MLCode.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#import maplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Q4</th>\n",
       "      <th>Q5</th>\n",
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       "      <th>Q47</th>\n",
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       "      <th>Q49</th>\n",
       "      <th>Q50</th>\n",
       "      <th>Q51</th>\n",
       "      <th>Q52</th>\n",
       "      <th>Q53</th>\n",
       "      <th>Q54</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>136.000000</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>136.000000</td>\n",
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       "      <td>136.000000</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>136.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.816176</td>\n",
       "      <td>1.647059</td>\n",
       "      <td>1.764706</td>\n",
       "      <td>1.507353</td>\n",
       "      <td>1.566176</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.522059</td>\n",
       "      <td>1.455882</td>\n",
       "      <td>1.492647</td>\n",
       "      <td>1.580882</td>\n",
       "      <td>...</td>\n",
       "      <td>2.485294</td>\n",
       "      <td>2.588235</td>\n",
       "      <td>2.294118</td>\n",
       "      <td>2.786765</td>\n",
       "      <td>2.389706</td>\n",
       "      <td>2.455882</td>\n",
       "      <td>2.529412</td>\n",
       "      <td>2.536765</td>\n",
       "      <td>2.205882</td>\n",
       "      <td>2.036765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.656423</td>\n",
       "      <td>1.508148</td>\n",
       "      <td>1.471886</td>\n",
       "      <td>1.515336</td>\n",
       "      <td>1.654185</td>\n",
       "      <td>0.903636</td>\n",
       "      <td>0.965838</td>\n",
       "      <td>1.572296</td>\n",
       "      <td>1.586967</td>\n",
       "      <td>1.443017</td>\n",
       "      <td>...</td>\n",
       "      <td>1.505473</td>\n",
       "      <td>1.390287</td>\n",
       "      <td>1.610694</td>\n",
       "      <td>1.091435</td>\n",
       "      <td>1.525938</td>\n",
       "      <td>1.403000</td>\n",
       "      <td>1.240915</td>\n",
       "      <td>1.475268</td>\n",
       "      <td>1.496255</td>\n",
       "      <td>1.648380</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
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       "  </tbody>\n",
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       "<p>8 rows × 54 columns</p>\n",
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      "text/plain": [
       "               Q1          Q2          Q3          Q4          Q5          Q6  \\\n",
       "count  136.000000  136.000000  136.000000  136.000000  136.000000  136.000000   \n",
       "mean     1.816176    1.647059    1.764706    1.507353    1.566176    0.705882   \n",
       "std      1.656423    1.508148    1.471886    1.515336    1.654185    0.903636   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "50%      2.000000    1.500000    2.000000    1.000000    1.000000    0.000000   \n",
       "75%      3.000000    3.000000    3.000000    3.000000    3.000000    1.000000   \n",
       "max      4.000000    4.000000    4.000000    4.000000    4.000000    4.000000   \n",
       "\n",
       "               Q7          Q8          Q9         Q10  ...         Q45  \\\n",
       "count  136.000000  136.000000  136.000000  136.000000  ...  136.000000   \n",
       "mean     0.522059    1.455882    1.492647    1.580882  ...    2.485294   \n",
       "std      0.965838    1.572296    1.586967    1.443017  ...    1.505473   \n",
       "min      0.000000    0.000000    0.000000    0.000000  ...    0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000  ...    1.000000   \n",
       "50%      0.000000    1.000000    1.000000    2.000000  ...    3.000000   \n",
       "75%      1.000000    3.000000    3.000000    3.000000  ...    4.000000   \n",
       "max      4.000000    4.000000    4.000000    4.000000  ...    4.000000   \n",
       "\n",
       "              Q46         Q47         Q48         Q49         Q50         Q51  \\\n",
       "count  136.000000  136.000000  136.000000  136.000000  136.000000  136.000000   \n",
       "mean     2.588235    2.294118    2.786765    2.389706    2.455882    2.529412   \n",
       "std      1.390287    1.610694    1.091435    1.525938    1.403000    1.240915   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      2.000000    1.000000    2.000000    1.000000    1.000000    2.000000   \n",
       "50%      3.000000    2.500000    3.000000    3.000000    3.000000    3.000000   \n",
       "75%      4.000000    4.000000    4.000000    4.000000    4.000000    4.000000   \n",
       "max      4.000000    4.000000    4.000000    4.000000    4.000000    4.000000   \n",
       "\n",
       "              Q52         Q53         Q54  \n",
       "count  136.000000  136.000000  136.000000  \n",
       "mean     2.536765    2.205882    2.036765  \n",
