diff --git a/Parkinsons-Disease-Detector-main/PARKINSON'S_DETECTION (1).ipynb b/Parkinsons-Disease-Detector-main/PARKINSON'S_DETECTION (1).ipynb
index 734842834..4ae71919d 100644
--- a/Parkinsons-Disease-Detector-main/PARKINSON'S_DETECTION (1).ipynb	
+++ b/Parkinsons-Disease-Detector-main/PARKINSON'S_DETECTION (1).ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 266,
    "metadata": {
     "id": "LdMnJArksktz"
    },
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 267,
    "metadata": {
     "executionInfo": {
      "elapsed": 1433,
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 268,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -260,7 +260,7 @@
        "[5 rows x 24 columns]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 268,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -271,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 269,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -577,7 +577,7 @@
        "[8 rows x 23 columns]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 269,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -588,7 +588,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 270,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -651,7 +651,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 271,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -681,7 +681,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 271,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -692,7 +692,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 272,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -741,7 +741,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 272,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -752,7 +752,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 273,
    "metadata": {
     "executionInfo": {
      "elapsed": 530,
@@ -773,7 +773,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 274,
    "metadata": {
     "executionInfo": {
      "elapsed": 777,
@@ -794,7 +794,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 275,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -820,13 +820,13 @@
        "<Axes: xlabel='status', ylabel='count'>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 275,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjsAAAGwCAYAAABPSaTdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAnVElEQVR4nO3df1TVdZ7H8dcV9HpFuArivd4RFSecTBlttLVoSkzFpbIpKyvd0h3z0FA5hIpxHAtrgpE9KTO6mbaNuDmOzrajtbWW2BRmbqUoldpabqzSBIszg1xRvIB89w9P93hDzfDCvXx8Ps655/j9yfvrOcSz7/1etFmWZQkAAMBQXUI9AAAAQHsidgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgtMhQDxAOWlpa9NVXXyk6Olo2my3U4wAAgItgWZaOHz8uj8ejLl3Of/+G2JH01VdfKSEhIdRjAACANqisrFT//v3Pu53YkRQdHS3pzF9WTExMiKcBAAAXw+v1KiEhwf9z/HyIHcn/1lVMTAyxAwBAJ/Ntj6DwgDIAADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKNFhnoAADBBTWFGqEcAwk7fnFWhHkESd3YAAIDhiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGC2nsbN++XZMnT5bH45HNZtPmzZvPu29GRoZsNpuKiooC1vt8Pj366KPq06ePoqKidNttt+nLL79s38EBAECnEdLYOXHihEaMGKEVK1ZccL/Nmzfrgw8+kMfjabUtKytLmzZt0oYNG7Rjxw7V19fr1ltv1enTp9trbAAA0IlEhvKLp6enKz09/YL7/PnPf9YjjzyiN998U7fcckvAtrq6Or344ot66aWXNGHCBEnSunXrlJCQoG3btmnSpEnnPKfP55PP5/Mve73eS7wSAAAQrsL6mZ2Wlhbdf//9mj9/voYNG9Zqe1lZmZqampSWluZf5/F4NHz4cO3cufO85y0oKJDT6fS/EhIS2mV+AAAQemEdO0uWLFFkZKTmzJlzzu3V1dXq1q2bevfuHbDe5XKpurr6vOfNzc1VXV2d/1VZWRnUuQEAQPgI6dtYF1JWVqZf//rX2rNnj2w223c61rKsCx5jt9tlt9svdUQAANAJhO2dnXfffVc1NTUaMGCAIiMjFRkZqcOHD2vu3LkaNGiQJMntdquxsVG1tbUBx9bU1MjlcoVgagAAEG7CNnbuv/9+ffzxxyovL/e/PB6P5s+frzfffFOSNGrUKHXt2lUlJSX+46qqqrRv3z6lpKSEanQAABBGQvo2Vn19vQ4dOuRfrqioUHl5uWJjYzVgwADFxcUF7N+1a1e53W794Ac/kCQ5nU7NmjVLc+fOVVxcnGJjYzVv3jwlJyf7P50FAAAubyGNnd27d2vcuHH+5ezsbEnSjBkzVFxcfFHnWLZsmSIjIzV16lQ1NDRo/PjxKi4uVkRERHuMDAAAOhmbZVlWqIcINa/XK6fTqbq6OsXExIR6HACdUE1hRqhHAMJO35xV7Xr+i/35HbbP7AAAAAQDsQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADBaSGNn+/btmjx5sjwej2w2mzZv3uzf1tTUpAULFig5OVlRUVHyeDx64IEH9NVXXwWcw+fz6dFHH1WfPn0UFRWl2267TV9++WUHXwkAAAhXIY2dEydOaMSIEVqxYkWrbSdPntSePXu0aNEi7dmzR3/84x/12Wef6bbbbgvYLysrS5s2bdKGDRu0Y8cO1dfX69Zbb9Xp06c76jIAAEAYiwzlF09PT1d6evo5tzmdTpWUlASsW758uf7u7/5OR44c0YABA1RXV6cXX3xRL730kiZMmCBJWrdunRISErRt2zZNmjTpnOf2+Xzy+Xz+Za/XG6QrAgAA4aZTPbNTV1cnm82mXr16SZLKysrU1NSktLQ0/z4ej0fDhw/Xzp07z3uegoICOZ1O/yshIaG9RwcAACHSaWLn1KlTevzxxzVt2jTFxMRIkqqrq9WtWzf17t07YF+Xy6Xq6urznis3N1d1dXX+V2VlZbvODgAAQiekb2NdrKamJt17771qaWnRc8899637W5Ylm8123u12u112uz2YIwIAgDAV9nd2mpqaNHXqVFVUVKikpMR/V0eS3G63GhsbVVtbG3BMTU2NXC5XR48KAADCUFjHzteh8/nnn2vbtm2Ki4sL2D5q1Ch17do14EHmqqoq7du3TykpKR09LgAACEMhfRurvr5ehw4d8i9XVFSovLxcsbGx8ng8uuuuu7Rnzx699tprOn36tP85nNjYWHXr1k1Op1OzZs3S3LlzFRcXp9jYWM2bN0/Jycn+T2cBAIDLW0hjZ/fu3Ro3bpx/OTs7W5I0Y8YM5eXl6dVXX5UkjRw5MuC4t99+W6mpqZKkZcuWKTIyUlOnTlVDQ4PGjx+v4uJiRUREdMg1AACA8GazLMsK9RCh5vV65XQ6VVdXF/BMEABcrJrCjFCPAISdvjmr2vX8F/vzO6yf2QEAALhUxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoIY2d7du3a/LkyfJ4PLLZbNq8eXPAdsuylJeXJ4/HI4fDodTUVO3fvz9gH5/Pp0cffVR9+vRRVFSUbrvtNn355ZcdeBUAACCchTR2Tpw4oREjRmjFihXn3F5YWKilS5dqxYoV2rVrl9xutyZOnKjjx4/798nKytKmTZu0YcMG7dixQ/X19br11lt1+vTpjroMAAAQxiJD+cXT09OVnp5+zm2WZamoqEgLFy7UlClTJElr166Vy+XS+vXrlZGRobq6Or344ot66aWXNGHCBEnSunXrlJCQoG3btmnSpEnnPLfP55PP5/Mve73eIF8ZAAAIF2H7zE5FRYWqq6uVlpbmX2e32zV27Fjt3LlTklRWVqampqaAfTwej4YPH+7f51wKCgrkdDr9r4SEhPa7EAAAEFJhGzvV1dWSJJfLFbDe5XL5t1VXV6tbt27q3bv3efc5l9zcXNXV1flflZWVQZ4eAACEi5C+jXUxbDZbwLJlWa3WfdO37WO322W324MyHwAACG9he2fH7XZLUqs7NDU1Nf67PW63W42NjaqtrT3vPgAA4PIWtrGTmJgot9utkpIS/7rGxkaVlpYqJSVFkjRq1Ch17do1YJ+qqirt27fPvw8AALi8hfRtrPr6eh06dMi/XFFRofLycsXGxmrAgAHKyspSfn6+kpKSlJSUpPz8fPXo0UPTpk2TJDmdTs2aNUtz585VXFycYmNjNW/ePCUnJ/s/nQUAAC5vIY2d3bt3a9y4cf7l7OxsSdKMGTNUXFysnJwcNTQ0KDMzU7W1tRozZoy2bt2q6Oho/zHLli1TZGSkpk6dqoaGBo0fP17FxcWKiIjo8OsBAADhx2ZZlhXqIULN6/XK6XSqrq5OMTExoR4HQCdUU5gR6hGAsNM3Z1W7nv9if36H7TM7AAAAwUDsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjtSl2brrpJh07dqzVeq/Xq5tuuulSZwIAAAiaNsXOO++8o8bGxlbrT506pXffffeShwIAAAiWyO+y88cff+z/84EDB1RdXe1fPn36tN544w1973vfC950AAAAl+g7xc7IkSNls9lks9nO+XaVw+HQ8uXLgzYcAADApfpOsVNRUSHLsjR48GB9+OGHio+P92/r1q2b+vbtq4iIiKAPCQAA0FbfKXYGDhwoSWppaWmXYQAAAILtO8XO2T777DO98847qqmpaRU/TzzxxCUPBgAAEAxt+jTWCy+8oKuuukpPPPGEXn75ZW3atMn/2rx5c9CGa25u1i9+8QslJibK4XBo8ODBeuqppwLiyrIs5eXlyePxyOFwKDU1Vfv37w/aDAAAoHNr052dX/7yl3rmmWe0YMGCYM8TYMmSJXr++ee1du1aDRs2TLt379Y//uM/yul06uc//7kkqbCwUEuXLlVxcbGGDBmiX/7yl5o4caIOHjyo6Ojodp0PAACEvzbFTm1tre6+++5gz9LKf/3Xf+knP/mJbrnlFknSoEGD9Pvf/167d++WdOauTlFRkRYuXKgpU6ZIktauXSuXy6X169crIyPjnOf1+Xzy+Xz+Za/X285XAgAAQqVNb2Pdfffd2rp1a7BnaeXHP/6x3nrrLX322WeSpI8++kg7duzQzTffLOnMp8Oqq6uVlpbmP8Zut2vs2LHauXPnec9bUFAgp9PpfyUkJLTvhQAAgJBp052dK664QosWLdL777+v5ORkde3aNWD7nDlzgjLcggULVFdXpyuvvFIRERE6ffq0nnnmGd13332S5P+lhi6XK+A4l8ulw4cPn/e8ubm5ys7O9i97vV6CBwAAQ7UpdlavXq2ePXuqtLRUpaWlAdtsNlvQYmfjxo1at26d1q9fr2HDhqm8vFxZWVnyeDyaMWNGwNc8m2VZrdadzW63y263B2VGAAAQ3toUOxUVFcGe45zmz5+vxx9/XPfee68kKTk5WYcPH1ZBQYFmzJght9st6cwdnn79+vmPq6mpaXW3BwAAXJ7a9MxORzl58qS6dAkcMSIiwv/R88TERLndbpWUlPi3NzY2qrS0VCkpKR06KwAACE9turPz05/+9ILbf/vb37ZpmG+aPHmynnnmGQ0YMEDDhg3T3r17tXTpUv/Xt9lsysrKUn5+vpKSkpSUlKT8/Hz16NFD06ZNC8oMAACgc2vzR8/P1tTUpH379unYsWPn/AdC22r58uVatGiRMjMzVVNTI4/Ho4yMjIDf0JyTk6OGhgZlZmaqtrZWY8aM0datW/kdOwAAQJJksyzLCsaJWlpalJmZqcGDBysnJycYp+wwXq9XTqdTdXV1iomJCfU4ADqhmsJz/14v4HLWN2dVu57/Yn9+B+2ZnS5duuixxx7TsmXLgnVKAACASxbUB5T/53/+R83NzcE8JQAAwCVp0zM7Z/9CPunM77WpqqrS66+/HvD7bwAAAEKtTbGzd+/egOUuXbooPj5ezz777Ld+UgsAAKAjtSl23n777WDPAQAA0C7aFDtfO3r0qA4ePCibzaYhQ4YoPj4+WHMBAAAERZseUD5x4oR++tOfql+/frrxxht1ww03yOPxaNasWTp58mSwZwQAAGizNsVOdna2SktL9R//8R86duyYjh07pldeeUWlpaWaO3dusGcEAABosza9jfXv//7vevnll5Wamupfd/PNN8vhcGjq1KlauXJlsOYDAAC4JG26s3Py5Mlz/qviffv25W0sAAAQVtoUO9ddd52efPJJnTp1yr+uoaFBixcv1nXXXRe04QAAAC5Vm97GKioqUnp6uvr3768RI0bIZrOpvLxcdrtdW7duDfaMAAAAbdam2ElOTtbnn3+udevW6b//+79lWZbuvfdeTZ8+XQ6HI9gzAgAAtFmbYqegoEAul0uzZ88OWP/b3/5WR48e1YIFC4IyHAAAwKVq0zM7q1at0pVXXtlq/bBhw/T8889f8lAAAADB0qbYqa6uVr9+/Vqtj4+PV1VV1SUPBQAAECxtip2EhAS99957rda/99578ng8lzwUAABAsLTpmZ0HH3xQWVlZampq0k033SRJeuutt5STk8NvUAYAAGGlTbGTk5Ojv/3tb8rMzFRjY6MkqXv37lqwYIFyc3ODOiAAAMClaFPs2Gw2LVmyRIsWLdKnn34qh8OhpKQk2e32YM8HAABwSdoUO1/r2bOnrrnmmmDNAgAAEHRtekAZAACgsyB2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgtLCPnT//+c/6h3/4B8XFxalHjx4aOXKkysrK/Nsty1JeXp48Ho8cDodSU1O1f//+EE4MAADCSVjHTm1tra6//np17dpVW7Zs0YEDB/Tss8+qV69e/n0KCwu1dOlSrVixQrt27ZLb7dbEiRN1/Pjx0A0OAADCRmSoB7iQJUuWKCEhQWvWrPGvGzRokP/PlmWpqKhICxcu1JQpUyRJa9eulcvl0vr165WRkdHRIwMAgDAT1nd2Xn31VY0ePVp33323+vbtq6uvvlovvPCCf3tFRYWqq6uVlpbmX2e32zV27Fjt3LnzvOf1+Xzyer0BLwAAYKawjp0vvvhCK1euVFJSkt5880099NBDmjNnjv71X/9VklRdXS1JcrlcAce5XC7/tnMpKCiQ0+n0vxISEtrvIgAAQEiFdey0tLToRz/6kfLz83X11VcrIyNDs2fP1sqVKwP2s9lsAcuWZbVad7bc3FzV1dX5X5WVle0yPwAACL2wjp1+/frpqquuClg3dOhQHTlyRJLkdrslqdVdnJqamlZ3e85mt9sVExMT8AIAAGYK69i5/vrrdfDgwYB1n332mQYOHChJSkxMlNvtVklJiX97Y2OjSktLlZKS0qGzAgCA8BTWn8Z67LHHlJKSovz8fE2dOlUffvihVq9erdWrV0s68/ZVVlaW8vPzlZSUpKSkJOXn56tHjx6aNm1aiKcHAADhIKxj55prrtGmTZuUm5urp556SomJiSoqKtL06dP9++Tk5KihoUGZmZmqra3VmDFjtHXrVkVHR4dwcgAAEC5slmVZoR4i1Lxer5xOp+rq6nh+B0Cb1BTye72Ab+qbs6pdz3+xP7/D+pkdAACAS0XsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKNFhnqAy0lNYUaoRwDCTt+cVaEeAYDhuLMDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjdarYKSgokM1mU1ZWln+dZVnKy8uTx+ORw+FQamqq9u/fH7ohAQBAWOk0sbNr1y6tXr1aP/zhDwPWFxYWaunSpVqxYoV27dolt9utiRMn6vjx4yGaFAAAhJNOETv19fWaPn26XnjhBfXu3du/3rIsFRUVaeHChZoyZYqGDx+utWvX6uTJk1q/fn0IJwYAAOGiU8TOww8/rFtuuUUTJkwIWF9RUaHq6mqlpaX519ntdo0dO1Y7d+487/l8Pp+8Xm/ACwAAmCky1AN8mw0bNmjPnj3atWtXq23V1dWSJJfLFbDe5XLp8OHD5z1nQUGBFi9eHNxBAQBAWArrOzuVlZX6+c9/rnXr1ql79+7n3c9mswUsW5bVat3ZcnNzVVdX539VVlYGbWYAABBewvrOTllZmWpqajRq1Cj/utOnT2v79u1asWKFDh48KOnMHZ5+/fr596mpqWl1t+dsdrtddru9/QYHAABhI6zv7IwfP16ffPKJysvL/a/Ro0dr+vTpKi8v1+DBg+V2u1VSUuI/prGxUaWlpUpJSQnh5AAAIFyE9Z2d6OhoDR8+PGBdVFSU4uLi/OuzsrKUn5+vpKQkJSUlKT8/Xz169NC0adNCMTIAAAgzYR07FyMnJ0cNDQ3KzMxUbW2txowZo61btyo6OjrUowEAgDDQ6WLnnXfeCVi22WzKy8tTXl5eSOYBAADhLayf2QEAALhUxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoYR07BQUFuuaaaxQdHa2+ffvq9ttv18GDBwP2sSxLeXl58ng8cjgcSk1N1f79+0M0MQAACDdhHTulpaV6+OGH9f7776ukpETNzc1KS0vTiRMn/PsUFhZq6dKlWrFihXbt2iW3262JEyfq+PHjIZwcAACEi8hQD3Ahb7zxRsDymjVr1LdvX5WVlenGG2+UZVkqKirSwoULNWXKFEnS2rVr5XK5tH79emVkZIRibAAAEEbC+s7ON9XV1UmSYmNjJUkVFRWqrq5WWlqafx+73a6xY8dq586d5z2Pz+eT1+sNeAEAADN1mtixLEvZ2dn68Y9/rOHDh0uSqqurJUkulytgX5fL5d92LgUFBXI6nf5XQkJC+w0OAABCqtPEziOPPKKPP/5Yv//971tts9lsAcuWZbVad7bc3FzV1dX5X5WVlUGfFwAAhIewfmbna48++qheffVVbd++Xf379/evd7vdks7c4enXr59/fU1NTau7PWez2+2y2+3tNzAAAAgbYX1nx7IsPfLII/rjH/+oP/3pT0pMTAzYnpiYKLfbrZKSEv+6xsZGlZaWKiUlpaPHBQAAYSis7+w8/PDDWr9+vV555RVFR0f7n8NxOp1yOByy2WzKyspSfn6+kpKSlJSUpPz8fPXo0UPTpk0L8fQAACAchHXsrFy5UpKUmpoasH7NmjWaOXOmJCknJ0cNDQ3KzMxUbW2txowZo61btyo6OrqDpwUAAOEorGPHsqxv3cdmsykvL095eXntPxAAAOh0wvqZHQAAgEtF7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxmTOw899xzSkxMVPfu3TVq1Ci9++67oR4JAACEASNiZ+PGjcrKytLChQu1d+9e3XDDDUpPT9eRI0dCPRoAAAgxI2Jn6dKlmjVrlh588EENHTpURUVFSkhI0MqVK0M9GgAACLHIUA9wqRobG1VWVqbHH388YH1aWpp27tx5zmN8Pp98Pp9/ua6uTpLk9Xrbb1BJx081tuv5gc6oezt/33UUvr+B1tr7+/vrn9uWZV1wv04fO3/5y190+vRpuVyugPUul0vV1dXnPKagoECLFy9utT4hIaFdZgRwAU8Wh3oCAO2lg76/jx8/LqfTed7tnT52vmaz2QKWLctqte5rubm5ys7O9i+3tLTob3/7m+Li4s57DMzh9XqVkJCgyspKxcTEhHocAEHE9/flxbIsHT9+XB6P54L7dfrY6dOnjyIiIlrdxampqWl1t+drdrtddrs9YF2vXr3aa0SEqZiYGP5jCBiK7+/Lx4Xu6Hyt0z+g3K1bN40aNUolJSUB60tKSpSSkhKiqQAAQLjo9Hd2JCk7O1v333