PCA_data_viz.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# setting up default plotting parameters\n",
    "%matplotlib inline\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [20.0, 7.0]\n",
    "plt.rcParams.update({'font.size': 22,})\n",
    "\n",
    "sns.set_palette('viridis')\n",
    "sns.set_style('white')\n",
    "sns.set_context('talk', font_scale=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(442, 10)\n"
     ]
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       "      <td>0.038076</td>\n",
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       "      <td>0.021872</td>\n",
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       "      <td>-0.043401</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.019908</td>\n",
       "      <td>-0.017646</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.001882</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.051474</td>\n",
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       "      <td>-0.008449</td>\n",
       "      <td>-0.019163</td>\n",
       "      <td>0.074412</td>\n",
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       "      <td>-0.068330</td>\n",
       "      <td>-0.092204</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.085299</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.044451</td>\n",
       "      <td>-0.005671</td>\n",
       "      <td>-0.045599</td>\n",
       "      <td>-0.034194</td>\n",
       "      <td>-0.032356</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.002864</td>\n",
       "      <td>-0.025930</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.089063</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.011595</td>\n",
       "      <td>-0.036656</td>\n",
       "      <td>0.012191</td>\n",
       "      <td>0.024991</td>\n",
       "      <td>-0.036038</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>0.022692</td>\n",
       "      <td>-0.009362</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.005383</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.036385</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>0.003935</td>\n",
       "      <td>0.015596</td>\n",
       "      <td>0.008142</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>-0.031991</td>\n",
       "      <td>-0.046641</td>\n",
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      "text/plain": [
       "          0         1         2         3         4         5         6  \\\n",
       "0  0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
       "1 -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
       "2  0.085299  0.050680  0.044451 -0.005671 -0.045599 -0.034194 -0.032356   \n",
       "3 -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
       "4  0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
       "\n",
       "          7         8         9  \n",
       "0 -0.002592  0.019908 -0.017646  \n",
       "1 -0.039493 -0.068330 -0.092204  \n",
       "2 -0.002592  0.002864 -0.025930  \n",
       "3  0.034309  0.022692 -0.009362  \n",
       "4 -0.002592 -0.031991 -0.046641  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_diabetes\n",
    "\n",
    "# load regression dataset\n",
    "diabetes, target = load_diabetes(return_X_y=True)\n",
    "diabetes = pd.DataFrame(diabetes)\n",
    "print(diabetes.shape)\n",
    "diabetes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>5</th>\n",
       "      <th>6</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.800500</td>\n",
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       "      <td>-0.732065</td>\n",
       "      <td>-0.912451</td>\n",
       "      <td>-0.054499</td>\n",
       "      <td>0.418551</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.039567</td>\n",
       "      <td>-0.938537</td>\n",
       "      <td>-1.082180</td>\n",
       "      <td>-0.553511</td>\n",
       "      <td>-0.177624</td>\n",
       "      <td>-0.402886</td>\n",
       "      <td>1.564414</td>\n",
       "      <td>-0.830301</td>\n",
       "      <td>-1.436551</td>\n",
       "      <td>-1.938479</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.793307</td>\n",
       "      <td>1.065488</td>\n",
       "      <td>0.934533</td>\n",
       "      <td>-0.119218</td>\n",
       "      <td>-0.958674</td>\n",
       "      <td>-0.718897</td>\n",
       "      <td>-0.680245</td>\n",
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       "      <td>0.060207</td>\n",
       "      <td>-0.545154</td>\n",
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       "      <th>3</th>\n",
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       "      <td>-0.938537</td>\n",
       "      <td>-0.243771</td>\n",
       "      <td>-0.770658</td>\n",
       "      <td>0.256292</td>\n",
       "      <td>0.525397</td>\n",
       "      <td>-0.757647</td>\n",
       "      <td>0.721302</td>\n",
       "      <td>0.477072</td>\n",
       "      <td>-0.196823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.113172</td>\n",
       "      <td>-0.938537</td>\n",
       "      <td>-0.764944</td>\n",
       "      <td>0.459840</td>\n",
       "      <td>0.082726</td>\n",
       "      <td>0.327890</td>\n",
       "      <td>0.171178</td>\n",
       "      <td>-0.054499</td>\n",
       "      <td>-0.672582</td>\n",
       "      <td>-0.980568</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "          0         1         2         3         4         5         6  \\\n",
       "0  0.800500  1.065488  1.297088  0.459840 -0.929746 -0.732065 -0.912451   \n",
       "1 -0.039567 -0.938537 -1.082180 -0.553511 -0.177624 -0.402886  1.564414   \n",
       "2  1.793307  1.065488  0.934533 -0.119218 -0.958674 -0.718897 -0.680245   \n",
       "3 -1.872441 -0.938537 -0.243771 -0.770658  0.256292  0.525397 -0.757647   \n",
       "4  0.113172 -0.938537 -0.764944  0.459840  0.082726  0.327890  0.171178   \n",
       "\n",
       "          7         8         9  \n",
       "0 -0.054499  0.418551 -0.370989  \n",
       "1 -0.830301 -1.436551 -1.938479  \n",
       "2 -0.054499  0.060207 -0.545154  \n",
       "3  0.721302  0.477072 -0.196823  \n",
       "4 -0.054499 -0.672582 -0.980568  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# standardize features\n",
    "diabetes = StandardScaler().fit_transform(diabetes)\n",
    "pd.DataFrame(data = diabetes).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "diabetes_pca = pca.fit_transform(diabetes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "principal_df = pd.DataFrame(data = diabetes_pca,\n",
    "                            columns=['Principal Component 1', 'Principal Component 2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Principal Component 1</th>\n",
       "      <th>Principal Component 2</th>\n",
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       "  </thead>\n",
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       "      <th>3</th>\n",
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      ],
      "text/plain": [
       "   Principal Component 1  Principal Component 2\n",
       "0               0.587208              -1.946828\n",
       "1              -2.831612               1.372085\n",
       "2               0.272148              -1.634898\n",
       "3               0.049310               0.382253\n",
       "4              -0.756451               0.811968"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "principal_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Principal Component 1</th>\n",
       "      <th>Principal Component 2</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>151.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>1.372085</td>\n",
       "      <td>75.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.272148</td>\n",
       "      <td>-1.634898</td>\n",
       "      <td>141.0</td>\n",
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       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.756451</td>\n",
       "      <td>0.811968</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   Principal Component 1  Principal Component 2  Target\n",
       "0               0.587208              -1.946828   151.0\n",
       "1              -2.831612               1.372085    75.0\n",
       "2               0.272148              -1.634898   141.0\n",
       "3               0.049310               0.382253   206.0\n",
       "4              -0.756451               0.811968   135.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target = pd.DataFrame(data=target, columns=['Target'])\n",
    "\n",
    "df = pd.concat([principal_df, target], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x110948630>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(df['Principal Component 1'], df['Principal Component 2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}