SVC.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2bf27470",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "digits = load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "430e707c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR', 'data', 'feature_names', 'frame', 'images', 'target', 'target_names']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(digits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1dce133a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "918735c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(digits.data, columns=digits.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6bf4994c",
   "metadata": {},
   "outputs": [
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     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "350124f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['target'] = digits.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fcbc7a44",
   "metadata": {},
   "outputs": [
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     "execution_count": 25,
     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "38893ca5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df[df['target'] == 1]\n",
    "df2 = df[df['target'] == 2]\n",
    "df3 = df[df['target'] == 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "37dcc5f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2183e578070>"
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     "execution_count": 50,
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     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df1['pixel_0_4'], df1['pixel_0_5'], marker='+', color = \"green\")\n",
    "plt.scatter(df2['pixel_0_4'], df2['pixel_0_5'], marker='.', color = \"blue\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c125ef4",
   "metadata": {},
   "source": [
    "#### I cannot visualize this. Long and boring. Let's staraight goo to SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f66a19bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop(['target'], axis = 1)\n",
    "y = df.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "2804fd85",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "87f6da74",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "model = SVC(C = 100, gamma=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "d0ad92c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=100, gamma=0.001)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "ef6fe902",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(xtest, ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60b74a41",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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