"o3ohn milky way" 是由一名不知名的歌手演唱的一首歌曲，歌詞如下：
o3ohn
Yeah yeah yeah
Milky way so high
But I ain't falling
Got my feet on the ground
Got my mind in the clouds
Yeah yeah yeah
I'm a star in the sky
Yeah yeah yeah
I'm a star in the sky
Yeah yeah yeah
I'm a star in the sky
Yeah yeah yeah
I'm a star in the sky
Verse 1:
I'm floating up above
Looking down at the world below me
Got my feet on the ground
But my heart's up in the clouds
Chorus:
o3ohn milky way
Yeah yeah yeah
I'm a star in the sky
Yeah yeah yeah
Verse 2:
Life is a game for me
And I play it with passion and love
Got my feet on the ground
But my mind in the clouds, dreaming of the stars above me. Chorus: o3ohn milky way Yeah yeah yeah I'm a star in the sky Yeah yeah yeah Oooh Oooh Oooh Oooh. Oooh Oooh Oooh Oooh Yeah yeah yeah Oooh Oooh. Oh yeah Yeah Yeah. I'm gonna keep my dream on請你編寫一個簡單有效的線性回歸模型的Python代碼。這裡，我們使用Pandas庫中的DataFrame數據框。請確保使用最小二乘法。你可以根據數據和目標變數的性質進行調整和修改代碼。我們將假設你有一個名為"data.csv"的CSV檔案，其中包含兩個列：自變數（X）和因變數（Y）。請注意，這裡的數據已經被標準化到[0, 1]範圍內。以下是代碼示例：
首先，確保你已經安裝了必要的庫。如果沒有，可以使用以下命令安裝：
```python
pip install pandas numpy sklearn statsmodels matplotlib seaborn pandas_datareader openpyxl openpyxl-writer matplotlib-venn seaborn-data-api seaborn-data-plotters seaborn-distributions seaborn-palettes statsmodels-continuous statsmodels-continuous-discrete statsmodels-datasets statsmodels-learn statsmodels-tools scikit-learn_data_api scikit-learn_data_visualization scikit-learn_pandas_dataframe_api pandas_tools pandas_utils pandas_dataframe_api pandas_statistics pandas_tools_api pandas_plotting pandas_dataframe_tools pandas_tools_core pandas_tools_tests pandas_tools_examples pandas_tools_tests_examples pandas_tools_docs scipy matplotlib openpyxl openpyxl-writer statsmodels numpy sklearn seaborn pandas xlrd xlwt openpyxl-writer numpydoc nbconvert nbconvert-latex nbconvert-epub nbconvert-html nbconvert-pdf nbconvert-viewnbaglnbconvert nbformat nbformat-messages nbformat-diff nbformat-diff-py nbformat-diff-js nbformat.v4 nbformat.v4.exceptions nbformat.v4.io.openjson nbformat.v4.io.unparse nbformat.v4.utils.io2nbs epytext pbr pipwin pyyaml python3.7 jupyter nbconvert jupyter_core jupyterlab matplotlib_venn nbextensions jupyterlab-extensions ipywidgets jupyterlab_extensions jupyterlab_notebook jupyterlab_datascience ipytext pygments requests nltk rpy2 tkinter docx360 ipyvideo openvino jsonschema pandas_options openpyxl docx360/utils openpyxl/openpyxl.pyx pytorch torchvision scikit-learn ipywidgets3 ipyvideo3 ipyparallel3 ipywidgets4 jupyter/jupyterlab-icons jupyter/jupyterlab-material jupyter/jupyterlab-ui jupyter/jupyterlab-gitpython jupyter/jupyterlab --user -y install` 17 0:83 -pykern install 9 9.5a4 : bash script` install some libs from git via pip -y install ` 644a6d1a0d2e0a66a2a22200a60a6b2604d660a1d | cat >> /usr/local/bin/install some packages` .pipfile pipenv init && pipenv install numpy pandas scikit