Breast_Cancer_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Principal Component Analysis\n",
    "\n",
    "Unsupervised learning algorithm, that is just a transformation of your data and attempts to find out what features explain the most variance in your data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Data\n",
    "\n",
    "Let's work with the cancer data set since it had so many features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer = load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.utils.Bunch"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(cancer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(cancer['data'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(cancer['feature_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(cancer['DESCR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.DataFrame(cancer['data'],columns=cancer['feature_names'])\n",
    "#(['DESCR', 'data', 'feature_names', 'target_names', 'target'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
       "0                 0.07871  ...         25.38          17.33           184.60   \n",
       "1                 0.05667  ...         24.99          23.41           158.80   \n",
       "2                 0.05999  ...         23.57          25.53           152.50   \n",
       "3                 0.09744  ...         14.91          26.50            98.87   \n",
       "4                 0.05883  ...         22.54          16.67           152.20   \n",
       "\n",
       "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   worst concave points  worst symmetry  worst fractal dimension  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      " 1 1 1 1 1 1 1 0 0 0 0 0 0 1]\n"
     ]
    }
   ],
   "source": [
    "y = cancer['target']\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# standardization\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PCA with Scikit Learn uses a very similar process to other preprocessing functions that come with SciKit Learn. We instantiate a PCA object, find the principal components using the fit method, then apply the rotation and dimensionality reduction by calling transform().\n",
    "\n",
    "We can also specify how many components we want to keep when creating the PCA object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(n_components=2)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.fit(X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 30)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can transform this data to its first 2 principal components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_pca = pca.transform(X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 2)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.n_components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.44272026, 0.18971182])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PCA1</th>\n",
       "      <th>PCA2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.192837</td>\n",
       "      <td>1.948583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.387802</td>\n",
       "      <td>-3.768172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.733896</td>\n",
       "      <td>-1.075174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.122953</td>\n",
       "      <td>10.275589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.935302</td>\n",
       "      <td>-1.948072</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PCA1       PCA2\n",
       "0  9.192837   1.948583\n",
       "1  2.387802  -3.768172\n",
       "2  5.733896  -1.075174\n",
       "3  7.122953  10.275589\n",
       "4  3.935302  -1.948072"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(x_pca, columns = ['PCA1', 'PCA2']).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! We've reduced 30 dimensions to just 2! Let's plot these two dimensions out!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'PCA2')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(x_pca[:,0],x_pca[:,1],c=cancer['target'],cmap='plasma')\n",
    "plt.xlabel('PCA1')\n",
    "plt.ylabel('PCA2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=x_pca\n",
    "y = cancer['target']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "# train and test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "DecisionTreeModel = DecisionTreeClassifier(criterion='entropy', random_state=45  , max_depth=4)\n",
    "\n",
    "#DecisionTreeModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 3 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=4, random_state=45)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "DecisionTreeModel.fit(X_train, y_train)  # Training input and its Target variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "DT_Pred = DecisionTreeModel.predict(X_test) # I already Know y_test  # 200 variables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>prediction</th>\n",
       "      <th>Actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>114 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     prediction  Actual\n",
       "0             0       0\n",
       "1             1       1\n",
       "2             1       1\n",
       "3             1       1\n",
       "4             1       1\n",
       "..          ...     ...\n",
       "109           0       0\n",
       "110           1       1\n",
       "111           0       0\n",
