K-Means.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal is to predict the type of tumor based on gene extraction values. The data used can be found in the UCI Machine Learning Repository [here](https://archive.ics.uci.edu/ml/datasets/gene+expression+cancer+RNA-Seq#)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../Machine-Learning-Data/gene_data.csv').drop(['Unnamed: 0'], axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add true labels for comparison later\n",
    "true_labels = pd.read_csv('../Machine-Learning-Data/gene_labels.csv')[['Class']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspect the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 801 entries, 0 to 800\n",
      "Columns: 20532 entries, Unnamed: 0 to gene_20530\n",
      "dtypes: float64(20531), object(1)\n",
      "memory usage: 125.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_0</th>\n",
       "      <th>gene_1</th>\n",
       "      <th>gene_2</th>\n",
       "      <th>gene_3</th>\n",
       "      <th>gene_4</th>\n",
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       "      <th>gene_20525</th>\n",
       "      <th>gene_20526</th>\n",
       "      <th>gene_20527</th>\n",
       "      <th>gene_20528</th>\n",
       "      <th>gene_20529</th>\n",
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       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>6.256586</td>\n",
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       "      <td>10.517059</td>\n",
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       "      <td>2.094168</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>2</th>\n",
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       "      <td>4.327199</td>\n",
       "      <td>6.881787</td>\n",
       "      <td>9.870730</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.972130</td>\n",
       "      <td>0.452595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.125213</td>\n",
       "      <td>8.127123</td>\n",
       "      <td>10.908640</td>\n",
       "      <td>5.401607</td>\n",
       "      <td>9.911597</td>\n",
       "      <td>9.045255</td>\n",
       "      <td>9.788359</td>\n",
       "      <td>10.090470</td>\n",
       "      <td>1.683023</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.663618</td>\n",
       "      <td>4.507649</td>\n",
       "      <td>6.659068</td>\n",
       "      <td>10.196184</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.843375</td>\n",
       "      <td>0.434882</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>8.792959</td>\n",
       "      <td>10.141520</td>\n",
       "      <td>8.942805</td>\n",
       "      <td>9.601208</td>\n",
       "      <td>11.392682</td>\n",
       "      <td>9.694814</td>\n",
       "      <td>9.684365</td>\n",
       "      <td>3.292001</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.655741</td>\n",
       "      <td>2.821547</td>\n",
       "      <td>6.539454</td>\n",
       "      <td>9.738265</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.566967</td>\n",
       "      <td>0.360982</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.996032</td>\n",
       "      <td>8.891425</td>\n",
       "      <td>10.373790</td>\n",
       "      <td>7.181162</td>\n",
       "      <td>9.846910</td>\n",
       "      <td>11.922439</td>\n",
       "      <td>9.217749</td>\n",
       "      <td>9.461191</td>\n",
       "      <td>5.110372</td>\n",
       "      <td>0.0</td>\n",
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       "<p>5 rows × 20531 columns</p>\n",
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      ],
      "text/plain": [
       "   gene_0    gene_1    gene_2    gene_3     gene_4  gene_5    gene_6  \\\n",
       "0     0.0  2.017209  3.265527  5.478487  10.431999     0.0  7.175175   \n",
       "1     0.0  0.592732  1.588421  7.586157   9.623011     0.0  6.816049   \n",
       "2     0.0  3.511759  4.327199  6.881787   9.870730     0.0  6.972130   \n",
       "3     0.0  3.663618  4.507649  6.659068  10.196184     0.0  7.843375   \n",
       "4     0.0  2.655741  2.821547  6.539454   9.738265     0.0  6.566967   \n",
       "\n",
       "     gene_7  gene_8  gene_9  ...  gene_20521  gene_20522  gene_20523  \\\n",
       "0  0.591871     0.0     0.0  ...    4.926711    8.210257    9.723516   \n",
       "1  0.000000     0.0     0.0  ...    4.593372    7.323865    9.740931   \n",
       "2  0.452595     0.0     0.0  ...    5.125213    8.127123   10.908640   \n",
       "3  0.434882     0.0     0.0  ...    6.076566    8.792959   10.141520   \n",
       "4  0.360982     0.0     0.0  ...    5.996032    8.891425   10.373790   \n",
       "\n",
       "   gene_20524  gene_20525  gene_20526  gene_20527  gene_20528  gene_20529  \\\n",
       "0    7.220030    9.119813   12.003135    9.650743    8.921326    5.286759   \n",
       "1    6.256586    8.381612   12.674552   10.517059    9.397854    2.094168   \n",
