From 580fc066b7c97b17f7acb475e37f140e3e3d1b0f Mon Sep 17 00:00:00 2001
From: EnverJ <53200160+EnverJ@users.noreply.github.com>
Date: Wed, 5 Nov 2025 21:29:19 +0000
Subject: [PATCH] diabetes prediction

---
 2-Regression/1-Tools/notebook.ipynb          |   88 ++
 2-Regression/1-Tools/solution/notebook.ipynb | 1158 ++++++++++--------
 2 files changed, 711 insertions(+), 535 deletions(-)

diff --git a/2-Regression/1-Tools/notebook.ipynb b/2-Regression/1-Tools/notebook.ipynb
index e69de29bb..c00860ea5 100644
--- a/2-Regression/1-Tools/notebook.ipynb
+++ b/2-Regression/1-Tools/notebook.ipynb
@@ -0,0 +1,88 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c50398ed",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "aaf49431",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[9 0 8]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np \n",
+    "a = np.array([[9,0,8],[7,5,4],[4,5,3],[1,2,3],[2,3,4]])\n",
+    "print(a[0])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "106b79cb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed3a7910",
+   "metadata": {},
+   "source": [
+    "# Welcome to your notebook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1f632132",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello world, I am learning ML\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Hello world, I am learning ML\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2-Regression/1-Tools/solution/notebook.ipynb b/2-Regression/1-Tools/solution/notebook.ipynb
index 7b32a8f59..fe8441720 100644
--- a/2-Regression/1-Tools/solution/notebook.ipynb
+++ b/2-Regression/1-Tools/solution/notebook.ipynb
@@ -16,25 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "from sklearn import datasets, linear_model, model_selection\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Load the diabetes dataset, divided into `X` data and `y` features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -48,577 +30,673 @@
     }
    ],
    "source": [
-    "X, y = datasets.load_diabetes(return_X_y=True)\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import datasets, linear_model, model_selection\n",
+    "\n",
+    "# Load the diabetes dataset\n",
+    "X, y= datasets.load_diabetes(return_X_y=True)\n",
+    "#   Print the shape of the data and the first row\n",
     "print(X.shape)\n",
     "print(X[0])"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Select just one feature to target for this exercise"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(442,)\n"
+      "(442,)\n",
+      "(442, 1)\n"
      ]
     }
    ],
    "source": [
-    "# Selecting the 3rd feature\n",
-    "X = X[:, 2]\n",
-    "print(X.shape)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(442, 1)\n",
-      "[[ 0.06169621]\n",
-      " [-0.05147406]\n",
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-      " [-0.01159501]\n",
-      " [-0.03638469]\n",
-      " [-0.04069594]\n",
-      " [-0.04716281]\n",
-      " [-0.00189471]\n",
-      " [ 0.06169621]\n",
-      " [ 0.03906215]\n",
-      " [-0.08380842]\n",
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-      " [-0.06009656]\n",
-      " [ 0.00133873]\n",
-      " [-0.02345095]\n",
-      " [-0.07410811]\n",
-      " [ 0.01966154]\n",
-      " [-0.01590626]\n",
-      " [-0.01590626]\n",
-      " [ 0.03906215]\n",
-      " [-0.0730303 ]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Reshaping to get a 2D array\n",
-    "X = X.reshape(-1, 1)\n",
+    "#  Extract the column at index 2 bmi\n",
+    "X = X[:,2]\n",
     "print(X.shape)\n",
-    "print(X)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Split the training and test data for both `X` and `y`"
+    "\n",
+    "#  Shape to a 2D array\n",
+    "X = X.reshape(-1,1)\n",
+    "print(X.shape)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
-    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Select the model and fit it with the training data"
+    "#  split the data into training and testing data\n",
+    "X_train, X_test,y_train, y_test = model_selection.train_test_split(X,y, test_size=0.33)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy_X',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy_X&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">tol&nbsp;</td>\n",
+       "            <td class=\"value\">1e-06</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('positive',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">positive&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
       ],
       "text/plain": [
        "LinearRegression()"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "model = linear_model.LinearRegression()\n",
-    "model.fit(X_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use test data to predict a line"
+    "#  Create a linear regression model and train it with our data\n",
+    "model=linear_model.LinearRegression()\n",
+    "model.fit(X_train,y_train)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
-    "y_pred = model.predict(X_test)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Display the results in a plot"
+    "#  Predict using our test data\n",
+    "y_pred = model.predict(X_test)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -628,15 +706,25 @@
     }
    ],
    "source": [
-    "plt.scatter(X_test, y_test,  color='black')\n",
+    "#  create a scatter plot\n",
+    "plt.scatter(X_test, y_test, color='black')\n",
+    "\n",
+    "# Plot the prediction\n",
     "plt.plot(X_test, y_pred, color='blue', linewidth=3)\n",
+    "\n",
+    "# Add labels and a title\n",
+    "plt.xlabel('Scale BMIs')\n",
+    "plt.ylabel('Disease Progression')\n",
+    "plt.title('A Graph plot Showing Diabetes Progression Against BMI')\n",
+    "\n",
+    "#  Draw the plot\n",
     "plt.show()"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -650,7 +738,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.1"
+   "version": "3.11.4"
   },
   "metadata": {
    "interpreter": {