       "std      1.475268    1.496255    1.648380  \n",
       "min      0.000000    0.000000    0.000000  \n",
       "25%      1.000000    1.000000    0.000000  \n",
       "50%      3.000000    2.000000    2.000000  \n",
       "75%      4.000000    4.000000    4.000000  \n",
       "max      4.000000    4.000000    4.000000  \n",
       "\n",
       "[8 rows x 54 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"divorce_data.csv\", sep=';')\n",
    "X = train.drop('Divorce', 1)\n",
    "y = train['Divorce']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "reference = pd.read_csv(\"reference.tsv\", sep=\"|\")\n",
    "\n",
    "X_train.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8', 'Q9', 'Q10', 'Q11',\n",
      "       'Q12', 'Q13', 'Q14', 'Q15', 'Q16', 'Q17', 'Q18', 'Q19', 'Q20', 'Q21',\n",
      "       'Q22', 'Q23', 'Q24', 'Q25', 'Q26', 'Q27', 'Q28', 'Q29', 'Q30', 'Q31',\n",
      "       'Q32', 'Q33', 'Q34', 'Q35', 'Q36', 'Q37', 'Q38', 'Q39', 'Q40', 'Q41',\n",
      "       'Q42', 'Q43', 'Q44', 'Q45', 'Q46', 'Q47', 'Q48', 'Q49', 'Q50', 'Q51',\n",
      "       'Q52', 'Q53', 'Q54'],\n",
      "      dtype='object')\n",
      "119    0\n",
      "92     0\n",
      "98     0\n",
      "152    0\n",
      "155    0\n",
      "      ..\n",
      "9      1\n",
      "103    0\n",
      "67     1\n",
      "117    0\n",
      "47     1\n",
      "Name: Divorce, Length: 136, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(X_train.columns)\n",
    "print(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "[[2 2 4 ... 2 1 1]\n",
      " [4 4 4 ... 2 2 1]\n",
      " [2 2 2 ... 2 2 1]\n",
      " ...\n",
      " [1 1 0 ... 0 0 0]\n",
      " [0 0 0 ... 3 1 0]\n",
      " [0 0 0 ... 3 1 0]]\n"
     ]
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       "      <td>0.25</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>...</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>170 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0     1     2     3     4     5     6     7     8     9   ...    45  \\\n",
       "0    0.50  0.50  1.00  0.25  0.00  0.00  0.00  0.00  0.00  0.00  ...  0.50   \n",
       "1    1.00  1.00  1.00  1.00  1.00  0.00  0.00  1.00  1.00  1.00  ...  0.50   \n",
       "2    0.50  0.50  0.50  0.50  0.25  0.75  0.50  0.25  0.25  0.50  ...  0.75   \n",
       "3    0.75  0.50  0.75  0.50  0.75  0.75  0.75  0.75  0.75  0.75  ...  0.50   \n",
       "4    0.50  0.50  0.25  0.25  0.25  0.25  0.00  0.00  0.00  0.00  ...  0.50   \n",
       "..    ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   ...   \n",
       "165  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  ...  0.25   \n",
       "166  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  ...  1.00   \n",
       "167  0.25  0.25  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.25  ...  0.75   \n",
       "168  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  ...  0.75   \n",
       "169  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.25  0.00  0.00  ...  0.75   \n",
       "\n",
       "       46    47    48    49    50    51    52    53   54  \n",
       "0    0.25  0.75  0.75  0.75  0.50  0.75  0.50  0.25  1.0  \n",
       "1    0.50  0.75  1.00  1.00  1.00  1.00  0.50  0.50  1.0  \n",
       "2    0.50  0.75  0.25  0.25  0.25  0.50  0.50  0.50  1.0  \n",
       "3    0.50  0.75  0.75  0.75  0.75  0.50  0.50  0.50  1.0  \n",
       "4    0.25  0.50  0.75  0.50  0.50  0.50  0.25  0.00  1.0  \n",
       "..    ...   ...   ...   ...   ...   ...   ...   ...  ...  \n",
       "165  0.00  1.00  0.25  0.25  1.00  0.50  0.50  0.50  0.0  \n",
       "166  0.25  0.50  0.50  0.50  0.50  0.75  0.50  0.50  0.0  \n",
       "167  0.00  0.50  0.00  0.25  0.25  0.75  0.00  0.00  0.0  \n",
       "168  0.75  0.50  0.50  0.75  0.50  1.00  0.75  0.25  0.0  \n",
       "169  1.00  1.00  0.00  0.25  0.75  0.75  0.75  0.25  0.0  \n",
       "\n",
       "[170 rows x 55 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_corr = train.values\n",
    "print(x_corr)\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "x_scaled = min_max_scaler.fit_transform(x_corr)\n",
    "train = pd.DataFrame(x_scaled)\n",
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8fa8ef9040>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "auto_train = train\n",
    "\n",
    "corr = auto_train.corr()\n",
    "sns.heatmap(corr, xticklabels = corr.columns, yticklabels=corr.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39\n"
     ]
    }
   ],
   "source": [
    "maximum_ind = 0\n",
    "for i in range(len(corr[54]) - 1):\n",
    "    if corr[54][i] > corr[54][maximum_ind]:\n",
    "        maximum_ind = i\n",
    "print(maximum_ind)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "atribute_id                                                   40\n",
      "description    We're just starting a discussion before I know...\n",
      "Name: 39, dtype: object\n",
      "0      0.75\n",
      "1      1.00\n",
      "2      0.75\n",
      "3      1.00\n",