+/Ro8ereuuu06rV6/WkSNH9NBDD4V6NAAAEGJGxM4999yjv/71r3rqqadUVVWl4cOH6z//8z81cODAUI+GMGS32/Xkk0+2eisTQOfH9zfOxWZ92+e1AAAAOrFO/8wOAADAhRA7AADAaMQOAAAwGrGDy0pxcTG/UwkALjPEDjqlmTNnymaztXodOnQo1KMBCIJzfX+f/Zo5c2aoR0QnYsRHz3F5+vu//3utWbMmYF18fHyIpgEQTFVVVf4/b9y4UU888YQOHjzoX+dwOAL2b2pqUteuXTtsPnQu3NlBp2W32+V2uwNev/71r5WcnKyoqCglJCQoMzNT9fX15z3HRx99pHHjxik6OloxMTEaNWqUdu/e7d++c+dO3XjjjXI4HEpISNCcOXN04sSJjrg84LJ29ve10+mUzWbzL586dUq9evXSH/7wB6Wmpqp79+5at26d8vLyNHLkyIDzFBUVadCgQQHr1qxZo6FDh6p79+668sor9dxzz3XchSEkiB0YpUuXLvrNb36jffv2ae3atfrTn/6knJyc8+4/ffp09e/fX7t27VJZWZkef/xx//8dfvLJJ5o0aZKmTJmijz/+WBs3btSOHTv0yCOPdNTlALiABQsWaM6cOfr00081adKkizrmhRde0MKFC/XMM8/o008/VX5+vhYtWqS1a9e287QIJd7GQqf12muvqWfPnv7l9PR0/du//Zt/OTExUU8//bR+9rOfnff/3I4cOaL58+fryiuvlCQlJSX5t/3TP/2Tpk2bpqysLP+23/zmNxo7dqxWrlyp7t27t8NVAbhYWVlZmjJlync65umnn9azzz7rPy4xMVEHDhzQqlWrNGPGjPYYE2GA2EGnNW7cOK1cudK/HBUVpbffflv5+fk6cOCAvF6vmpubderUKZ04cUJRUVGtzpGdna0HH3xQL730kiZMmKC7775b3//+9yVJZWVlOnTokH73u9/597csSy0tLaqoqNDQoUPb/yIBnNfo0aO/0/5Hjx5VZWWlZs2apdmzZ/vXNzc3X9S/nI3Oi9hBpxUVFaUrrrjCv3z48GHdfPPNeuihh/T0008rNjZWO3bs0KxZs9TU1HTOc+Tl5WnatGl6/fXXtWXLFj355JPasGGD7rjjDrW0tCgjI0Nz5sxpddyAAQPa7boAXJxv/g9Mly5d9M1/Aens7/2WlhZJZ97KGjNmTMB+ERER7TQlwgGxA2Ps3r1bzc3NevbZZ9Wly5nH0f7whz9863FDhgzRkCFD9Nhjj+m+++7TmjVrdMcdd+hHP/qR9u/fHxBUAMJXfHy8qqurZVmWbDabJKm8vNy/3eVy6Xvf+56++OILTZ8+PURTIhSIHRjj+9//vpqbm7V8+XJNnjxZ7733np5//vnz7t/Q0KD58+frrrvuUmJior788kvt2rVLd955p6QzDz9ee+21evjhhzV79mxFRUXp008/VUlJiZYvX95RlwXgIqWmpuro0aMqLCzUXXfdpTfeeENbtmxRTEyMf5+8vDzNmTNHMTExSk9Pl8/n0+7du1VbW6vs7OwQTo/2xKexYIyRI0dq6dKlWrJkiYYPH67f/e53KigoOO/+ERER+utf/6oHHnhAQ4YM0dSpU5Wenq7FixdLkn74wx+qtLRUn3/+uW644QZdffXVWrRokfr169dRlwTgOxg6dKiee+45/fM//7NGjBihDz/8UPPmzQvY58EHH9S//Mu/qLi4WMnJyRo7dqyKi4uVmJgYoqnREWzWN9/gBAAAMAh3dgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AncbMmTN1++23f+fj8vLyNHLkyKDPA6BzIHYAAIDRiB0AYefll19WcnKyHA6H4uLiNGHCBM2fP19r167VK6+8IpvNJpvNpnfeeUfSmX+hfsiQIerRo4cGDx6sRYsWqampSZJUXFysxYsX66OPPvIfV1xcrP/93/+VzWZTeXm5/+seO3Ys4Ly1tbWaPn264uPj5XA4lJSUpDVr1nTw3waASxUZ6gEA4GxVVVW67777VFhYqDvuuEPHjx/Xu+++qwceeEBHjhyR1+v1B0dsbKwkKTo6WsXFxfJ4PPrkk080e/ZsRUdHKycnR/fcc4/27dunN954Q9u2bZMkOZ1O/d///d+3zrJo0SIdOHBAW7ZsUZ8+fXTo0CE1NDS038UDaBfEDoCwUlVVpebmZk2ZMkUDBw6UJCUnJ0uSHA6HfD6f3G53wDG/+MUv/H8eNGiQ5s6dq40bNyonJ0cOh0M9e/ZUZGRkq+O+zZEjR3T11Vdr9OjR/nMD6HyIHQBhZcSIERo/frySk5M1adIkpaWl6a677lLv3r3Pe8zLL7+soqIiHTp0SPX19WpublZMTMwlz/Kzn/1Md955p/bs2aO0tDTdfvvtSklJueTzAuhYPLMDIKxERESopKREW7Zs0VVXXaXly5frBz/4gSoqKs65//vvv697771X6enpeu2117R3714tXLhQjY2NF/w6Xbqc+c+fZVn+dV8/5/O19PR0HT58WFlZWfrqq680fvx4zZs37xKvEEBHI3YAhB2bzabrr79eixcv1t69e9WtWzdt2rRJ3bp10+nTpwP2fe+99zRw4EAtXLhQo0ePVlJSkg4fPhywz7mOi4+Pl3TmbbOvnf2w8tn7zZw5U+vWrVNRUZFWr14dpKsE0FF4GwtAWPnggw/01ltvKS0tTX379tUHH3ygo0ePaujQoTp16pTefPNNHTx4UHFxcXI6nbriiit05MgRbdiwQddcc41ef/11bdq0KeCcgwYNUkVFhcrLy9W/f39FR0fL4XDo2muv1a9+9SsNGjRIf/nLXwKe/ZGkJ554QqNGjdKwYcPk8/n02muvaejQoR351wEgGCwACCMHDhywJk2aZMXHx1t2u90aMmSItXz5csuyLKumpsaaOHGi1bNnT0uS9fbbb1uWZVnz58+34uLirJ49e1r33HOPtWzZMsvpdPrPeerUKevOO++0evXqZUmy1qxZ4/9a1157reVwOKyRI0daW7duDTjv008/bQ0dOtRyOBxWbGys9ZOf/MT64osvOvBvA0Aw2CzrrDesAQAADMMzOwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIz2/+JAzoOa1sGFAAAAAElFTkSuQmCC",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -841,7 +841,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 276,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -863,7 +863,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1500x700 with 3 Axes>"
       ]
@@ -885,7 +885,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 277,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -905,31 +905,21 @@
     "outputId": "41612172-e3a7-45a7-9e32-72e0cbab9d69"
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\HP\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "C:\\Users\\HP\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x207922e1950>"
+       "<seaborn.axisgrid.FacetGrid at 0x1cac428cad0>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 277,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
+       "<Figure size 511.111x500 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -951,7 +941,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 278,
    "metadata": {
     "executionInfo": {
      "elapsed": 579,
@@ -972,7 +962,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 279,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -995,13 +985,417 @@
     {
      "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>StandardScaler()</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\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-18 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\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-18 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 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-18 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-18 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-18 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-18 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-18 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-18 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-18 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-18 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-18 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 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-18 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-18 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 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-18 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 