       "2    5.401607    9.911597    9.045255    9.788359   10.090470    1.683023   \n",
       "3    8.942805    9.601208   11.392682    9.694814    9.684365    3.292001   \n",
       "4    7.181162    9.846910   11.922439    9.217749    9.461191    5.110372   \n",
       "\n",
       "   gene_20530  \n",
       "0         0.0  \n",
       "1         0.0  \n",
       "2         0.0  \n",
       "3         0.0  \n",
       "4         0.0  \n",
       "\n",
       "[5 rows x 20531 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_0</th>\n",
       "      <th>gene_1</th>\n",
       "      <th>gene_2</th>\n",
       "      <th>gene_3</th>\n",
       "      <th>gene_4</th>\n",
       "      <th>gene_5</th>\n",
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       "      <th>gene_7</th>\n",
       "      <th>gene_8</th>\n",
       "      <th>gene_9</th>\n",
       "      <th>...</th>\n",
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       "      <th>gene_20522</th>\n",
       "      <th>gene_20523</th>\n",
       "      <th>gene_20524</th>\n",
       "      <th>gene_20525</th>\n",
       "      <th>gene_20526</th>\n",
       "      <th>gene_20527</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>801.000000</td>\n",
       "      <td>801.000000</td>\n",
       "      <td>801.000000</td>\n",
       "      <td>801.000000</td>\n",
       "      <td>801.000000</td>\n",
       "      <td>801.0</td>\n",
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       "      <td>801.000000</td>\n",
       "      <td>801.000000</td>\n",
       "      <td>801.000000</td>\n",
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       "      <td>801.000000</td>\n",
       "      <td>801.000000</td>\n",
       "      <td>801.000000</td>\n",
       "      <td>801.000000</td>\n",
       "      <td>801.000000</td>\n",
       "      <td>801.000000</td>\n",
       "      <td>801.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.026642</td>\n",
       "      <td>3.010909</td>\n",
       "      <td>3.095350</td>\n",
       "      <td>6.722305</td>\n",
       "      <td>9.813612</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.405509</td>\n",
       "      <td>0.499882</td>\n",
       "      <td>0.016744</td>\n",
       "      <td>0.013428</td>\n",
       "      <td>...</td>\n",
       "      <td>5.896573</td>\n",
       "      <td>8.765891</td>\n",
       "      <td>10.056252</td>\n",
       "      <td>4.847727</td>\n",
       "      <td>9.741987</td>\n",
       "      <td>11.742228</td>\n",
       "      <td>10.155271</td>\n",
       "      <td>9.590726</td>\n",
       "      <td>5.528177</td>\n",
       "      <td>0.095411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.136850</td>\n",
       "      <td>1.200828</td>\n",
       "      <td>1.065601</td>\n",
       "      <td>0.638819</td>\n",
       "      <td>0.506537</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.108237</td>\n",
       "      <td>0.508799</td>\n",
       "      <td>0.133635</td>\n",
       "      <td>0.204722</td>\n",
       "      <td>...</td>\n",
       "      <td>0.746399</td>\n",
       "      <td>0.603176</td>\n",
       "      <td>0.379278</td>\n",
       "      <td>2.382728</td>\n",
       "      <td>0.533898</td>\n",
       "      <td>0.670371</td>\n",
       "      <td>0.580569</td>\n",
       "      <td>0.563849</td>\n",
       "      <td>2.073859</td>\n",
       "      <td>0.364529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.009284</td>\n",
       "      <td>8.435999</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.930747</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.853517</td>\n",
       "      <td>6.678368</td>\n",
       "      <td>8.669456</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.974942</td>\n",
       "      <td>9.045255</td>\n",
       "      <td>7.530141</td>\n",
       "      <td>7.864533</td>\n",
       "      <td>0.593975</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.299039</td>\n",
       "      <td>2.390365</td>\n",
       "      <td>6.303346</td>\n",
       "      <td>9.464466</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.676042</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.454926</td>\n",
       "      <td>8.383834</td>\n",
       "      <td>9.826027</td>\n",
       "      <td>3.130750</td>\n",
       "      <td>9.400747</td>\n",
       "      <td>11.315857</td>\n",
       "      <td>9.836525</td>\n",
       "      <td>9.244219</td>\n",
       "      <td>4.092385</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.143687</td>\n",
       "      <td>3.127006</td>\n",
       "      <td>6.655893</td>\n",
       "      <td>9.791599</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.450114</td>\n",
       "      <td>0.443076</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.972582</td>\n",
       "      <td>8.784144</td>\n",
       "      <td>10.066385</td>\n",
       "      <td>5.444935</td>\n",
       "      <td>9.784524</td>\n",
       "      <td>11.749802</td>\n",
       "      <td>10.191207</td>\n",
