diff --git a/notebooks/examples/linear_regression.ipynb b/notebooks/examples/linear_regression.ipynb
index 99ac9b0778f085689904d4a0bd7e23cb6589bb87..8adf9d7baf57b067c02130f1520e0ba56a32a7b4 100644
--- a/notebooks/examples/linear_regression.ipynb
+++ b/notebooks/examples/linear_regression.ipynb
@@ -764,8 +764,8 @@
    ],
    "source": [
     "linear = linear_model.LinearRegression()\n",
-    "linear.fit(train[features],train[predict])\n",
-    "predictions = linear.predict(test[features])\n",
+    "linear.fit(X_train,y_train)\n",
+    "predictions = linear.predict(X_test)\n",
     "print(\"linear coeffecients for: age, smoker, bmi, children, region\")\n",
     "print(linear.coef_)\n",
     "print(\"intercept: \", linear.intercept_)"
@@ -777,7 +777,7 @@
     "collapsed": false
    },
    "source": [
-    "With the coefficients and intercept we can demonstrate how the model predicts the target variable internally using the formula which you can find in the mdbook linear regression chapter."
+    "With the coefficients and intercept we can demonstrate how the model predicts the target variable internally using the formula which you can find in the mdbook linear regression chapter. To proof that this is the same calculation we will also let the model predict the same data."
    ]
   },
   {
@@ -791,30 +791,21 @@
    },
    "outputs": [
     {
-     "ename": "KeyError",
-     "evalue": "100",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3360\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3361\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3362\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;31mKeyError\u001b[0m: 100",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_14236/3776536213.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpredictValue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlinear\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mintercept_\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mlinear\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mlinear\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mlinear\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mlinear\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mlinear\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"predicted value : \"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpredictValue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"actual value : \"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3456\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3457\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3458\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3459\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3460\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3361\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3362\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3363\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3364\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3365\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mKeyError\u001b[0m: 100"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hand predicted value :  7591.985875541097\n",
+      "model predicted value :  7591.985875541097\n",
+      "actual value :  6186.127\n"
      ]
     }
    ],
    "source": [
-    "predictValue = linear.intercept_ + (X_train[100][0] * linear.coef_[0]) + (X_train[100][1] * linear.coef_[1]) + (X_train[100][2] * linear.coef_[2]) + (X_train[100][3] * linear.coef_[3]) + (X_train[100][4] * linear.coef_[4])\n",
-    "print(\"predicted value : \", predictValue)\n",
+    "hand_predicted_value = linear.intercept_ + (X_train.iat[100,0] * linear.coef_[0]) + (X_train.iat[100,1] * linear.coef_[1]) + (X_train.iat[100,2] * linear.coef_[2]) + (X_train.iat[100,3] * linear.coef_[3]) + (X_train.iat[100,4] * linear.coef_[4])\n",
+    "tmp = pd.DataFrame(X_train.iloc[100]).transpose()\n",
+    "model_predicted_value = linear.predict(tmp)\n",
+    "print(\"hand predicted value : \", hand_predicted_value)\n",
+    "print(\"model predicted value : \", model_predicted_value[0])\n",
     "print(\"actual value : \", y_train[100])"
    ]
   },
@@ -829,22 +820,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.766329065180451\n",
+      "Root mean squared error :  5677.688231925713\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "acc = linear.score(X_test,y_test)\n",
     "print(acc)\n",
     "rmse = np.sqrt(mean_squared_error(y_test,predictions))\n",
     "print(\"Root mean squared error : \",  rmse)\n",
-    "plt.plot(y_test[0:20])\n",
-    "plt.plot(predictions[0:20])\n",
-    "plt.legend([\"this is test\", \"this is prediction\"])\n",
+    "x_axis = range(0,20)\n",
+    "plt.plot(x_axis, y_test[0:20])\n",
+    "plt.plot(x_axis, predictions[0:20])\n",
+    "plt.legend([\"true values\", \"predicted values\"])\n",
     "plt.show()"
    ]
   },
@@ -854,21 +867,45 @@
     "collapsed": false
    },
    "source": [
-    "Now we are trying to see if our model is in a linear relation or a polynomial relation. To be able to answer this question we will plot different polynomial degrees and their respective rmse to see which polynomial degree is best for predicting our data."
+    "Now we will try to see if our model is in a linear relation or a polynomial relation. To be able to answer this question we will plot different polynomial degrees and their respective rmse to see which polynomial degree is best for predicting our data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[5677.688231925711, 4364.019399068003, 4224.0557359453915, 5199.253242637651, 5585.951466471735]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from sklearn.preprocessing import PolynomialFeatures\n",
+    "plt.title(\"RMSE vs. Polynomial Degree\")\n",
+    "plt.ylabel(\"RMSE\")\n",
+    "plt.xlabel(\"degree\")\n",
+    "plt.grid()\n",
     "rmselist = []\n",
     "for x in range(5):\n",
     "    polynomial_features= PolynomialFeatures(degree=x+1)\n",
@@ -897,7 +934,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -917,48 +954,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "rmse:  4224.0557359453915\n",
+      "r2 score:  0.8706635627502789\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "rmse = np.sqrt(mean_squared_error(y_test,y_poly_pred))\n",
-    "print(\"rmse : \",rmse)\n",
-    "print(\"accuracy : \", r2_score(y_test,y_poly_pred))\n",
-    "plt.plot(y_test[0:20])\n",
-    "plt.plot(y_poly_pred[0:20])\n",
+    "print(\"rmse: \",rmse)\n",
+    "print(\"r2 score: \", r2_score(y_test,y_poly_pred))\n",
+    "plt.plot(x_axis,y_test[0:20])\n",
+    "plt.plot(x_axis,y_poly_pred[0:20])\n",
     "plt.legend([\"this is test\", \"this is prediction\"])\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "As a last step we are doing the same prediction as earlier but with our new polynomial degree. There is a nice improvement with our polynomial prediction."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "predictValue = model.intercept_ + (x_poly_train[100][0] * model.coef_[0]) + (x_poly_train[100][1] * model.coef_[1]) + (x_poly_train[100][2] * model.coef_[2]) + (x_poly_train[100][3] * model.coef_[3]) + (x_poly_train[100][4] * model.coef_[4])\n",
-    "print(\"predicted value : \", predictValue)\n",
-    "print(\"actual value : \", y_train[100])"
-   ]
   }
  ],
  "metadata": {