RachelMaeLademora_DPrepHDip_CA1.ipynb

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   "cell_type": "markdown",
   "id": "a0d3391b",
   "metadata": {},
   "source": [
    "In this CA1 assessment, I need to perform and determine the characteristics of the APS failure dataset, then clean the data before performing the EDA and the PCA. And reduce the dimensionality of the number of features of the target variable in my dataset. Lastly, I will do the machine learning testing of the model.    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91ae5c18",
   "metadata": {},
   "source": [
    "# 1. Import the data \n",
    "\n",
    "I import the libraries.\n",
    "\n",
    "The libraries are vital in reading the code to properly perform the data analysis function. It will help data analytic students like me abstain from code restating. Without these libraries, it will cause errors in data analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "98ee8e7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline \n",
    "import numpy as np \n",
    "from sklearn.decomposition import PCA\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9cbb80ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv (\"aps_failure_set.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b2d60dd",
   "metadata": {},
   "source": [
    "df means dataframe, and csv means the type of dataset, aps_failure_set. csv: This is the dataset that I used to base every step that I will do in every data-related task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0c9219f9",
   "metadata": {},
   "outputs": [
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       "</table>\n",
       "<p>5 rows × 171 columns</p>\n",
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       "  class  aa_000 ab_000      ac_000 ad_000 ae_000 af_000 ag_000 ag_001 ag_002  \\\n",
       "0   neg   76698     na  2130706438    280      0      0      0      0      0   \n",
       "1   neg   33058     na           0     na      0      0      0      0      0   \n",
       "2   neg   41040     na         228    100      0      0      0      0      0   \n",
       "3   neg      12      0          70     66      0     10      0      0      0   \n",
       "4   neg   60874     na        1368    458      0      0      0      0      0   \n",
       "\n",
       "   ...   ee_002  ee_003  ee_004  ee_005  ee_006  ee_007  ee_008 ee_009 ef_000  \\\n",
       "0  ...  1240520  493384  721044  469792  339156  157956   73224      0      0   \n",
       "1  ...   421400  178064  293306  245416  133654   81140   97576   1500      0   \n",
       "2  ...   277378  159812  423992  409564  320746  158022   95128    514      0   \n",
       "3  ...      240      46      58      44      10       0       0      0      4   \n",
       "4  ...   622012  229790  405298  347188  286954  311560  433954   1218      0   \n",
       "\n",
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       "0      0  \n",
       "1      0  \n",
       "2      0  \n",
       "3     32  \n",
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       "\n",
       "[5 rows x 171 columns]"
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     },
     "execution_count": 3,
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39160b6c",
   "metadata": {},
   "source": [
    "This is the head of the dataframe or dataset that shows 5 rows and 171 columns. I used this to show the inside of the dataset, as this helped me better understand the analysis of the dataset. And the class would be my target in the dataset inside the head."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c91d2277",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 60000 entries, 0 to 59999\n",
      "Columns: 171 entries, class to eg_000\n",
      "dtypes: int64(1), object(170)\n",
      "memory usage: 78.3+ MB\n"
     ]
    }
   ],
   "source": [
    " df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8487cd7a",
   "metadata": {},
   "source": [
    "I used this to identify the data types of attributes in the dataset, with the object being 170 and the dtypes being.(AlmaBetter, n.d.)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ba7ef259",
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   "outputs": [
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       "             aa_000\n",
       "count  6.000000e+04\n",
       "mean   5.933650e+04\n",
       "std    1.454301e+05\n",
       "min    0.000000e+00\n",
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   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae94aeba",
   "metadata": {},
   "source": [
    "I used describe to show the attribute of the APs failure dataset and to see the number of means. (pandas.pydata.org, n.d.)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "73a28898",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class     0\n",