      "4      0.25\n",
      "       ... \n",
      "165    0.00\n",
      "166    0.25\n",
      "167    0.50\n",
      "168    0.25\n",
      "169    0.00\n",
      "Name: 39, Length: 170, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(reference.loc[39,:])\n",
    "\n",
    "print(train[39])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# __PCA to reduce the dimensionality__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "x = train.loc[:, 0:53].values\n",
    "y = train.loc[:,54]\n",
    "pca = PCA(n_components=2)\n",
    "principalComponents = pca.fit_transform(x)\n",
    "principal_X = pd.DataFrame(data = principalComponents, columns = ['pca1', 'pca2'])\n",
    "finalDf = pd.concat([principal_X, y], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>pca1</th>\n",
       "      <th>pca2</th>\n",
       "      <th>54</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.258708</td>\n",
       "      <td>-0.059397</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.520622</td>\n",
       "      <td>0.967748</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.528058</td>\n",
       "      <td>0.781615</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.191263</td>\n",
       "      <td>0.966155</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.512216</td>\n",
       "      <td>0.175415</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>-1.958550</td>\n",
       "      <td>-0.089196</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>-2.063431</td>\n",
       "      <td>-0.855094</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>-2.030445</td>\n",
       "      <td>-0.048272</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>-2.041899</td>\n",
       "      <td>-0.782544</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>-2.079919</td>\n",
       "      <td>-0.526879</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>170 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         pca1      pca2   54\n",
       "0   -1.258708 -0.059397  1.0\n",
       "1    1.520622  0.967748  1.0\n",
       "2    0.528058  0.781615  1.0\n",
       "3    1.191263  0.966155  1.0\n",
       "4   -1.512216  0.175415  1.0\n",
       "..        ...       ...  ...\n",
       "165 -1.958550 -0.089196  0.0\n",
       "166 -2.063431 -0.855094  0.0\n",
       "167 -2.030445 -0.048272  0.0\n",
       "168 -2.041899 -0.782544  0.0\n",
       "169 -2.079919 -0.526879  0.0\n",
       "\n",
       "[170 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (8,8))\n",
    "ax = fig.add_subplot(1,1,1) \n",
    "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
    "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
    "ax.set_title('2 component PCA', fontsize = 20)\n",
    "targets = [0, 1]\n",
    "colors = ['r', 'g']\n",
    "for target, color in zip(targets,colors):\n",
    "    indicesToKeep = finalDf[54] == target\n",
    "    ax.scatter(finalDf.loc[indicesToKeep, 'pca1']\n",
    "               , finalDf.loc[indicesToKeep, 'pca2']\n",
    "               , c = color\n",
    "               , s = 50)\n",
    "ax.legend(targets)\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.7722181 , 0.03526505])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict using Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "pca = PCA(n_components=1)\n",
    "X_train = pca.fit_transform(X_train)\n",
    "X_test = pca.transform(X_test)\n",
    "\n",
    "classifier = RandomForestClassifier(max_depth=2, random_state=0)\n",
    "classifier.fit(X_train, y_train)\n",
    "\n",
    "# Predicting the test results\n",
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[16  0]\n",
      " [ 1 17]]\n",
      "0.9705882352941176\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)\n",
    "print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict using Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logreg = LogisticRegression(random_state=16)\n",
    "logreg.fit(X_train, y_train)\n",
    "y_pred = logreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[16  0]\n",
      " [ 2 16]]\n",
      "0.9411764705882353\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "cm = metrics.confusion_matrix(y_test, y_pred)\n",
    "print(cm)\n",
    "print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict using Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[16  0]\n",
      " [ 2 16]]\n",
      "0.9411764705882353\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "gnb = GaussianNB()\n",
    "gaus = gnb.fit(X_train, y_train)\n",
    "y_pred = gnb.predict(X_test)\n",
    "\n",
    "cm = metrics.confusion_matrix(y_test, y_pred)\n",
    "print(cm)\n",
    "print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.5 64-bit",
   "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": "3.9.5"
  },
  "vscode": {
   "interpreter": {
    "hash": "721a568cbb9d47a59aa03adb3871a100460bcd452a398fc506f0d6a5b8f0f113"
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 },
 "nbformat": 4,
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}