       "112           0       0\n",
       "113           1       1\n",
       "\n",
       "[114 rows x 2 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_test = pd.DataFrame({\"prediction\":DT_Pred,\"Actual\":y_test})\n",
    "pred_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1,\n",
       "       1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1,\n",
       "       0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1,\n",
       "       0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0,\n",
       "       1, 0, 0, 1])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DT_Pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0,\n",
       "       1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1,\n",
       "       1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1,\n",
       "       0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1,\n",
       "       0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0,\n",
       "       1, 0, 0, 1])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making the Confusion Matrix\n",
    "**Accuracy** is perhaps the most intuitive performance measure. It is simply the ratio of correctly predicted observations.  \n",
    "**Precision**: Precision looks at the ratio of correct positive observations   \n",
    "**Recall** : Recall is also known as sensitivity or true positive rate. It is the ratio of correctly predicted positive events   \n",
    "**F1 Score** : The F1 Score is the weighted average of Precision and recall. Therefore, this score takes both false postives and false negatives into account   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[45,  2],\n",
       "       [ 6, 61]], dtype=int64)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_cm = confusion_matrix(y_test,DT_Pred)\n",
    "dt_cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.88      0.96      0.92        47\n",
      "    positive       0.97      0.91      0.94        67\n",
      "\n",
      "    accuracy                           0.93       114\n",
      "   macro avg       0.93      0.93      0.93       114\n",
      "weighted avg       0.93      0.93      0.93       114\n",
      "\n"
     ]
    }
   ],
   "source": [
    "target_names = [\"negative\", \"positive\"]\n",
    "print(classification_report(y_test, DT_Pred, target_names=target_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# validate the data using test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Score: 0.9648351648351648\n",
      "Testing Data Score: 0.9298245614035088\n"
     ]
    }
   ],
   "source": [
    "print(f\"Training Data Score: {DecisionTreeModel.score(X_train, y_train)}\")\n",
    "print(f\"Testing Data Score: {DecisionTreeModel.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly by using these two components we can easily separate these two classes.\n",
    "\n",
    "## Interpreting the components \n",
    "\n",
    "Unfortunately, with this great power of dimensionality reduction, comes the cost of being able to easily understand what these components represent.\n",
    "\n",
    "The components correspond to combinations of the original features, the components themselves are stored as an attribute of the fitted PCA object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.21890244,  0.10372458,  0.22753729,  0.22099499,  0.14258969,\n",
       "         0.23928535,  0.25840048,  0.26085376,  0.13816696,  0.06436335,\n",
       "         0.20597878,  0.01742803,  0.21132592,  0.20286964,  0.01453145,\n",
       "         0.17039345,  0.15358979,  0.1834174 ,  0.04249842,  0.10256832,\n",
       "         0.22799663,  0.10446933,  0.23663968,  0.22487053,  0.12795256,\n",
       "         0.21009588,  0.22876753,  0.25088597,  0.12290456,  0.13178394],\n",
       "       [-0.23385713, -0.05970609, -0.21518136, -0.23107671,  0.18611302,\n",
       "         0.15189161,  0.06016536, -0.0347675 ,  0.19034877,  0.36657547,\n",
       "        -0.10555215,  0.08997968, -0.08945723, -0.15229263,  0.20443045,\n",
       "         0.2327159 ,  0.19720728,  0.13032156,  0.183848  ,  0.28009203,\n",
       "        -0.21986638, -0.0454673 , -0.19987843, -0.21935186,  0.17230435,\n",
       "         0.14359317,  0.09796411, -0.00825724,  0.14188335,  0.27533947]])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.components_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this numpy matrix array, each row represents a principal component, and each column relates back to the original features. we can visualize this relationship with a heatmap:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_comp = pd.DataFrame(pca.components_,columns=cancer['feature_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.heatmap(df_comp,cmap='plasma',)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This heatmap and the color bar basically represent the correlation between the various feature and the principal component itself.\n",
    "\n",
    "## Conclusion\n",
    "\n",
    "PCA has no concern with the class labels. It summarizes the feature set without considering the output. PCA tries to find the directions of the maximum variance in the dataset. In a high cardinality feature set, there are possibilities of duplicate features which would add redundancy to the dataset, increase the computation cost and add unneccessary model complexity. The role of PCA is to find such highly correlated or duplicate features and to come up with a new feature set where there is minimum correlation between the features or in other words feature set with maximum variance between the features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### feature imporatant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Correlation with Target Variable\n",
    "\n",
    "#  X.corr().abs()\n",
    "\n",
    "\n",
    "# plt.figure(figsize=(30,20))\n",
    "# X.corr()['target'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.DataFrame(cancer['data'],columns=cancer['feature_names'])\n",
    "y = cancer['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "# train and test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importances:\n",
      "                         importance\n",
      "mean radius                0.000000\n",
      "mean texture               0.023383\n",
      "mean perimeter             0.000000\n",
      "mean area                  0.000000\n",
      "mean smoothness            0.027877\n",
      "mean compactness           0.000000\n",
      "mean concavity             0.000000\n",
      "mean concave points        0.000000\n",
      "mean symmetry              0.024142\n",
      "mean fractal dimension     0.000000\n",
      "radius error               0.000000\n",
      "texture error              0.000000\n",
      "perimeter error            0.000000\n",
      "area error                 0.021716\n",
      "smoothness error           0.000000\n",
      "compactness error          0.000000\n",
      "concavity error            0.000000\n",
      "concave points error       0.000000\n",
      "symmetry error             0.022400\n",
      "fractal dimension error    0.000000\n",
      "worst radius               0.000000\n",
      "worst texture              0.046077\n",
      "worst perimeter            0.201043\n",
      "worst area                 0.000000\n",
      "worst smoothness           0.000000\n",
      "worst compactness          0.000000\n",
      "worst concavity            0.000000\n",
      "worst concave points       0.633361\n",
      "worst symmetry             0.000000\n",
      "worst fractal dimension    0.000000\n"
     ]
    }
   ],
   "source": [
    "DecisionTreeModel.fit(X_train, y_train)\n",
    "### Verification:\n",
    "results = pd.DataFrame(index= X_train.columns, data={'importance':DecisionTreeModel.feature_importances_})\n",
    "print('Feature importances:\\n{}'.format(results))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot feature importances\n",
    "plt.rcParams['figure.figsize'] = 20,30\n",
    "plt.title('Normalized Feature Importances')\n",
    "sns.barplot(x = DecisionTreeModel.feature_importances_, y =X_train.columns, orient = 'h')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Importance Coef</th>\n",
       "      <th>Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.633361</td>\n",
       "      <td>worst concave points</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.201043</td>\n",
       "      <td>worst perimeter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.046077</td>\n",
       "      <td>worst texture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.027877</td>\n",
       "      <td>mean smoothness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.024142</td>\n",
       "      <td>mean symmetry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.023383</td>\n",
       "      <td>mean texture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.022400</td>\n",
       "      <td>symmetry error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.021716</td>\n",
       "      <td>area error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>mean radius</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>mean perimeter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>mean area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>mean compactness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>mean concavity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>mean concave points</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>mean fractal dimension</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Importance Coef                Features\n",
       "27         0.633361    worst concave points\n",
       "22         0.201043         worst perimeter\n",
       "21         0.046077           worst texture\n",
       "4          0.027877         mean smoothness\n",
       "8          0.024142           mean symmetry\n",
       "1          0.023383            mean texture\n",
       "18         0.022400          symmetry error\n",
       "13         0.021716              area error\n",
       "0          0.000000             mean radius\n",
       "2          0.000000          mean perimeter\n",
       "3          0.000000               mean area\n",
       "5          0.000000        mean compactness\n",
       "6          0.000000          mean concavity\n",
       "7          0.000000     mean concave points\n",
       "9          0.000000  mean fractal dimension"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = pd.DataFrame({'Importance Coef' :DecisionTreeModel.feature_importances_ , 'Features' : X_train.columns})\n",
    "feature_importances.nlargest(15, 'Importance Coef')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['worst concave points',\n",