       "aa_000    0\n",
       "ab_000    0\n",
       "ac_000    0\n",
       "ad_000    0\n",
       "         ..\n",
       "ee_007    0\n",
       "ee_008    0\n",
       "ee_009    0\n",
       "ef_000    0\n",
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       "Length: 171, dtype: int64"
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e0f3298",
   "metadata": {},
   "source": [
    "This means an outline of the missing values. (pandas.pydata.org, n.d.)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de028398",
   "metadata": {},
   "source": [
    "## 2. Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a71d2bc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "missing_value_formats = [\"n.a.\",\"?\",\"NA\",\"n/a\", \"na\", \"--\"]\n",
    "df = pd.read_csv(\"aps_failure_set.csv\", na_values = missing_value_formats)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c68c3998",
   "metadata": {},
   "source": [
    "I used this because it is important to preprocess data for cleaning and to determine the NAN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6b840aa5",
   "metadata": {},
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       "      <td>622012.0</td>\n",
       "      <td>229790.0</td>\n",
       "      <td>405298.0</td>\n",
       "      <td>347188.0</td>\n",
       "      <td>286954.0</td>\n",
       "      <td>311560.0</td>\n",
       "      <td>433954.0</td>\n",
       "      <td>1218.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 171 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  class  aa_000  ab_000        ac_000  ad_000  ae_000  af_000  ag_000  ag_001  \\\n",
       "0   neg   76698     NaN  2.130706e+09   280.0     0.0     0.0     0.0     0.0   \n",
       "1   neg   33058     NaN  0.000000e+00     NaN     0.0     0.0     0.0     0.0   \n",
       "2   neg   41040     NaN  2.280000e+02   100.0     0.0     0.0     0.0     0.0   \n",
       "3   neg      12     0.0  7.000000e+01    66.0     0.0    10.0     0.0     0.0   \n",
       "4   neg   60874     NaN  1.368000e+03   458.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "   ag_002  ...     ee_002    ee_003    ee_004    ee_005    ee_006    ee_007  \\\n",
       "0     0.0  ...  1240520.0  493384.0  721044.0  469792.0  339156.0  157956.0   \n",
       "1     0.0  ...   421400.0  178064.0  293306.0  245416.0  133654.0   81140.0   \n",
       "2     0.0  ...   277378.0  159812.0  423992.0  409564.0  320746.0  158022.0   \n",
       "3     0.0  ...      240.0      46.0      58.0      44.0      10.0       0.0   \n",
       "4     0.0  ...   622012.0  229790.0  405298.0  347188.0  286954.0  311560.0   \n",
       "\n",
       "     ee_008  ee_009  ef_000  eg_000  \n",
       "0   73224.0     0.0     0.0     0.0  \n",
       "1   97576.0  1500.0     0.0     0.0  \n",
       "2   95128.0   514.0     0.0     0.0  \n",
       "3       0.0     0.0     4.0    32.0  \n",
       "4  433954.0  1218.0     0.0     0.0  \n",
       "\n",
       "[5 rows x 171 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33bd9885",
   "metadata": {},
   "source": [
    "I used this because it is important to visualize the head of the NAN values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6d65bebf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bae3a38",
   "metadata": {},
   "source": [
    "I use fillna to displace the NAN to O values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bdde3db7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>aa_000</th>\n",
       "      <th>ab_000</th>\n",
       "      <th>ac_000</th>\n",
       "      <th>ad_000</th>\n",
       "      <th>ae_000</th>\n",
       "      <th>af_000</th>\n",
       "      <th>ag_000</th>\n",
       "      <th>ag_001</th>\n",
       "      <th>ag_002</th>\n",
       "      <th>...</th>\n",
       "      <th>ee_002</th>\n",
       "      <th>ee_003</th>\n",
       "      <th>ee_004</th>\n",
       "      <th>ee_005</th>\n",
       "      <th>ee_006</th>\n",
       "      <th>ee_007</th>\n",
       "      <th>ee_008</th>\n",
       "      <th>ee_009</th>\n",
       "      <th>ef_000</th>\n",
       "      <th>eg_000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>neg</td>\n",
       "      <td>76698</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.130706e+09</td>\n",
       "      <td>280.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1240520.0</td>\n",
       "      <td>493384.0</td>\n",
       "      <td>721044.0</td>\n",
       "      <td>469792.0</td>\n",
       "      <td>339156.0</td>\n",
       "      <td>157956.0</td>\n",
       "      <td>73224.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>neg</td>\n",
