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car_insurance.ipynb

@@ -9,7 +9,7 @@
   },
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-   "execution_count": 39,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21,7 +21,7 @@
   },
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@@ -66,7 +66,7 @@
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+   "execution_count": 67,
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     {
@@ -256,7 +256,7 @@
        "4     0               1      1.0  "
       ]
      },
-     "execution_count": 41,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
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@@ -282,7 +282,7 @@
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+   "execution_count": 68,
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@@ -310,7 +310,7 @@
        "dtype: int64"
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-     "execution_count": 42,
+     "execution_count": 68,
      "metadata": {},
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@@ -322,7 +322,7 @@
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+   "execution_count": 69,
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@@ -350,7 +350,7 @@
        "dtype: int64"
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+     "execution_count": 69,
      "metadata": {},
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@@ -362,7 +362,7 @@
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@@ -371,7 +371,7 @@
        "0"
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+     "execution_count": 70,
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@@ -390,7 +390,7 @@
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@@ -580,7 +580,7 @@
        "4     0               1      1.0  "
       ]
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-     "execution_count": 45,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
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@@ -591,7 +591,7 @@
   },
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@@ -601,7 +601,7 @@
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@@ -629,7 +629,7 @@
        "dtype: int64"
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-     "execution_count": 47,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
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@@ -640,7 +640,7 @@
   },
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    "cell_type": "code",
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+   "execution_count": 74,
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     {
@@ -842,7 +842,7 @@
        "4               1      1.0  "
       ]
      },
-     "execution_count": 48,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -872,7 +872,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 75,
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     {
@@ -1110,7 +1110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 76,
    "metadata": {},
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     {
@@ -1360,7 +1360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 77,
    "metadata": {},
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@@ -1374,7 +1374,7 @@
   },
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-   "execution_count": 52,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
@@ -1383,11 +1383,12 @@
      "text": [
       "Decision tree regression\n",
       "The accuricy for the train set is :1.0\n",
-      "The accuricy for the test set is :0.791002044989775\n"
+      "The accuricy for the test set is :0.7950920245398773\n"
      ]
     }
    ],
    "source": [
+    "#Building a decision tree classifier model to make predictions\n",
     "from sklearn.tree import DecisionTreeClassifier\n",
     "modelDT = DecisionTreeClassifier()\n",
     "modelDT = modelDT.fit(X_train, y_train)\n",
@@ -1399,7 +1400,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
@@ -1412,6 +1413,7 @@
     }
    ],
    "source": [
+    "#Building a KNeighbours classifier model to make predictions\n",
     "from sklearn.neighbors import KNeighborsClassifier\n",
     "modelKNC = KNeighborsClassifier()\n",
     "modelKNC = modelKNC.fit(X_train,y_train)\n",
@@ -1429,7 +1431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1444,13 +1446,12 @@
     "ParamsKNC = {\"n_neighbors\":[5,7, 8, 10],\n",
     "             \"metric\": [\"euclidean\", \"manhattan\", \"chebyshev\", \"minkowski\"],\n",
     "             \"algorithm\" : [\"auto\",\"ball_tree\",\"kd_tree\",\"brute\"],\n",
-    "             \"weights\": [\"uniform\",\"distance\"],\n",
-    "             \"p\" :[1,2]}"
+    "             \"weights\": [\"uniform\",\"distance\"]}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1465,14 +1466,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'criterion': 'gini', 'max_depth': 7, 'max_features': None, 'min_samples_leaf': 1, 'min_weight_fraction_leaf': 0.1, 'splitter': 'random'}\n"
+      "{'criterion': 'gini', 'max_depth': 11, 'max_features': None, 'min_samples_leaf': 9, 'min_weight_fraction_leaf': 0.1, 'splitter': 'random'}\n"
      ]
     }
    ],
@@ -1483,7 +1484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1497,14 +1498,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'algorithm': 'auto', 'metric': 'manhattan', 'n_neighbors': 10, 'p': 1, 'weights': 'uniform'}\n"
+      "{'algorithm': 'auto', 'metric': 'manhattan', 'n_neighbors': 10, 'weights': 'uniform'}\n"
      ]
     }
    ],
@@ -1522,7 +1523,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
@@ -1547,7 +1548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
@@ -1561,7 +1562,7 @@
    ],
    "source": [
     "#KNeighbours Classifier using the best parameters found\n",
-    "modelKNC = KNeighborsClassifier(algorithm=\"auto\",metric=\"manhattan\",n_neighbors=10,p=1,weights=\"uniform\")\n",
+    "modelKNC = KNeighborsClassifier(algorithm=\"auto\",metric=\"manhattan\",n_neighbors=10,weights=\"uniform\")\n",
     "modelKNC = modelKNC.fit(X_train,y_train)\n",
     "modelKNC.predict(X_test)\n",
     "print (\"The accuricy for the train set is :\" + str(modelKNC.score(X_train,y_train)))\n",