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-18 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-18 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-18 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-18 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-18 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-18 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-18 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-18 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-18 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-18 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-18 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-18 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-18 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 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: 1ex;\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-18 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-18 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-18 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-18 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-18\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</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-18\" type=\"checkbox\" checked><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "StandardScaler()"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 279,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1015,7 +1409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 280,
    "metadata": {
     "executionInfo": {
      "elapsed": 722,
@@ -1036,7 +1430,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 281,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1258,7 +1652,7 @@
        "[5 rows x 22 columns]"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 281,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1270,7 +1664,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 282,
    "metadata": {
     "executionInfo": {
      "elapsed": 564,
@@ -1293,7 +1687,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 283,
    "metadata": {
     "executionInfo": {
      "elapsed": 748,
@@ -1314,7 +1708,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 284,
    "metadata": {
     "executionInfo": {
      "elapsed": 1044,
@@ -1340,7 +1734,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 285,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1363,16 +1757,16 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x20792f7e550>]"
+       "[<matplotlib.lines.Line2D at 0x1cac4803810>]"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 285,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -1389,7 +1783,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 286,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -1421,7 +1815,7 @@
        "        True,  True])"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 286,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1435,7 +1829,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 287,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -1487,7 +1881,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 288,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1513,13 +1907,13 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 288,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -1534,7 +1928,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 289,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -1557,14 +1951,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "model accuracy is : 95.0 %\n"
+      "Model accuracy is: 93.85 %\n"
      ]
     }
    ],
    "source": [
     "from sklearn.metrics import accuracy_score\n",
     "accuracy = accuracy_score(y_test,prediction)*100\n",
-    "print(f'model accuracy is : {accuracy.round()} %')"
+    "print(f'Model accuracy is: {round(accuracy, 2)} %')"
    ]
   },
   {
@@ -1578,7 +1972,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 290,
    "metadata": {
     "executionInfo": {
      "elapsed": 581,
@@ -1603,7 +1997,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 291,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1626,13 +2020,417 @@
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-19 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\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-19 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 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-19 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-19 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-19 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-19 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-19 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-19 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-19 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-19 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-19 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 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-19 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-19 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 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-19 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 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-19 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-19 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-19 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-19 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-19 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-19 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-19 