diff --git a/.gitignore b/.gitignore
index c77715b..a387d0f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,12 +1,13 @@
+# Specific to this repo
+develop_log.md
+*hw*
+
# Compiled source
*.o
*.out
*.so
*.exe
-develop_log.md
-*hw*
-
# Jupyter Notebook
.ipynb_checkpoints
diff --git a/regression/[play] polynomial regression.ipynb b/regression/[play] polynomial regression.ipynb
new file mode 100644
index 0000000..24d0373
--- /dev/null
+++ b/regression/[play] polynomial regression.ipynb
@@ -0,0 +1,480 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "792c583e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.preprocessing import PolynomialFeatures\n",
+ "from sklearn.linear_model import LinearRegression"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "4f50f5fd",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
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+ " \n",
+ " \n",
+ " x1 \n",
+ " x2 \n",
+ " y \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 16.243454 \n",
+ " 13.413857 \n",
+ " 570.412369 \n",
+ " \n",
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+ " 1 \n",
+ " 6.117564 \n",
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+ " \n",
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+ " \n",
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+ "
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+ "
100 rows × 3 columns
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 4))\n",
+ "\n",
+ "ax[0].scatter(x1, y)\n",
+ "ax[1].scatter(x2, y)\n",
+ "\n",
+ "for i in range(2):\n",
+ " ax[i].set_title(f'y v.s. x{i}')\n",
+ " ax[i].set_xlabel(f'x{i}')\n",
+ " ax[i].set_ylabel('y')\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "82f2b66f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X, y = df[[\"x1\", \"x2\"]], df[\"y\"]\n",
+ "\n",
+ "poly = PolynomialFeatures(degree=2, include_bias=False)\n",
+ "#poly = PolynomialFeatures(degree=2, include_bias=False, interaction_only = True)\n",
+ "poly_features = poly.fit_transform(X)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "cb4d561d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(100, 5)"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "poly_features.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "8799040e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Column names in poly_features: ['x1' 'x2' 'x1^2' 'x1 x2' 'x2^2']\n"
+ ]
+ }
+ ],
+ "source": [
+ "feature_names = poly.get_feature_names_out()\n",
+ "print(\"Column names in poly_features:\", feature_names)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "ebf1cd54",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X_train, X_test, y_train, y_test = train_test_split(poly_features, y, test_size=0.3, random_state=42)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "e547e5eb",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "LinearRegression() In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.