K nearest neighbour..ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ef9a906b-1890-4cd8-ad33-258c8f67d334",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAGdCAYAAACyzRGfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAijUlEQVR4nO3de3xU9f3n8feZTDKJmAwECSG/JEgVuau0sipYFKEgUi7FW6sgXvrg1zZRQZdFtGhbqwErXrAU1LWwXUS2tg0GukUpl+ANQShaFIMoi9FsQEVmSIAwyXx/f0SikYRkksn3zJDX8/GYx4OcczLz8TBkXp45c+IYY4wAAAAs8bg9AAAAaF+IDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFjldXuAbwuHwyorK1Nqaqocx3F7HAAA0AzGGB06dEhZWVnyeE5+bCPm4qOsrEw5OTlujwEAAFqgtLRU2dnZJ90m5uIjNTVVUu3waWlpLk8DAACaIxgMKicnp+51/GRiLj6Ov9WSlpZGfAAAEGeac8oEJ5wCAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYFXMXGQMA2GWqP5ZC2yV5pKQL5CRkuj0STnHEBwC0U6bmc5nALOlY8TeWemSSR8tJe0CO53TXZsOpjfgAgHbIhCtkDlwv1ZR+a01YOvoPmZpPpfTn5DiJrsyHUxvnfABAe3Tkr1LNXkk1DawM174NU/VPy0OhvSA+AKAdMkf+0sQWHpnDf7UyC9of4gMA2qOazySZk2wQlsLltqZBO0N8AEB7lNBV0sl+9blH8mTZmgbtDPEBAO2Qk3JNE1uE5Zx2tZVZ0P4QHwDQHqVMlLznSEpoYKVHSrpQ8l1ueyq0E8QHALRDjuc0OelLJd8o1X8p8EopV8vp9LQch6sxoG3wzAKAdsrx+OV0elymZp8U+rdqj3gMlOPp5PZoOMURHwDQzjkJXb86ARWwg7ddAACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHALQxY47JmJDbYwCSYuP56HX10QHgFGWMkY4WyVQulqrfq12WOFBOh5/KSf6By9OhvTHGSEcKZQ4vlqpLapclDvrq+TjM+jwRHfkoKCjQoEGDlJqaqoyMDE2YMEElJSUNbmuM0ejRo+U4jlasWBGNWQEgLhhjZIIPyARmSNU7v14RelvmYJ5Mxe/dGw7tTu3z8Zcywbul6l1frwhtlTn4nzIVz1ifKaL4KC4uVl5enjZt2qQ1a9YoFApp5MiRqqysPGHbxx9/XI7jRG1QAIgbxzZKR5Z+9YX5xopw7ZKK+TKhd6yPhXaq6mXpyAtffdHQ8/F3MqGGDyS0lYjedlm9enW9r5csWaKMjAxt3bpVQ4cOrVu+fft2zZs3T2+99Za6desWnUkBIE6YyuckJUiqaWSLBJnKZXI6nmtxKrRXpnKpmnw+HnleTuKvrM3UqnM+AoGAJCk9Pb1u2eHDh3X99ddrwYIFyszMbPI+qqqqVFVVVfd1MBhszUgA4L7qd9X4D3rVrqveYWsatHfVO9Xk8zH0rq1pJLXi0y7hcFjTpk3TkCFD1L9//7rl06dP1+DBgzV+/Phm3U9BQYH8fn/dLScnp6UjAUBscHzN2Ca57ecApJh8PrY4PvLy8rRjxw4tX768bllRUZHWrVunxx9/vNn3M2vWLAUCgbpbaWlpS0cCgNjgG6naw9yNceT4RtqaBu1d8g/U9PPR7iewWhQf+fn5WrVqldavX6/s7Oy65evWrdOHH36ojh07yuv1yuutfVfnqquu0mWXXdbgffl8PqWlpdW7AUA8c067QVKiGv4R65GcVOm0qy1PhfbKOW2KauOjoQ+BJEhORyllgt2ZjDGm6c1qGWN02223qbCwUBs2bFDPnj3rrS8vL9fnn39eb9mAAQP0xBNPaOzYserRo0eTjxEMBuX3+xUIBAgRAHHLVL0pc/DnkqlU7Q99R1KN