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2-Regression/1-Tools/notebook.ipynb

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+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.9.5 64-bit"
+  },
+  "interpreter": {
+   "hash": "ac59ebe37160ed0dfa835113d9b8498d9f09ceb179beaac4002f036b9467c963"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Welcome to your notebook"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "hello notebook\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('hello notebook')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt     #for line plot\n",
+    "import numpy as np                  #handling numeric data \n",
+    "from sklearn import datasets, linear_model, model_selection\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(442, 10)\n[ 0.03807591  0.05068012  0.06169621  0.02187235 -0.0442235  -0.03482076\n -0.04340085 -0.00259226  0.01990842 -0.01764613]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load the diabetes dataset\n",
+    "X, y = datasets.load_diabetes(return_X_y=True)\n",
+    "print(X.shape)\n",
+    "print(X[0])"
+   ]
+  },
+  {
+   "source": [
+    "Target: Column 11 is a quantitative measure of disease progression one year after baseline\n",
+    "\n",
+    "Given this target, the data set is demonstrating disease progression for the each patient with some specific features."
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = X[:, np.newaxis, 2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ]
+}