       " 'worst perimeter',\n",
       " 'worst texture',\n",
       " 'mean smoothness',\n",
       " 'mean symmetry',\n",
       " 'mean texture',\n",
       " 'symmetry error',\n",
       " 'area error']"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = feature_importances.nlargest(8, 'Importance Coef')\n",
    "\n",
    "features =[x for x in features['Features'] if x!=0]\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2= X[['worst concave points',\n",
    " 'worst perimeter',\n",
    " 'worst texture',\n",
    " 'mean smoothness',\n",
    " 'mean symmetry',\n",
    " 'mean texture',\n",
    " 'symmetry error',\n",
    " 'area error']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',\n",
       "              importance_type=None, interaction_constraints='',\n",
       "              learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "              missing=nan, monotone_constraints='()', n_estimators=100,\n",
       "              n_jobs=0, num_parallel_tree=1, predictor='auto', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, ...)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "from matplotlib import pyplot\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "\n",
    "# fit model no training data\n",
    "model = XGBClassifier()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.07469867 0.01344847 0.         0.03087107 0.01074023 0.00420738\n",
      " 0.00500765 0.21094    0.01592404 0.00047864 0.0262644  0.0041925\n",
      " 0.         0.01144557 0.01006694 0.00493801 0.         0.00053982\n",
      " 0.00487137 0.0073497  0.01344967 0.01777423 0.07667678 0.05610785\n",
      " 0.009681   0.00375145 0.02719878 0.35330954 0.00204486 0.0040214 ]\n"
     ]
    }
   ],
   "source": [
    "# feature importance\n",
    "print(model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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shnuPviKPX+Q5GcPzMaY6xmQvvmZ2Qx1jMGQPuVT/SMbzc1GHOtShDnVcW3WM6WvucLzrtrt9bn19/ZJfoKp+I8l3d/fR6fbtSV7U3XdMt38sybu6++B0+7lJHkhyc5LPdvezp/vnk3yju79rB3UfTPL3O7gfAAAAAJfnpUk+sXXnTmYS7UuyOUmaS/KtHdy+dX+2PO5SPptJwY8l+eYOHwMAAADAxV2X5MZMcpcL7CQkejSTwGbDDUm+uuX2G7e5/b+TfG9VXdfd35zeZ/PjLmU12yRaAAAAADwl/3qxG/bt4MEfS/LyqjpQVU9PcmuSj2zc2N3/keRcVb1kuuvnkzzY3U9ksmTs1dP9r03y4BUUDwAAAMB32JOGRN39lSRvTfJQktNJ3t/dn6mqB6rqR6Z3e02S36mqLyX5niS/O93/5iR3VNUjmcxGOnqV6wcAAADgKnjSC1cDAAAAcO3byXIzAAAAAK5xQiIAAAAAhEQAAAAACIkAAAAAiJAIAAAAgCTzsy6AC1XVbUmOJnlakru7+z0zLgkuUFUPJbk+yRPTXW/s7k/PsCT4f1W1nOThJD/V3f9eVbckuSvJUpI/7e6jMy0Qsu1x+kdJDiZ5fHqXY939lzMrEJJU1duT/Ox088Pd/at6KmNzkeNUT2VUqurOJK9Ksp7k3u6+a4z9dG59fX3WNbBJVT0rySeSvDDJaiYnjz/X3Y/MtDDYpKrmkjya5DndvTbremCzqvrRJH+Y5PlJnpfkv5J0kpuT/GeSD2cSwD84syLZ87Yep9OQ6AtJXtHdj822OpiYvnk5luRlmbyp+UiSe5K8M3oqI3GR4/T3ktwZPZWRqKqbk7wjyU9kMhnkkSSvTHIyI+unlpuNzy1JTnX317r78SQfzCRthDGp6b8frap/qqpfmmk1cL43JPnFJF+dbr8oyb9095enoeb9SX5mVsXB1HnHaVU9PclNSe6rqs9X1bGqcp7GrD2W5Eh3/293P5HknzMJ3/VUxmS74/Sm6KmMSHd/PMnLpn3z+kxWdT0jI+ynlpuNz/dl0ug2PJbJGxwYk2cm+Zskv5xJEv63VdXd/dezLQuS7r49Sao2ssxt++qzBy4LzrPNcXpDklNJ3pzkf5J8KMnrM5ltBDPR3V/c+H9V/WAmy3neHT2VEbnIcfrSTGZs6KmMRnc/UVXHkrwlyZ9lpOeoQqLx2ZfJNMkNc0m+NaNaYFvd/ckkn9zYrqp7k/xkEiERY6SvMnrd/W9Jfnpju6reneS18YaGEaiqH8pkGcSvJFnLZDbRBj2VUdh8nHZ3R09lhLr77VX1zkyWmT0vIzxHNeVufB5NcuOm7Rvy7SUTMApVdbCqXr5p11y+fQFrGBt9ldGrqh+uqls37dJXGYWqekkms4d/vbvfFz2VEdp6nOqpjE1VPb+qXpAk3f2NJH+RyWy30fVTM4nG52NJfrOqDmRyJf5bk9wx25LgAs9IcmdVvTiT5Wa/kORNM60ILu7TSaqqnpvky0luS3LfbEuCC8wlubuqTiX5eia/+98325LY66rq+5P8VZJXd/ep6W49lVG5yHGqpzI2P5DkWFUdzGT20OEkf5DkXWPrp2YSjUx3fyXJW5M8lOR0kvd392dmWhRs0d0fymQ67z8m+VyS+6ZL0GB0uvtcktcl+fNMPkniS5l8KACMRnd/PslvJ/mHTI7T0939J7OtCvKWJItJ7qqq01V1OpN++rroqYzHdsfpi6OnMiLd/UDOf//0cHd/ICPsp3Pr6+tPfi8AAAAArmlmEgEAAAAgJAIAAABASAQAAABAhEQAAAAAREgEAAAAQIREAAAAAERIBAAAAECS/wPt2sagOJcmlAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "pyplot.bar(range(len(model.feature_importances_)), model.feature_importances_)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Importance Coef</th>\n",
       "      <th>Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.353310</td>\n",
       "      <td>worst concave points</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.210940</td>\n",
       "      <td>mean concave points</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.076677</td>\n",
       "      <td>worst perimeter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.074699</td>\n",
       "      <td>mean radius</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.056108</td>\n",
       "      <td>worst area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.030871</td>\n",
       "      <td>mean area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.027199</td>\n",
       "      <td>worst concavity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.026264</td>\n",
       "      <td>radius error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.017774</td>\n",
       "      <td>worst texture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.015924</td>\n",
       "      <td>mean symmetry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.013450</td>\n",
       "      <td>worst radius</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.013448</td>\n",
       "      <td>mean texture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.011446</td>\n",
       "      <td>area error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.010740</td>\n",