5HSSk/6snMT+TdwDED2m6lWZg/mSOaJ6z0dPFzmdnpWT2LvVjxHJ63dEJ5zm5eVp2bJlevHFF5Wamqry8nJJkt/vV0pKijIzMxs8yTQ3N7dZ4QEApwrHd6HUZWPthcaObZXkyEm6WEoZI4fzPWCZ47tE6vKKdGSFTOhfkhLk+AZLyVfKac45IdGeJ5IjH41dt2Px4sW66aabGv2ewsJCTZgwoVmPwZEPAADiT5sd+YigU1r1PQAA4NTFL5YDAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArPK6PQCA2PT/9+zTe6/vkuNI/b/fRxk5Z7g9EoBTRERHPgoKCjRo0CClpqYqIyNDEyZMUElJSd36AwcO6LbbblOvXr2UkpKi3Nxc3X777QoEAlEfHEDbCHwe1H3j5+rGs/M1Z/J8FUyar0ln/kIPXPeoKg5Wuj0egFNARPFRXFysvLw8bdq0SWvWrFEoFNLIkSNVWVn7A6msrExlZWV65JFHtGPHDi1ZskSrV6/Wrbfe2ibDA4iuI5VHdddl9+vN/7tNMl8vN8bo1b+9qZkjf6NjVSH3BgRwSnCMMabpzRr22WefKSMjQ8XFxRo6dGiD27zwwguaNGmSKisr5fU2/S5PMBiU3+9XIBBQWlpaS0cD0AIrF76k+fn/s154fNvMP92mEZMa/vcOoP2K5PW7VSecHn87JT09/aTbpKWlNRoeVVVVCgaD9W4A3LF68Xo5J1nv8ThavXidtXkAnJpaHB/hcFjTpk3TkCFD1L9//wa3+fzzz/XAAw9o6tSpjd5PQUGB/H5/3S0nJ6elIwFopS/KDuhkx0LDYaMvPv3S3kAATkktjo+8vDzt2LFDy5cvb3B9MBjUmDFj1LdvX/3qV79q9H5mzZqlQCBQdystLW3pSABa6YzsznI8jR/78HgcZeR2tjgRgFNRiz5qm5+fr1WrVmnjxo3Kzs4+Yf2hQ4d0xRVXKDU1VYWFhUpMTGz0vnw+n3w+X0vGABBlV946XCWbdze6Phw2uuKW4RYnAnAqiujIhzFG+fn5Kiws1Lp169SjR48TtgkGgxo5cqSSkpJUVFSk5OTkqA0LoG2NmDxUZw/sIU/CiT8aPAke9RvSS9+/6kIXJgNwKokoPvLy8rR06VItW7ZMqampKi8vV3l5uY4cOSLp6/CorKzUs88+q2AwWLdNTU1Nm/wHAIiepOQkPbLufl123eB6AZLgTdDIKZeqYPUv5U3k2oQAWieij9o6TsPvBS9evFg33XSTNmzYoGHDhjW4zZ49e3TmmWc2+Rh81BaIDQfKv9T7b+6WHKnvxeeoYxe/2yMBiGGRvH636jofbYH4AAAg/li7zgcAAECkiA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriI8YUx2qVnWo2u0xAESRMcdkTMjtMeIe+/HU4XV7AEjGGG38yyb9ZV6R3t+8W5J0zgVn6eo7x+qy6wbLcRyXJwQQKWOMdLRIpnKxVP1e7bLEgXI6/FRO8g9cni5+1O7HFV/tx/drlyV+76v9ONzl6dBSjjHGuD3ENwWDQfn9fgUCAaWlpbk9jhXP3rNMy+cUyuNxFA7X/nUc//M1d43V1N/d6PKEACJhjJEJPiAdWSrJkXT8x6xHUljO6bfLOT3fvQHjRO1+vF86slzH912t4/txupzTf+7egKgnktdv3nZx2Y5Xd2r5nEJJqguPb/75hXkrtX39DldmA9BCxzZ+FR7S1+EhHX/xNBXzZULvWB8r7lSt/yo8pK/D4+s/m4rHZELvWR8LrUd8uKzoDy8pwdv4X0OC16OiP7xkcSIArWUqn5OUcJItEmQql9kaJ26Zw0vV5H48/LytcRBFxIfLdr31oWqqw42ur6kO64OtH1mcCECrVb8rqeYkG9RI1RzRbFKoGfsx9G9b0yCKiA+XJaUkNWObRAuTAIgax9eMbZLbfo5416z9mNL2cyDqiA+XfX/iRfJ4Gv9r8CR49P2JF1mcCECr+Ubq5G8XOHJ8I21NE7+SR6nJ/cgnh+IS8eGyK6eOkK9DkjyeEz9O63gc+VKSNOY/