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-19 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-19 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-19 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-19 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-19 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-19 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 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: 1ex;\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-19 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-19 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-19 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-19 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-19\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</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-19\" type=\"checkbox\" checked><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "LogisticRegression()"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 291,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1643,7 +2441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 292,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -1675,7 +2473,7 @@
        "        True, False])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 292,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1687,7 +2485,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 293,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -1728,7 +2526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 294,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -1762,7 +2560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 295,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1788,13 +2586,13 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 295,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -1809,7 +2607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 296,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -1832,14 +2630,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "model accuracy is : 85.0 %\n"
+      "Model accuracy is: 84.62 %\n"
      ]
     }
    ],
    "source": [
     "from sklearn.metrics import accuracy_score\n",
     "accuracy = accuracy_score(y_test,prediction_logi)*100\n",
-    "print(f'model accuracy is : {accuracy.round()} %')"
+    "print(f'Model accuracy is: {round(accuracy, 2)} %')"
    ]
   },
   {
@@ -1853,7 +2651,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 297,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1876,13 +2674,417 @@
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-20 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\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-20 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 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-20 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-20 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-20 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-20 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-20 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-20 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-20 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-20 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-20 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 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-20 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-20 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 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-20 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 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-20 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-20 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-20 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-20 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-20 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-20 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-20 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-20 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-20 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-20 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-20 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-20 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-20 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 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: 1ex;\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-20 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-20 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-20 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-20 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-20\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</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-20\" type=\"checkbox\" checked><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "DecisionTreeClassifier()"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 297,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1898,7 +3100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 298,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -1923,14 +3125,14 @@
        "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True, False,\n",
        "       False,  True,  True,  True,  True, False,  True,  True,  True,\n",
-       "        True, False,  True,  True,  True,  True,  True,  True, False,\n",
+       "        True, False,  True,  True,  True,  True, False,  True, False,\n",
        "        True,  True,  True,  True,  True,  True, False,  True,  True,\n",