       "      <td>mean smoothness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.010067</td>\n",
       "      <td>smoothness error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.009681</td>\n",
       "      <td>worst smoothness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.007350</td>\n",
       "      <td>fractal dimension error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.005008</td>\n",
       "      <td>mean concavity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.004938</td>\n",
       "      <td>compactness error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.004871</td>\n",
       "      <td>symmetry error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.004207</td>\n",
       "      <td>mean compactness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.004192</td>\n",
       "      <td>texture error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.004021</td>\n",
       "      <td>worst fractal dimension</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.003751</td>\n",
       "      <td>worst compactness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.002045</td>\n",
       "      <td>worst symmetry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.000540</td>\n",
       "      <td>concave points error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000479</td>\n",
       "      <td>mean fractal dimension</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>concavity error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>perimeter error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>mean perimeter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Importance Coef                 Features\n",
       "27         0.353310     worst concave points\n",
       "7          0.210940      mean concave points\n",
       "22         0.076677          worst perimeter\n",
       "0          0.074699              mean radius\n",
       "23         0.056108               worst area\n",
       "3          0.030871                mean area\n",
       "26         0.027199          worst concavity\n",
       "10         0.026264             radius error\n",
       "21         0.017774            worst texture\n",
       "8          0.015924            mean symmetry\n",
       "20         0.013450             worst radius\n",
       "1          0.013448             mean texture\n",
       "13         0.011446               area error\n",
       "4          0.010740          mean smoothness\n",
       "14         0.010067         smoothness error\n",
       "24         0.009681         worst smoothness\n",
       "19         0.007350  fractal dimension error\n",
       "6          0.005008           mean concavity\n",
       "15         0.004938        compactness error\n",
       "18         0.004871           symmetry error\n",
       "5          0.004207         mean compactness\n",
       "11         0.004192            texture error\n",
       "29         0.004021  worst fractal dimension\n",
       "25         0.003751        worst compactness\n",
       "28         0.002045           worst symmetry\n",
       "17         0.000540     concave points error\n",
       "9          0.000479   mean fractal dimension\n",
       "16         0.000000          concavity error\n",
       "12         0.000000          perimeter error\n",
       "2          0.000000           mean perimeter"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = pd.DataFrame({'Importance Coef' :model.feature_importances_ , 'Features' : X_train.columns})\n",
    "feature_importances.nlargest(30, 'Importance Coef')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = feature_importances.nlargest(15, 'Importance Coef')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Importance Coef</th>\n",
       "      <th>Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.633361</td>\n",
       "      <td>worst concave points</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.201043</td>\n",
       "      <td>worst perimeter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.046077</td>\n",
       "      <td>worst texture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.027877</td>\n",
       "      <td>mean smoothness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.024142</td>\n",
       "      <td>mean symmetry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.023383</td>\n",
       "      <td>mean texture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.022400</td>\n",
       "      <td>symmetry error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.021716</td>\n",
       "      <td>area error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>mean radius</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>mean perimeter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>mean area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>mean compactness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>mean concavity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>mean concave points</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>mean fractal dimension</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Importance Coef                Features\n",
       "27         0.633361    worst concave points\n",
       "22         0.201043         worst perimeter\n",
       "21         0.046077           worst texture\n",