+ccFxBPntBskJarhH7EeyUmVTrva8lTxxzltsmqvCNHQ5QYSJMcvpUy0PBWigfhwWedunTTnpdnq0LGDpNojHZ4Ej+RIHfynqWD1L9Ulu7PLUwKIhOPNkdPpGck5TbUvnB7V/R+845eTvkSOJ93FCeOD482V0+mpr95a+dZ+9HT8aj92dG9AtBjX+YgRRyqPav3zr+md4ndljNG5Q/vq8hu+r5QOvC8MxCsTrqi90NixrZIcOUkXSylj5HC+R0RMuEI6skIm9C/VvmU1WEq+kv0YYyJ5/SY+AABAq3GRMQAAELOIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArIooPgoKCjRo0CClpqYqIyNDEyZMUElJSb1tjh49qry8PHXu3Fmnn366rrrqKu3bty+qQwMAgPgVUXwUFxcrLy9PmzZt0po1axQKhTRy5EhVVlbWbTN9+nStXLlSL7zwgoqLi1VWVqaJEydGfXAAABCfHGOMaek3f/bZZ8rIyFBxcbGGDh2qQCCgLl26aNmyZbr66qslSe+//7769OmjN954QxdddFGT9xkMBuX3+xUIBJSWltbS0QAAgEWRvH636pyPQCAgSUpPT5ckbd26VaFQSCNGjKjbpnfv3srNzdUbb7zR4H1UVVUpGAzWuwEAgFNXi+MjHA5r2rRpGjJkiPr37y9JKi8vV1JSkjp27Fhv265du6q8vLzB+ykoKJDf76+75eTktHQkAAAQB1ocH3l5edqxY4eWL1/eqgFmzZqlQCBQdystLW3V/QEAgNjmbck35efna9WqVdq4caOys7PrlmdmZurYsWM6ePBgvaMf+/btU2ZmZoP35fP55PP5WjIGAACIQxEd+TDGKD8/X4WFhVq3bp169OhRb/33vvc9JSYmau3atXXLSkpK9PHHH+viiy+OzsQAACCuRXTkIy8vT8uWLdOLL76o1NTUuvM4/H6/UlJS5Pf7deutt+rOO+9Uenq60tLSdNttt+niiy9u1iddAADAqS+ij9o6jtPg8sWLF+umm26SVHuRsbvuukvPP/+8qqqqNGrUKP3hD39o9G2Xb+OjtgAAxJ9IXr9bdZ2PtkB8AAAQf6xd5wMAACBSxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVXrcHQHzZt/czvfva+5KkfkN6q2v3Li5PBACIN8QHmiV44JAem7pIrxVukTFGkuQ4jgZPGKQ7n/mZ0tJTXZ4QABAveNsFTTp29JhmXP5rvf7iW3XhIUnGGL1R9JZmXP5rVR2pcnFCAEA8IT7QpLXPvaKP3tmrcE34hHXhmrA+emev1j//mguTAQDiEfGBJr20eL0cj9PoesfjaPUf11mcCAAQz4gPNOmLsi9lwqbR9SZs9EXZlxYnAgDEM+IDTTojp3OTRz665HS2OBEAIJ4RH2jSlbcOb/LIx+hbh1ucCAAQz4gPNOnS6war94Vny5Nw4tPFk+BR7/92ti69brALkwEA4hHxgSYl+RI19+X7dPn1lyjB+/VTJsHr0eXXX6K5a+5Tki/RxQkBAPHEMd+8cEMMCAaD8vv9CgQCSktLc3scfMuX+wPauWmXJKnPReeoU4bf5YkAALEgktdvrnCKiHTK8GvwuEFujwEAiGO87QIAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsCri+Ni4caPGjh2rrKwsOY6jFStW1FtfUVGh/Px8ZWdnKyUlRX379tWiRYuiNS8AAIhzEcdHZWWlzjvvPC1YsKDB9XfeeadWr16tpUuXaufOnZo2bZry8/NVVFTU6mEBAED880b6DaNHj9bo0aMbXf/6669rypQpuuyyyyRJU6dO1VNPPaXNmzdr3LhxLR4UAACcGqJ+zsfgwYNVVFSkTz/9VMYYrV+/Xrt27dLIkSMb3L6qqkrBYLDeDQAAnLqiHh9PPvmk+vbtq+zsbCUlJemKK67QggULNHTo0Aa3LygokN/vr7vl5OREeyQAABBD2iQ+Nm3apKKiIm3dulXz5s1TXl6e/vnPfza4/axZsxQIBOpupaWl0