并训练模型
model = LinearRegression()
model.fit(X_train, y_train)
# 预测价格值
predictions=model.predict(X_test)
print(predictions)
```
```python3
import numpy as np
def sigmoid(x):
return 1 / (1 + np.exp(-x))
def derivative_sigmoid(x):
return x * (1 - x)
inputs=np.array([[0],[1],[2.5]])
weights=[-3,-4]
bias=-5
for i in range(10000):
outputs=sigmoid(np.dot(inputs, weights)+bias)
gradients=derivative_sigmoid(outputs)*(outputs-(np.dot(inputs, weights)+bias)) # 计算梯度
weights -= 0.01*gradients.T @ inputs
print(sigmoid(np.dot([2], weights)+bias))
```
```css
body {
font-family: Arial;
background-color: #f7f7f7;
margin: 10px;
padding: 20px ;
border-style:solid;
border-width:thin;
}
h3 {
text-align:center;
}
p {
text-indent:30px;
line-height:25px;
}
table{
width:50%;
margin-left:auto;
margin-right:auto;
}
th{
background-color:red ;
color:white ;
text-align:center ;
}
tr{background-color:#e6e6e6;}
td{padding-left :15%}
a{text-decoration:none;color:black;}
.button {
background-color:green;
color:white;
padding:15px 32px;
text-align:center ;
display:inline-block ;
font-size :16px ;
cursor:pointer ,
border-radius :8px ,
transition-duration:.4s ,
text-decoration:none ,
display:inline-block,
margin-top :8% .
}
