semvec/pypi-packages huggingface dataset:
query-domain-icp 0.0.3
获取域名 ICP 备案信息 自动破解滑动验证码 支持翻页

Python 获取域名 ICP 备案信息 自动破解滑动验证码 支持翻页

数据来源工信部备案系统 https://beian.miit.gov.cn/

本项目仅供学习交流请勿用于非法用途
housing-library-5506 0.1
Sample code for coding practice
Requires Python: >=3.11
Keywords: housing,data training

# Median housing value prediction

The housing data can be downloaded from https://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data. 

The following techniques have been used: 

 - Linear regression
 - Decision Tree
 - Random Forest

## Steps performed
 - We prepare and clean the data. We check and impute for missing values.
 - Features are generated and the variables are checked for correlation.
 - Multiple sampling techinuqies are evaluated. The data set is split into train and test.
 - All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.

## To excute the script
```
python3 nonstandard.py
```
## Command to create environment from env.yml file 
```
conda env create -f env.yml
```
## Command to activate the environment
```
conda activate mle-dev
```