Digits Dataset with SVM(0.9936170212765958).ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bridal-third",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.datasets import load_digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "downtown-progress",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR', 'data', 'feature_names', 'frame', 'images', 'target', 'target_names']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits=load_digits()\n",
    "dir(digits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "nominated-surveillance",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(digits.data,columns=digits.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "split-morgan",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"target\"]=digits.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "durable-midnight",
   "metadata": {},
   "outputs": [
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     "execution_count": 56,
     "metadata": {},
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   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "outer-christmas",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.iloc[:,:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "skilled-format",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "rolled-supply",
   "metadata": {},
   "outputs": [
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     "data": {
      "text/plain": [
       "0.9936170212765958"
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     "execution_count": 52,
     "metadata": {},
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   "source": [
    "X_t,X_v,y_t,y_v=train_test_split(X,y,test_size=0.261,random_state=42)\n",
    "\n",
    "model=SVC(kernel='poly')\n",
    "\n",
    "model.fit(X_t,y_t)\n",
    "\n",
    "model.score(X_v,y_v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "loving-capital",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([digits.data[1796]])"
   ]
  }
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