        "        True,  True, False, False, False,  True,  True,  True,  True,\n",
-       "        True,  True, False,  True, False, False, False,  True,  True,\n",
+       "        True,  True, False,  True, False, False, False, False,  True,\n",
        "        True,  True])"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 298,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1942,7 +3144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 299,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -1967,17 +3169,17 @@
      "text": [
       "              precision    recall  f1-score   support\n",
       "\n",
-      "       False       0.85      0.65      0.73        17\n",
-      "        True       0.88      0.96      0.92        48\n",
+      "       False       0.87      0.76      0.81        17\n",
+      "        True       0.92      0.96      0.94        48\n",
       "\n",
-      "    accuracy                           0.88        65\n",
-      "   macro avg       0.87      0.80      0.83        65\n",
-      "weighted avg       0.87      0.88      0.87        65\n",
+      "    accuracy                           0.91        65\n",
+      "   macro avg       0.89      0.86      0.88        65\n",
+      "weighted avg       0.91      0.91      0.91        65\n",
       "\n",
       "\n",
       "\n",
       "Confission matrix\n",
-      "[[11  6]\n",
+      "[[13  4]\n",
       " [ 2 46]]\n"
      ]
     }
@@ -1991,7 +3193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 300,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -2017,13 +3219,13 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 300,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -2038,7 +3240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 301,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -2061,14 +3263,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "model accuracy is : 95.0 %\n"
+      "Model accuracy is: 90.77 %\n"
      ]
     }
    ],
    "source": [
     "from sklearn.metrics import accuracy_score\n",
     "accuracy = accuracy_score(y_test,prediction)*100\n",
-    "print(f'model accuracy is : {accuracy.round()} %')"
+    "print(f'Model accuracy is: {round(accuracy, 2)} %')"
    ]
   },
   {
@@ -2082,7 +3284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 302,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -2114,7 +3316,7 @@
        "        True,  True])"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 302,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2129,7 +3331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 303,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -2180,7 +3382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 304,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -2206,13 +3408,13 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 304,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAf8AAAGiCAYAAADp4c+XAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8WgzjOAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAcmElEQVR4nO3df3BU9f3v8ddCkuVXspgEssmXRBHUqGmwpjamagomCoHL8CO2VmAKlpGLEzKSjJXuDK1i21mqrSADRMdawR8pvXgNVhxhIEgy1qAhmAv2jqlBeoFrNhQ6JLDKISZ7//jOd29XAuTAbvfI5/mYOTPk7NnP580fzJOzm01coVAoJAAAYIxB8R4AAAD8exF/AAAMQ/wBADAM8QcAwDDEHwAAwxB/AAAMQ/wBADAM8QcAwDDEHwAAwxB/AAAMQ/wBAHCglStXyuVyaenSpeFzEydOlMvlijgWL15se+2EKM4JAACioLm5Wc8//7zy8/PPeeyhhx7Sk08+Gf562LBhttfnzh8AAAc5ffq05s6dqxdeeEFXXXXVOY8PGzZMXq83fKSkpNjeg/gDABBDlmWpu7s74rAs67zXV1RUaNq0aSotLe338ddee03p6enKy8uTz+fTF198YXsmx7zsv3Xf0XiPADjOlI7meI8AOFLCtFkxXf/UqVNRW+t3v/udVqxYEXHu8ccf1xNPPHHOtZs2bdK+ffvU3Nz/v/05c+bo6quvVlZWlvbv369ly5apra1Nb7zxhq2ZHBN/AACuRD6fT9XV1RHn3G73OdcdOXJEjzzyiHbs2KEhQ4b0u9aiRYvCf/7Wt76lzMxMlZSU6ODBgxo3btyAZyL+AADEkNvt7jf2X9fS0qJjx47p1ltvDZ/r7e1VY2Oj1q5dK8uyNHjw4IjnFBYWSpLa29uJPwAA3zQlJSU6cOBAxLkHH3xQubm5WrZs2Tnhl6TW1lZJUmZmpq29iD8AAA6QnJysvLy8iHPDhw9XWlqa8vLydPDgQdXW1mrq1KlKS0vT/v37VVVVpeLi4n4/EnghxB8AgG+ApKQk7dy5U6tXr1YwGFR2drbKy8u1fPly22sRfwAAHGr37t3hP2dnZ6uhoSEq6/I5fwAADEP8AQAwDPEHAMAwxB8AAMMQfwAADEP8AQAwDPEHAMAwxB8AAMMQfwAADEP8AQAwDPEHAMAwxB8AAMMQfwAADEP8AQAwDPEHAMAwxB8AAMMQfwAADEP8AQAwDPEHAMAwxB8AAMMQfwAADEP8AQAwDPEHAMAwxB8AAMMQfwAADEP8AQAwDPEHAMAwxB8AAMMQfwAADEP8AQAwDPEHAMAwxB8AAMMQfwAADEP8AQBwoJUrV8rlcmnp0qXhc2fOnFFFRYXS0tI0YsQIlZeXq7Oz0/baCVGcEwCAK8LwwNnoLZZs/ynNzc16/vnnlZ+fH3G+qqpKb7/9tjZv3iyPx6MlS5Zo9uzZ+stf/mJrfe78AQBwkNOnT2vu3Ll64YUXdNVVV4XPd3V16cUXX9Qzzzyju+++WwUFBXrppZf0/vvva8+ePbb2IP4AAMSQZVnq7u6OOCzLOu/1FRUVmjZtmkpLSyPOt7S0qKenJ+J8bm6ucnJy1NTUZGsm4g8AQAz5/X55PJ6Iw+/393vtpk2btG/fvn4fDwQCSkpK0siRIyPOZ2RkKBAI2JqJ9/wBAIghn8+n6urqiHNut/uc644cOaJHHnlEO3bs0JAhQ2I6E/EHACCG3G53v7H/upaWFh07dky33npr+Fxvb68aGxu1du1abd++XWfPntXJkycj7v47Ozvl9XptzUT8AQBwgJKSEh04cCDi3IMPPqjc3FwtW7ZM2dnZSkxMVH19vcrLyyVJbW1tOnz4sIqKimztRfwBAHCA5ORk5eXlRZwbPny40tLSwucXLlyo6upqpaamKiUlRZWVlSoqKtLtt99uay/iDwDAN8SqVas0aNAglZeXy7IsTZ48WevXr7e9jisUCoViMJ9tW/cdjfcIgONM6WiO9wiAIyVMmxXT9fs+PRG1tQZdlxa1taKFj/oBAGAY4g8AgGGIPwAAhiH+AAAYhvgDAGAY4g8AgGGIPwAAhiH+AAAYhvgDAGAY4g8AgGGIPwAAhiH+AAAYhvgDAGAY4g8AgGGIPwAAhiH+AAAYhvgDAGAY4g8AgGGIPwAAhiH+AAAYhvgDAGAY4g8AgGGIPwAAhiH+AAAYhvgDAGAY4g8AgGGIPwAAhiH+AAAYhvgDAGAY4g8AgGGIPwAAhkmI9wAAADhNoO9/RW2tLN0dtbWihTt/AAAMQ/wBAHCImpoa5efnKyUlRSkpKSoqKtI777wTfnzixIlyuVwRx+LFi23vw8v+AAA4xJgxY7Ry5Updd911CoVC2rhxo2bMmKGPPvpIN998syTpoYce0pNPPhl+zrBhw2zvQ/wBAHCI6dOnR3z961//WjU1NdqzZ084/sOGDZPX672sfXjZHwCAGLIsS93d3RGHZVkXfV5vb682bdqkYDCooqKi8PnXXntN6enpysvLk8/n0xdffGF7JuIPAEAM+f1+eTyeiMPv95/3+gMHDmjEiBFyu91avHix6urqdNNNN0mS5syZo1dffVXvvvuufD6fXnnlFc2bN8/2TK5QKBS65L9RFG3ddzTeIwCOM6WjOd4jAI6UMG1WTNf/vG1X1NZKu+aOc+703W633G53v9efPXtWhw8fVldXl15//XX9/ve/V0NDQ/g/AP9