R4JAADEkIjP+TiZI0eO6J577lFhYaHGjBkjSTr33HO1fft2PfLIIxoxYsQJ3+Pz+eTz+aI5BgAAiGFRPfIRCoUUCoXk8dS/24SEBIXD4Wg+FAAAiFMRH/moqKjQ7t27677es2ePtm/frvT0dOXm5urSSy/VjBkzlJKSou7du6u4uFh/+tOf9Oijj0Z1cAAAEJ8cY4yJ5Bs2bNigYcOGnbB8ypQpWrJkicrLyzVr1iy9/PLLOnDggLp3766pU6dq+vTpchynyfsPBoPy+/0KBAJKS0uLZDQAAOCSSF6/I46PtkZ8AAAQfyJ5/eZ3uwAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKu8bg+AWoe+rNCa/1Wst4vflTFG5w7tq5E3Xaa09FS3RwMAIKocY4xxe4hvCgaD8vv9CgQCSktLc3scK94uflezx87R0coqSUZGkiNHvtOS9JsXZ2rg5QPcHhEAgJOK5PWbt11c9tknX+jeMQU6erhKxhgZI8lIxhhVHTmmX46do317P3N7TAAAoob4cNmqRS8rVBWSCZ94AMqEjaqPVWvlwpdcmAwAgLZBfLjs9Re3KFwTbnR9uCas117cYnEiAADaFvHhstCxUJPbVFdVW5gEAAA7iA+X9bnoHCV4G/9rSPB61Puisy1OBABA2yI+XDbuF1eoprrxt11qqsManzfa4kQAALQt4sNlfS7sqZse+LEkyZPw9V/H8T/feP+16j+ktyuzAQDQFrjIWAy44d6rdPbAHvrrY6v0zsb3JEkDLumjq6b/UBf98HsuTwcAQHRxkbEYc/yvw3EclycBAKD5Inn95shHjCE6AACnOs75AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVnndHsCW/aWfa8crO2WM1G9IL2WemeH2SAAAtEsRH/nYuHGjxo4dq6ysLDmOoxUrVpywzc6dOzVu3Dj5/X516NBBgwYN0scffxyNeSN26MsK/ebaeZp05i9UMGm+5kyer8ln5em+CQ8r8HnQlZkAAGjPIo6PyspKnXfeeVqwYEGD6z/88ENdcskl6t27tzZs2KB33nlHs2fPVnJycquHjdSxqpD+xw9+o9cKN8sY8/UKI735963678N+paOHq6zPBQBAexbx2y6jR4/W6NGjG11/77336sorr9TDDz9ct+yss85q2XStVPx/XtfubXsaXBeuCev/vVeqtUs3aszUH1ieDACA9iuqJ5yGw2H9/e9/1znnnKNRo0YpIyNDF154YYNvzRxXVVWlYDBY7xYtqxevk+NxGl3vyNE//rguao8HAACaFtX42L9/vyoqKjRnzhxdccUVevnll/WjH/1IEydOVHFxcYPfU1BQIL/fX3fLycmJ2jxffHpAJmwaXW+M0RdlB6L2eAAAoGlRP/IhSePHj9f06dN1/vnn6+6779YPf/hDLVq0qMHvmTVrlgKBQN2ttLQ0avN0yT1DnpMd+fA46pJ9RtQeDwAANC2q8XHGGWfI6/Wqb9++9Zb36dOn0U+7+Hw+paWl1btFy+hbLlf4ZEc+wkZX/nR41B4PAAA0LarxkZSUpEGDBqmkpKTe8l27dql79+7RfKhmGXrNxeo7uJc8CSf+Z3oSPOr53e/o8usvsT4XAADtWcSfdqmoqNDu3bvrvt6zZ4+2b9+u9PR05ebmasaMGbruuus0dOhQDRs2TKtXr9bKlSu1YcOGaM7dLN5Er+asvlcL7lisf/7vjaqprpEkebweDbtuiG77/a1KSk6yPhcAAO2ZY+pdAKNpGzZs0LBhw05YPmXKFC1ZskSS9Mc//lEFBQX65JNP1KtXL/3617/W+PHjm3X/wWBQfr9fgUAgqm/BHPwsoPfe2CUZqc9FPdWpa8eo3TcAAO1dJK/fEcdHW2ur+AAAAG0nktdvfrEcAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwKuLf7dLWjl9wNRgMujwJAABoruOv2825cHrMxcehQ4ckSTk5OS5PAgAAInXo0CH5/f6TbhNzv9slHA6rrKxMqampchzH7XGsCwaDysnJUWlpKb/bphXYj9HBfowO9mN0sB+jo632ozFGhw4dUlZWljyek5/VEXNHPjwej7Kzs90ew3VpaWn844oC9mN0sB+jg/0YHezH6GiL/djUEY/jOOEUAABYRXwAAACriI8Y4/P5dP/998vn87k9SlxjP0YH+zE62I/RwX6MjljYjzF3wikAADi1ceQDAABYRXwAAACriA8AAGAV8QEAAKwiPmLQnDlz5DiOpk2b5vYocenTTz/VpEmT1LlzZ6WkpGjAgAF666233B4rrtTU1Gj27Nnq0aOHUlJSdNZZZ+mBBx5o1u9saM82btyosWPHKisrS47jaMWKFfXWG2N03333qVu3bkpJSdGIESP0wQcfuDNsDDvZfgyFQpo5c6YGDBigDh06KCsrSzfeeKPKysrcGzhGNfV8/Kaf/exnchxHjz/+uJXZiI8Ys2XLFj311FM699xz3R4lLn355ZcaMmSIEhMT9Y9//EPvvfee5s2bp06dOrk9WlyZO3euFi5cqN///vfauXOn5s6dq4cfflhPPvmk26PFtMrKSp133nlasGBBg+sffvhhzZ8/X4sWLdKbb76pDh06aNSoUTp69KjlSWPbyfbj4cOHtW3bNs2ePVvbtm3T3/72N5WUlGjcuHEuTBrbmno+HldYWKhNmzYpKyvL0mSSDGLGoUOHTM+ePc2aNWvMpZdeau644w63R4o7M2fONJdcconbY8S9MWPGmFtuuaXesokTJ5obbrjBpYnijyRTWFhY93U4HDaZmZnmd7/7Xd2ygwcPGp/PZ55//nkXJowP396PDdm8ebORZPbu3WtnqDjU2H785JNPzH/8x3+YHTt2mO7du5vHHnvMyjwc+YgheXl5GjNmjEaMGOH2KHGrqKhIF1xwga655hplZGRo4MCBeuaZZ9weK+4MHjxYa9eu1a5duyRJb7/9tl599VWNHj3a5cni1549e1ReXl7v37ff79eFF16oN954w8XJ4l8gEJDjOOrYsaPbo8SVcDisyZMna8aMGerXr5/Vx465XyzXXi1fvlzbtm3Tli1b3B4lrn300UdauHCh7rzzTt1zzz3asmWLbr/9diUlJWnKlClujxc37r77bgWDQfXu3VsJCQmqqanRgw8+qBtuuMHt0eJWeXm5JKlr1671lnft2rVuHSJ39OhRzZw5Uz/5yU/4ZXMRmjt3rrxer26//Xbrj018xIDS0lLdcccdWrNmjZKTk90eJ66Fw2FdcMEFeuihhyRJAwcO1I4dO7Ro0SLiIwJ//vOf9dxzz2nZsmXq16+ftm/frmnTpikrK4v9iJgRCoV07bXXyhijhQsXuj1OXNm6daueeOIJbdu2TY7jWH983naJAVu3btX+/fv13e9+V16vV16vV8XFxZo/f768Xq9qamrcHjFudOvWTX379q23rE+fPvr4449dmig+zZgxQ3fffbd+/OMfa8CAAZo8ebKmT5+ugoICt0eLW5mZmZKkffv21Vu+b9++unVovuPhsXfvXq1Zs4ajHhF65ZVXtH//fuXm5ta97uzdu1d33XWXzjzzzDZ/fI58xIDhw4fr3//+d71lN998s3r37q2ZM2cqISHBpcniz5AhQ1RSUlJv2a5du9S9e3eXJopPhw8flsdT//9NEhISFA6HXZoo/vXo0UOZmZlau3atzj//fElSMBjUm2++qZ///OfuDhdnjofHBx98oPXr16tz585ujxR3Jk+efML5haNGjdLkyZN18803t/njEx8xIDU1Vf3796+3rEOHDurcufMJy3Fy06dP1+DBg/XQQw/p2muv1ebNm/X000/r6aefdnu0uDJ27Fg9+OCDys3NVb9+/fSvf/1Ljz76qG655Ra3R4tpFRUV2r17d93Xe/bs0fbt25Wenq7c3FxNmzZNv/3tb9WzZ0/16NFDs2fPVlZWliZMmODe0DHoZPuxW7duuvrqq7Vt2zatWrVKNTU1defMpKenKykpya2xY05Tz8dvR1tiYqIyMzPVq1evth/OymdqEDE+attyK1euNP379zc+n8/07t3bPP30026PFHeCwaC54447TG5urklOTjbf+c53zL333muqqqrcHi2mrV+/3kg64TZlyhRjTO3HbWfPnm26du1qfD6fGT58uCkpKXF36Bh0sv24Z8+eBtdJMuvXr3d79JjS1PPx22x+1NYxhksWAgAAezjhFAAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACs+i+Ccb25Sjgp8wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 1 \n",
