Assignment 3

01_materials/notebooks/Classification-1.ipynb

@@ -2322,6 +2322,13 @@
     "  \n",
     "By the end, we also used the `scikit-learn` library to automate the KNN process, making the classification task simpler and more efficient. We hope this notebook has given you a hands-on understanding of data visualization, classification, and the practical use of machine learning algorithms like KNN. Don’t hesitate to experiment more with the dataset or the code to deepen your learning!\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

01_materials/notebooks/Regression-2.ipynb

@@ -87,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -326,7 +326,7 @@
        "[813 rows x 12 columns]"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1572,11 +1572,43 @@
     "\n",
     "We hope this notebook has provided a practical understanding of data regression, model evaluation, and the application of machine learning algorithms like linear regression. Feel free to experiment further with the dataset or the code to enhance your learning!"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.model_selection import cross_validate\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "from sklearn import set_config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lm.coef_#slope of yjr line; b1"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "base",
+   "display_name": "lcr-env (3.11.14)",
    "language": "python",
    "name": "python3"
   },
@@ -1590,7 +1622,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.11.14"
   }
  },
  "nbformat": 4,