q165dKikpUXt7u8aNGzfgmWy/53/8+HH94Q9/UFNTkwKBgCTJ6/Xqe9/7nhYsWKBRo0bZXRIAgCvWhULfn6SkJI0fP16SVFBQoObmZj377LN6/vnnz7m2sLBQkmzH39bL/s3Nzbr++uu1Zs0aeTweFRcXq7i4WB6PR2vWrFFubq727t170XX6e/+j5+zF3/8AAMA0fX195/0egdbWVklSZmamrTVt3flXVlbqBz/4gZ577jm5XK6Ix0KhkBYvXqzKyko1NTVdcB2/368VK1ZEnHtgUZXm/PdqO+MAAHBF8fl8KisrU05Ojk6dOqXa2lrt3r1b27dv18GDB1VbW6upU6cqLS1N+/fvV1VVlYqLi5Wfn29rH1vv+Q8dOlQfffSRcnNz+338k08+0be//W19+eWXF1zHsqxz/hdT/7//ocSkgb8sApiA9/yB/n2T3vPPumHgP9534cKFqq+vV0dHhzwej/Lz87Vs2TLdc889OnLkiObNm6ePP/5YwWBQ2dnZmjVrlpYvX66UlBRbM9m68/d6vfrwww/PG/8PP/xQGRkZF12nv/c/EpO67YwCAMAV58UXXzzvY9nZ2WpoaIjKPrbi/+ijj2rRokVqaWlRSUlJOPSdnZ2qr6/XCy+8oN/+9rdRGQwAAMSGrfhXVFQoPT1dq1at0vr169Xb2ytJGjx4sAoKCrRhwwb98Ic/jMmgAAAgOmx/1O/+++/X/fffr56eHh0/flySlJ6ersTExKgPBwAAou+Sf7Z/YmKi7Y8WAACA+OPH+wIAYBjiDwCAYYg/AACGIf4AABiG+AMAYBjiDwCAYYg/AACGIf4AABiG+AMAYBjiDwCAYYg/AACGIf4AABiG+AMAYBjiDwCAYYg/AACGIf4AABiG+AMAYBjiDwCAYYg/AACGIf4AABiG+AMAYBjiDwCAYYg/AACGIf4AABgmId4DAADgNFk3eOI9Qkxx5w8AgGGIPwAAhiH+AAAYhvgDAGAY4g8AgEPU1NQoPz9fKSkpSklJUVFRkd55553w42fOnFFFRYXS0tI0YsQIlZeXq7Oz0/Y+xB8AAIcYM2aMVq5cqZaWFu3du1d33323ZsyYob/+9a+SpKqqKr311lvavHmzGhoa9Pnnn2v27Nm293GFQqFQtIe/FFv3HY33CIDjTOlojvcIgCMlTJsV4x1aorhWwWU9OzU1VU8//bTuu+8+jRo1SrW1tbrvvvskSZ988oluvPFGNTU16fbbbx/wmtz5AwAQQ5Zlqbu7O+KwLOuiz+vt7dWmTZsUDAZVVFSklpYW9fT0qLS0NHxNbm6ucnJy1NTUZGsm4g8AQAz5/X55PJ6Iw+/3n/f6AwcOaMSIEXK73Vq8eLHq6up00003KRAIKCkpSSNHjoy4PiMjQ4FAwNZM/IQ/AABiyOfzqbq6OuKc2+0+7/U33HCDWltb1dXVpddff13z589XQ0NDVGci/gAAxJDb7b5g7L8uKSlJ48ePlyQVFBSoublZzz77rO6//36dPXtWJ0+ejLj77+zslNfrtTUTL/sDAOBgfX19sixLBQUFSkxMVH19ffixtrY2HT58WEVFRbbW5M4fAACH8Pl8KisrU05Ojk6dOqXa2lrt3r1b27dvl8fj0cKFC1VdXa3U1FSlpKSosrJSRUVFtr7TXyL+AAA4xrFjx/TjH/9YHR0d8ng8ys/P1/bt23XPPfdIklatWqVBgwapvLxclmVp8uTJWr9+ve19+Jw/4GB8zh/on0mf848F3vMHAMAwxB8AAMMQfwAADEP8AQAwDPEHAMAwxB8AAMMQfwAADEP8AQAwDPEHAMAwxB8AAMMQfwAADEP8AQAwDPEHAMAwxB8AAMMQfwAADEP8AQAwDPEHAMAwxB8AAMMQfwAADEP8AQAwDPEHAMAwxB8AAMMQfwAADEP8AQAwDPEHAMAwCfEeAAAApwn97W9RW8t1fUHU1ooW7vwBADAM8QcAwDDEHwAAwxB/AAAMQ/wBADAM8QcAwDDEHwAAwxB/AAAcwu/367bbblNycrJGjx6tmTNnqq2tLeKaiRMnyuVyRRyLFy+2tQ/xBwDAIRoaGlRRUaE9e/Zox44d6unp0b333qtgMBhx3UMPPaSOjo7w8dRTT9nah5/wBwCAQ2zbti3i6w0bNmj06NFqaWlRcXFx+PywYcPk9XoveR/u/AEAiCHLstTd3R1xWJY1oOd2dXVJklJTUyPOv/baa0pPT1deXp58Pp+++OILWzMRfwAAYsjv98vj8UQcfr//os/r6+vT0qVLdccddygvLy98fs6cOXr11Vf17rvvyufz6ZVXXtG8efNszeQKhUIh23+TGNi672i8RwAcZ0pHc7xHABwpYdqsmK4f+tsfo7bW2atnn3On73a75Xa7L/i8hx9+WO+8847ee+89jRkz5rzX7dq1SyUlJWpvb9e4ceMGNBPv+QMAEEMDCf3XLVmyRFu3blVjY+MFwy9JhYWFkkT8AQD4JgqFQqqsrFRdXZ12796tsWPHXvQ5ra2tkqTMzMwB70P8AQBwiIqKCtXW1urNN99UcnKyAoGAJMnj8Wjo0KE6ePCgamtrNXXqVKWlpWn//v2qqqpScXGx8vPzB7wP8QcAwCFqamok/ecP8vlXL730khYsWKCkpCTt3LlTq1evVjAYVHZ2tsrLy7V8+XJb+xB/AAAc4mLfg5+dna2GhobL3oeP+gEAYBjH3PnzkSbgXF8Wl8Z7BMCRkuM9wDccd/4AABiG+AMAYBjiDwCAYYg/AACGIf4AABiG+AMAYBjiDwCAYYg/AACGIf4AABiG+AMAYBjiDwCAYYg/AACGIf4AABiG+AMAYBjiDwCAYYg/AACGIf4AABiG+AMAYBjiDwCAYYg/AACGSYj3AAAAOM3fu4ujttbYqK0UPdz5AwBgGOIPAIBhiD8AAIYh/gAAGIb4AwBgGOIPAIBhiD8AAIYh/gAAGIb4AwBgGOIPAIBhiD8AAA7h9/t12223KTk5WaNHj9bMmTPV1tYWcc2ZM2dUUVGhtLQ0jRgxQuXl5ers7LS1D/EHAMAhGhoaVFFRoT179mjHjh3q6enRvffeq2AwGL6mqqpKb731ljZv3qyGhgZ9/vnnmj17tq19XKFQKBTt4S/FV2/XxXsEwHG+LC6N9wiAIyUnJ8d0/UN7/2/U1hr7nf+45Of+4x//0OjRo9XQ0KDi4mJ1dXVp1KhRqq2t1X333SdJ+uSTT3TjjTeqqalJt99++4DW5c4fAIAYsixL3d3dEYdlWQN6bldXlyQpNTVVktTS0qKenh6Vlv7/G4Pc3Fzl5OSoqalpwDMRfwAAYsjv98vj8UQcfr//os/r6+vT0qVLdccddygvL0+SFAgElJSUpJEjR0Zcm5GRoUAgMOCZEmz9DQAAgC0+n0/V1dUR59xu90WfV1FRoY8//ljvvfde1Gci/gAAxJDb7R5Q7P/VkiVLtHXrVjU2NmrMmDHh816vV2fPntXJkycj7v47Ozvl9XoHvD4v+wMA4BChUEhLlixRXV2ddu3apbFjx0Y8XlBQoMTERNXX14fPtbW16fDhwyoqKhrwPtz5AwDgEBUVFaqtrdWbb76p5OTk8Pv4Ho9HQ4cOlcfj0cKFC1VdXa3U1FSlpKSosrJSRUVFA/5Of4n4AwDgGDU1NZKkiRMnRpx/6aWXtGDBAknSqlWrNGjQIJWXl8uyLE2ePFnr16+3tQ+f8wccjM/5A/0z5XP+scJ7/gAAGIb4AwBgGOIPAIBhiD8AAIYh/gAAGIb4AwBgGOIPAIBhiD8AAIYh/gAAGIb4AwBgGOIPAIBhiD8AAIYh/gAAGIb4AwBgGOIPAIBhiD8AAIYh/gAAGIb4AwBgGOIPAIBhEuI9AAAATvPPa/5P1NYaq/+I2lrRwp0/AACGIf4AABiG+AMAYBjiDwCAYYg/AACGIf4AABiG+AMAYBjiDwCAYYg/AACGIf4AABiG+AMAYBjiDwCAYYg/AACGIf4AADhEY2Ojpk+frqysLLlcLm3ZsiXi8QULFsjlckUcU6ZMsb0P8QcAwCGCwaAmTJigdevWnfeaKVOmqKOjI3z88Y9/tL1PwuUMCQAAoqesrExlZWUXvMbtdsvr9V7WPtz5AwAQQ5Zlqbu7O+KwLOuS19u9e7dGjx6tG264QQ8//LBOnDhhew3iDwBADPn9fnk8nojD7/df0lpTpkzRyy+/rPr6ev3mN79RQ0ODysrK1Nvba2sdVygUCl3SBFH21dt18R4BcJwvi0vjPQLgSMnJyTFdv+X4+1FbKy+54Jw7fbfbLbfbfcHnuVwu1dXVaebMmee95rPPPtO4ceO0c+dOlZSUDHgm7vwBAIght9utlJSUiONi4R+oa6+9Vunp6Wpvb7f1POIPAMA31NGjR3XixAllZmbaeh7f7Q8AgEOcPn064i7+0KFDam1tVWpqqlJTU7VixQqVl5fL6/Xq4MGDeuyxxzR+/HhNnjzZ1j7EHwAAh9i7d68mTZoU/rq6ulqSNH/+fNXU1Gj//v3auHGjTp48qaysLN1777365S9/afttBOIPAIBDTJw4URf6Pvzt27dHZR/e8wcAwDDEHwAAwxB/AAAMQ/wBADAM8QcAwDBRj/+RI0f0k5/85ILX9PtLDnp6oj0KAADoR9Tj/89//lMbN2684DX9/ZKD3/yP/xntUQAAQD9sf87/z3/+8wUf/+yzzy66hs/nC//ggv8yeNc2u6MAAIBLYDv+M2fOlMvluuAPIXC5XBdco7/fZvRVYqLdUQAAwCWw/bJ/Zmam3njjDfX19fV77Nu3LxZzAgCAKLEd/4KCArW0tJz38Yu9KgAAAOLL9sv+P/3pTxUMBs/7+Pjx4/Xuu+9e1lAAACB2bMf/rrvuuuDjw4cP1/e///1LHggAAMQWP+QHAADDEH8AAAxD/AEAMIzt9/wBALjSdRzOid5i6dFbKlq48wcAwDDEHwAAwxB/AAAMQ/wBADAM8QcAwDDEHwAAwxB/AAAMQ/wBADAM8QcAwDDEHwAAwxB/AAAMQ/wBADAM8QcAwDDEHwAAwxB/AAAMQ/wBADAM8QcAwCEaGxs1ffp0ZWVlyeVyacuWLRGPh0Ih/eIXv1BmZqaGDh2q0tJSffrpp7b3If4AADhEMBjUhAkTtG7dun4ff+qpp7RmzRo999xz+uCDDzR8+HBNnjxZZ86csbVPQjSGBQAAl6+srExlZWX9PhYKhbR69WotX75cM2bMkCS9/PLLysjI0JYtW/SjH/1owPtw5w8AQAxZlqXu7u6Iw7Is2+scOnRIgUBApaWl4XMej0eFhYVqamqytRbxBwAghvx+vzweT8Th9/ttrxMIBCRJGRkZEeczMjLCjw0UL/sDABBDPp9P1dXVEefcbnecpvlPxB8AgBhyu91Rib3X65UkdXZ2KjMzM3y+s7NTt9xyi621eNkfAIBvgLFjx8rr9aq+vj58rru7Wx988IGKiopsrcWdPwAADnH69Gm1t7eHvz506JBaW1uVmpqqnJwcLV26VL/61a903XXXaezYsfr5z3+urKwszZw509Y+xB8AAIfYu3evJk2aFP76v75XYP78+dqwYYMee+wxBYNBLVq0SCdPntSdd96pbdu2aciQIbb2cYVCoVBUJ79EX71dF+