    "# Visualizing Data Points\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = [4, 5, 10, 4, 3, 11, 14 , 8, 10, 12]\n",
    "y = [21, 19, 24, 17, 16, 25, 24, 22, 21, 21]\n",
    "classes = [0, 0, 1, 0, 0, 1, 1, 0, 1, 1]\n",
    "\n",
    "plt.scatter(x, y, c=classes)\n",
    "plt.show()\n",
    "\n",
    "# Step 2\n",
    "# Fit the KNN Algorithm\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "data = list(zip(x, y))\n",
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "\n",
    "knn.fit(data, classes)\n",
    "\n",
    "# Step 3\n",
    "# Classify a new data point\n",
    "\n",
    "new_x = 8\n",
    "new_y = 21\n",
    "new_point = [(new_x, new_y)]\n",
    "\n",
    "prediction = knn.predict(new_point)\n",
    "plt.scatter(x + [new_x], y + [new_y], c=classes + [prediction[0]])\n",
    "plt.text(x=new_x-1.7, y=new_y-0.7, s=f\"new point, class: {prediction[0]}\")\n",
    "plt.show()\n",
    "\n",
    "# Step 4\n",
    "# K value which changes the prediction\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "\n",
    "knn.fit(data, classes)\n",
    "\n",
    "prediction = knn.predict(new_point)\n",
    "\n",
    "plt.scatter(x + [new_x], y + [new_y], c=classes + [prediction[0]])\n",
    "plt.text(x=new_x-1.7, y=new_y-0.7, s=f\"new point, class: {prediction[0]}\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8d707a83-9229-4f75-bf5b-2768a32cfa6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5bc95e0e-ac75-41a8-94a3-c6be28e88104",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21, 24, 23, 17, 17, 21, 19, 18, 23, 24])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "07c44300-fe3a-4772-af3d-24434d709b1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 1, 1, 1, 0, 1, 0])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4cd092a0-02ce-4163-afb0-df3ead6068fe",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}