ML7(PCA).ipynb

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     "data": {
      "text/plain": "['DESCR',\n 'data',\n 'data_module',\n 'feature_names',\n 'filename',\n 'frame',\n 'target',\n 'target_names']"
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import  matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix\n",
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "dir(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n0                5.1               3.5                1.4               0.2\n1                4.9               3.0                1.4               0.2\n2                4.7               3.2                1.3               0.2\n3                4.6               3.1                1.5               0.2\n4                5.0               3.6                1.4               0.2",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>sepal length (cm)</th>\n      <th>sepal width (cm)</th>\n      <th>petal length (cm)</th>\n      <th>petal width (cm)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5.1</td>\n      <td>3.5</td>\n      <td>1.4</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.9</td>\n      <td>3.0</td>\n      <td>1.4</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.7</td>\n      <td>3.2</td>\n      <td>1.3</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.6</td>\n      <td>3.1</td>\n      <td>1.5</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>3.6</td>\n      <td>1.4</td>\n      <td>0.2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df = pd.DataFrame(iris.data,columns=iris.feature_names)\n",
    "df.head()"
   ],
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     "name": "#%%\n"
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   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n0                5.1               3.5                1.4               0.2   \n1                4.9               3.0                1.4               0.2   \n2                4.7               3.2                1.3               0.2   \n3                4.6               3.1                1.5               0.2   \n4                5.0               3.6                1.4               0.2   \n\n   target  \n0       0  \n1       0  \n2       0  \n3       0  \n4       0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>sepal length (cm)</th>\n      <th>sepal width (cm)</th>\n      <th>petal length (cm)</th>\n      <th>petal width (cm)</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5.1</td>\n      <td>3.5</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.9</td>\n      <td>3.0</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.7</td>\n      <td>3.2</td>\n      <td>1.3</td>\n      <td>0.2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.6</td>\n      <td>3.1</td>\n      <td>1.5</td>\n      <td>0.2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>3.6</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 4,
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   ],
   "source": [
    "df['target'] = iris.target\n",
    "df.head()"
   ],
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     "name": "#%%\n"
    }
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   "cell_type": "code",
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    {
     "data": {
      "text/plain": "(150, 4)"
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "X = df.drop(['target'], axis=1 )\n",
    "y = df.target\n",
    "X.shape"
   ],
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   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "(150, 2)"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "X_pca = pca.fit_transform(X)\n",
    "X_pca.shape"
   ],
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