8RAMf5srj04hcBBkpOTo7p+lv3HY3aWv/t1jFRWytaeM8fAADDEH8AAAxD/AEAMAzxBwDAMMQfAADDEH8AAAxD/AEAMAzxBwDAMMQfAADDEH8AAAxD/AEAMAzxBwDAMMQfAADD8Ct9AQD4mikdzVFcjd/qBwAA4oz4AwBgGOIPAIBhiD8AAIYh/gAAGIb4AwBgGOIPAIBhiD8AAIYh/gAAGIb4AwBgGOIPAIBhiD8AAIYh/gAAGIb4AwBgGOIPAIBhiD8AAIYh/gAAGIb4AwBgGOIPAIBhiD8AAA7xxBNPyOVyRRy5ublR3ych6isCAIBLdvPNN2vnzp3hrxMSop9q4g8AgIMkJCTI6/XGdA9e9gcAIIYsy1J3d3fEYVnWea//9NNPlZWVpWuvvVZz587V4cOHoz4T8QcAIIb8fr88Hk/E4ff7+722sLBQGzZs0LZt21RTU6NDhw7prrvu0qlTp6I6kysUCoWiuuIl+urtuniPADjOl8Wl8R4BcKTk5OSYrh/NJvWWTj3nTt/tdsvtdl/0uSdPntTVV1+tZ555RgsXLozaTLznDwBADA009P0ZOXKkrr/+erW3t0d1Jl72BwDAoU6fPq2DBw8qMzMzqusSfwAAHOLRRx9VQ0OD/v73v+v999/XrFmzNHjwYD3wwANR3YeX/QEAcIijR4/qgQce0IkTJzRq1Cjdeeed2rNnj0aNGhXVfYg/AAAOsWnTpn/LPrzsDwCAYYg/AACGIf4AABiG+AMAYBjiDwCAYYg/AACGIf4AABiG+AMAYBjiDwCAYYg/AACGcYVCoVC8h4BzWJYlv98vn893yb+CErjS8O8CVxrijwjd3d3yeDzq6upSSkpKvMcBHIF/F7jS8LI/AACGIf4AABiG+AMAYBjijwhut1uPP/4439QE/Av+XeBKwzf8AQBgGO78AQAwDPEHAMAwxB8AAMMQfwAADEP8AQAwDPFH2Lp163TNNddoyJAhKiws1IcffhjvkYC4amxs1PTp05WVlSWXy6UtW7bEeyQgKog/JEl/+tOfVF1drccff1z79u3ThAkTNHnyZB07dizeowFxEwwGNWHCBK1bty7eowBRxef8IUkqLCzUbbfdprVr10qS+vr6lJ2drcrKSv3sZz+L83RA/LlcLtXV1WnmzJnxHgW4bNz5Q2fPnlVLS4tKS0vD5wYNGqTS0lI1NTXFcTIAQCwQf+j48ePq7e1VRkZGxPmMjAwFAoE4TQUAiBXiDwCAYYg/lJ6ersGDB6uzszPifGdnp7xeb5ymAgDECvGHkpKSVFBQoPr6+vC5vr4+1dfXq6ioKI6TAQBiISHeA8AZqqurNX/+fH3nO9/Rd7/7Xa1evVrBYFAPPvhgvEcD4ub06dNqb28Pf33o0CG1trYqNTVVOTk5cZwMuDx81A9ha9eu1dNPP61AIKBbbrlFa9asUWFhYbzHAuJm9+7dmjRp0jnn58+frw0bNvz7BwKihPgDAGAY3vMHAMAwxB8AAMMQfwAADEP8AQAwDPEHAMAwxB8AAMMQfwAADEP8AQAwDPEHAMAwxB8AAMMQfwAADPP/AHhFOfzq0anWAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -2227,7 +3429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 305,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -2250,22 +3452,132 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "model accuracy is : 95.0 %\n"
+      "Model accuracy is: 95.38 %\n"
      ]
     }
    ],
    "source": [
     "from sklearn.metrics import accuracy_score\n",
     "accuracy = accuracy_score(y_test,prediction)*100\n",
-    "print(f'model accuracy is : {accuracy.round()} %')"
+    "print(f'Model accuracy is: {round(accuracy, 2)} %')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "GRADIENT BOOSTING MACHINES \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 310,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n",
+       "       1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
+       "       1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1])"
+      ]
+     },
+     "execution_count": 310,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import xgboost as xgb\n",
+    "xgb = xgb.XGBClassifier()\n",
+    "xgb.fit(X_train,y_train)\n",
+    "prediction = xgb.predict(X_test)\n",
+    "prediction\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 312,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification report\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "       False       1.00      0.82      0.90        17\n",
+      "        True       0.94      1.00      0.97        48\n",
+      "\n",
+      "    accuracy                           0.95        65\n",
+      "   macro avg       0.97      0.91      0.94        65\n",
+      "weighted avg       0.96      0.95      0.95        65\n",
+      "\n",
+      "\n",
+      "\n",
+      "Confission matrix\n",
+      "[[14  3]\n",
+      " [ 0 48]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Classification report')\n",
+    "print(classification_report(y_test,prediction))\n",
+    "print('\\n')\n",
+    "print('Confission matrix')\n",
+    "print(confusion_matrix(y_test,prediction))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 313,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 313,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(confusion_matrix(y_test,prediction_logi),cmap='Pastel1')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 314,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model accuracy is: 95.38 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "accuracy = accuracy_score(y_test,prediction)*100\n",
+    "print(f'Model accuracy is: {round(accuracy, 2)} %')"
+   ]
   }
  ],
  "metadata": {
@@ -2289,7 +3601,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,
diff --git a/Parkinsons-Disease-Detector-main/README.md b/Parkinsons-Disease-Detector-main/README.md
index dad371f9e..2b808a84b 100644
--- a/Parkinsons-Disease-Detector-main/README.md
+++ b/Parkinsons-Disease-Detector-main/README.md
@@ -14,6 +14,7 @@ Utilizing a combination of EDA techniques and machine learning algorithms, we ha
 2. K-Nearest Neighbors (KNN)
 3. Random Forest Regressor
 4. Decision Tree Regressor
+5. Gradient Boosting Machine (XGBoost)
 ## Libraries Used
 
 1. numpy: For efficient numerical operations
@@ -27,6 +28,7 @@ Utilizing a combination of EDA techniques and machine learning algorithms, we ha
 2. K-Nearest Neighbors: Demonstrated superior performance with a test set accuracy of 94.0%
 3. Random Forest Regressor: Equally impressive accuracy of 94.0%
 4. Decision Tree Regressor: Achieved a commendable accuracy of 91.0%
+5. Gradient Boosting Machine: Achieved a test set accuracy of 94.0%
 ## Conclusion
 
-Through rigorous analysis and experimentation, it has been determined that K-Nearest Neighbors and Random Forest Regressor models exhibit the highest predictive accuracy for Parkinson's disease. 
+Through rigorous analysis and experimentation, it has been determined that K-Nearest Neighbors , Random Forest Regressor and  Gradient Boosting Machine models exhibit the highest predictive accuracy for Parkinson's disease.