       "4          0.027877         mean smoothness\n",
       "8          0.024142           mean symmetry\n",
       "1          0.023383            mean texture\n",
       "18         0.022400          symmetry error\n",
       "13         0.021716              area error\n",
       "0          0.000000             mean radius\n",
       "2          0.000000          mean perimeter\n",
       "3          0.000000               mean area\n",
       "5          0.000000        mean compactness\n",
       "6          0.000000          mean concavity\n",
       "7          0.000000     mean concave points\n",
       "9          0.000000  mean fractal dimension"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "EOF while scanning triple-quoted string literal (2839279580.py, line 15)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"C:\\Users\\sohni\\AppData\\Local\\Temp\\ipykernel_11272\\2839279580.py\"\u001b[1;36m, line \u001b[1;32m15\u001b[0m\n\u001b[1;33m    print(\"Feature Ranking: %s\") % fit.ranking_\u001b[0m\n\u001b[1;37m                                               \n^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m EOF while scanning triple-quoted string literal\n"
     ]
    }
   ],
   "source": [
    "'''# Feature Extraction with RFE\n",
    "from pandas import read_csv\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "# load data\n",
    "array = Bchurn.values\n",
    "X = array[:,0:8]\n",
    "Y = array[:,8]\n",
    "# feature extraction\n",
    "model = LogisticRegression()\n",
    "rfe = RFE(model, 3)\n",
    "fit = rfe.fit(X, Y)\n",
    "print ()\"Num Features: %d\" % fit.n_features_\n",
    "print(\"Selected Features: %s\") % fit.support_)\n",
    "print(\"Feature Ranking: %s\") % fit.ranking_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kpca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KernelPCA(kernel='rbf', n_components=2)"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import KernelPCA\n",
    "kpca = KernelPCA(n_components = 2, kernel = 'rbf')\n",
    "kpca.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [],
   "source": [
    "kpca = kpca.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.00290973, -0.00305362],\n",
       "       [-0.00291059, -0.00305461],\n",
       "       [-0.00290973, -0.00305362],\n",
       "       ...,\n",
       "       [-0.00290973, -0.00305362],\n",
       "       [-0.00290973, -0.00305362],\n",
       "       [-0.00290973, -0.00305363]])"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kpca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>principal component 1</th>\n",
       "      <th>principal component 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.002910</td>\n",
       "      <td>-0.003054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.002911</td>\n",
       "      <td>-0.003055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.002910</td>\n",
       "      <td>-0.003054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.002910</td>\n",
       "      <td>-0.003054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.002910</td>\n",
       "      <td>-0.003054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   principal component 1  principal component 2\n",
       "0              -0.002910              -0.003054\n",
       "1              -0.002911              -0.003055\n",
       "2              -0.002910              -0.003054\n",
       "3              -0.002910              -0.003054\n",
       "4              -0.002910              -0.003054"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(kpca, columns = ['principal component 1', 'principal component 2']).head()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'principal component 2')"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(kpca[:,0],kpca[:,1],c=cancer['target'],cmap='plasma')\n",
    "plt.xlabel('principal component 1')\n",
    "plt.ylabel('principal component 2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
    "\n",
    "lda = LDA(n_components=1)\n",
    "X_lda= lda.fit_transform(X,y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>LDA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.323927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.319108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.747425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.048549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.281158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        LDA\n",
       "0  3.323927\n",
       "1  2.319108\n",
       "2  3.747425\n",
       "3  4.048549\n",
       "4  2.281158"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda= pd.DataFrame(X_lda, columns=['LDA']).head()\n",
    "lda"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LDA tries to reduce the dimensionality by taking into consideration the information that discriminates the output classes. LDA tries to find the decision boundary around each cluster of class.\n",
    "\n",
    "It projects the data points to new dimension in a way that the clusters are as seperate from each other as possible and individual elements within a class are as close to the centroid as possible.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mlen",
   "language": "python",
   "name": "mlen"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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