       "      <td>9.566511</td>\n",
       "      <td>5.218618</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.883484</td>\n",
       "      <td>3.802534</td>\n",
       "      <td>7.038447</td>\n",
       "      <td>10.142324</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.121984</td>\n",
       "      <td>0.789354</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.411292</td>\n",
       "      <td>9.147136</td>\n",
       "      <td>10.299025</td>\n",
       "      <td>6.637412</td>\n",
       "      <td>10.082269</td>\n",
       "      <td>12.177852</td>\n",
       "      <td>10.578561</td>\n",
       "      <td>9.917888</td>\n",
       "      <td>6.876382</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.482332</td>\n",
       "      <td>6.237034</td>\n",
       "      <td>6.063484</td>\n",
       "      <td>10.129528</td>\n",
       "      <td>11.355621</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.718190</td>\n",
       "      <td>2.779008</td>\n",
       "      <td>1.785592</td>\n",
       "      <td>4.067604</td>\n",
       "      <td>...</td>\n",
       "      <td>7.771054</td>\n",
       "      <td>11.105431</td>\n",
       "      <td>11.318243</td>\n",
       "      <td>9.207495</td>\n",
       "      <td>11.811632</td>\n",
       "      <td>13.715361</td>\n",
       "      <td>11.675653</td>\n",
       "      <td>12.813320</td>\n",
       "      <td>11.205836</td>\n",
       "      <td>5.254133</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 20531 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           gene_0      gene_1      gene_2      gene_3      gene_4  gene_5  \\\n",
       "count  801.000000  801.000000  801.000000  801.000000  801.000000   801.0   \n",
       "mean     0.026642    3.010909    3.095350    6.722305    9.813612     0.0   \n",
       "std      0.136850    1.200828    1.065601    0.638819    0.506537     0.0   \n",
       "min      0.000000    0.000000    0.000000    5.009284    8.435999     0.0   \n",
       "25%      0.000000    2.299039    2.390365    6.303346    9.464466     0.0   \n",
       "50%      0.000000    3.143687    3.127006    6.655893    9.791599     0.0   \n",
       "75%      0.000000    3.883484    3.802534    7.038447   10.142324     0.0   \n",
       "max      1.482332    6.237034    6.063484   10.129528   11.355621     0.0   \n",
       "\n",
       "           gene_6      gene_7      gene_8      gene_9  ...  gene_20521  \\\n",
       "count  801.000000  801.000000  801.000000  801.000000  ...  801.000000   \n",
       "mean     7.405509    0.499882    0.016744    0.013428  ...    5.896573   \n",
       "std      1.108237    0.508799    0.133635    0.204722  ...    0.746399   \n",
       "min      3.930747    0.000000    0.000000    0.000000  ...    2.853517   \n",
       "25%      6.676042    0.000000    0.000000    0.000000  ...    5.454926   \n",
       "50%      7.450114    0.443076    0.000000    0.000000  ...    5.972582   \n",
       "75%      8.121984    0.789354    0.000000    0.000000  ...    6.411292   \n",
       "max     10.718190    2.779008    1.785592    4.067604  ...    7.771054   \n",
       "\n",
       "       gene_20522  gene_20523  gene_20524  gene_20525  gene_20526  gene_20527  \\\n",
       "count  801.000000  801.000000  801.000000  801.000000  801.000000  801.000000   \n",
       "mean     8.765891   10.056252    4.847727    9.741987   11.742228   10.155271   \n",
       "std      0.603176    0.379278    2.382728    0.533898    0.670371    0.580569   \n",
       "min      6.678368    8.669456    0.000000    7.974942    9.045255    7.530141   \n",
       "25%      8.383834    9.826027    3.130750    9.400747   11.315857    9.836525   \n",
       "50%      8.784144   10.066385    5.444935    9.784524   11.749802   10.191207   \n",
       "75%      9.147136   10.299025    6.637412   10.082269   12.177852   10.578561   \n",
       "max     11.105431   11.318243    9.207495   11.811632   13.715361   11.675653   \n",
       "\n",
       "       gene_20528  gene_20529  gene_20530  \n",
       "count  801.000000  801.000000  801.000000  \n",
       "mean     9.590726    5.528177    0.095411  \n",
       "std      0.563849    2.073859    0.364529  \n",
       "min      7.864533    0.593975    0.000000  \n",
       "25%      9.244219    4.092385    0.000000  \n",
       "50%      9.566511    5.218618    0.000000  \n",
       "75%      9.917888    6.876382    0.000000  \n",
       "max     12.813320   11.205836    5.254133  \n",
       "\n",
       "[8 rows x 20531 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# try running pca before clustering using sci-kit learn\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "X2d = pca.fit_transform(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.1583855, 0.1050396])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the explained variance ratio\n",
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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