knn

Author: codebasics

ML/17_knn_classification/Exercise/knn_exercise_digits.ipynb renamed to ML/17_knn_classification/Exercise/knn_exercise_digits_solution.ipynb

@@ -964,7 +964,7 @@
    "cell_type": "code",
    "execution_count": 23,
    "metadata": {
-    "scrolled": true
+    "scrolled": false
    },
    "outputs": [
     {
@@ -983,23 +983,10 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 70,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.97407407407407409"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "linear_model.score(X_test,y_test)"
+    "**I tried various values of K manually and K=5 seem to be giving me the best score**"
    ]
   },
   {

ML/17_knn_classification/knn_classification_tutorial.ipynb

@@ -1131,7 +1131,7 @@
    "source": [
     "**Do not forget to work on exercise, exercise link is below** \n",
     "\n",
-    "\n"
+    "https://github.com/codebasics/py/blob/master/ML/17_knn_classification/knn_exercise.md\n"
    ]
   }
  ],

ML/17_knn_classification/knn_exercise.md

@@ -3,4 +3,4 @@ from sklearn.datasets load digits dataset and do following
  1. Plot confusion matrix
  1. Plot classification report
 
-[Solution link]()
+[Solution link](https://github.com/codebasics/py/blob/master/ML/17_knn_classification/Exercise/knn_exercise_digits_solution.ipynb)