       "      <td>33058</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>421400.0</td>\n",
       "      <td>178064.0</td>\n",
       "      <td>293306.0</td>\n",
       "      <td>245416.0</td>\n",
       "      <td>133654.0</td>\n",
       "      <td>81140.0</td>\n",
       "      <td>97576.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>neg</td>\n",
       "      <td>41040</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.280000e+02</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>277378.0</td>\n",
       "      <td>159812.0</td>\n",
       "      <td>423992.0</td>\n",
       "      <td>409564.0</td>\n",
       "      <td>320746.0</td>\n",
       "      <td>158022.0</td>\n",
       "      <td>95128.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>neg</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.000000e+01</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>240.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>neg</td>\n",
       "      <td>60874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.368000e+03</td>\n",
       "      <td>458.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>622012.0</td>\n",
       "      <td>229790.0</td>\n",
       "      <td>405298.0</td>\n",
       "      <td>347188.0</td>\n",
       "      <td>286954.0</td>\n",
       "      <td>311560.0</td>\n",
       "      <td>433954.0</td>\n",
       "      <td>1218.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 171 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  class  aa_000  ab_000        ac_000  ad_000  ae_000  af_000  ag_000  ag_001  \\\n",
       "0   neg   76698     0.0  2.130706e+09   280.0     0.0     0.0     0.0     0.0   \n",
       "1   neg   33058     0.0  0.000000e+00     0.0     0.0     0.0     0.0     0.0   \n",
       "2   neg   41040     0.0  2.280000e+02   100.0     0.0     0.0     0.0     0.0   \n",
       "3   neg      12     0.0  7.000000e+01    66.0     0.0    10.0     0.0     0.0   \n",
       "4   neg   60874     0.0  1.368000e+03   458.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "   ag_002  ...     ee_002    ee_003    ee_004    ee_005    ee_006    ee_007  \\\n",
       "0     0.0  ...  1240520.0  493384.0  721044.0  469792.0  339156.0  157956.0   \n",
       "1     0.0  ...   421400.0  178064.0  293306.0  245416.0  133654.0   81140.0   \n",
       "2     0.0  ...   277378.0  159812.0  423992.0  409564.0  320746.0  158022.0   \n",
       "3     0.0  ...      240.0      46.0      58.0      44.0      10.0       0.0   \n",
       "4     0.0  ...   622012.0  229790.0  405298.0  347188.0  286954.0  311560.0   \n",
       "\n",
       "     ee_008  ee_009  ef_000  eg_000  \n",
       "0   73224.0     0.0     0.0     0.0  \n",
       "1   97576.0  1500.0     0.0     0.0  \n",
       "2   95128.0   514.0     0.0     0.0  \n",
       "3       0.0     0.0     4.0    32.0  \n",
       "4  433954.0  1218.0     0.0     0.0  \n",
       "\n",
       "[5 rows x 171 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "432e3855",
   "metadata": {},
   "source": [
    "This is now the inside of the dataset that shows the conversion of NAN to 0."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbb9001b",
   "metadata": {},
   "source": [
    "## 3.PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "780327a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aps_no_label = df.drop(columns=[\"class\"])\n",
    "pca = PCA().fit(aps_no_label) \n",
    "plt.plot(np.cumsum(pca.explained_variance_ratio_)) \n",
    "plt.xlabel('number of components') \n",
    "plt.ylabel('cumulative explained variance');  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "486d1c00",
   "metadata": {},
   "source": [
    "This graph shows and determines the principal component variance of the dataset that becomes stable in the 10 values of the y-axis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "08917371",
   "metadata": {},
   "outputs": [],
   "source": [
    "characterisation  = StandardScaler()\n",
    "scaled_aps_no_label = characterisation.fit_transform(aps_no_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b73b60f",
   "metadata": {},
   "source": [
    "I used this to properly and proficiently visualize the model's graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d3633397",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(10)\n",
    "projected = pca.fit_transform(scaled_aps_no_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58c1aee0",
   "metadata": {},
   "source": [
    "I used this to see the reduction of columns from 171 columns to 10 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "da2a7929",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 10)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "projected.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f6417c3",
   "metadata": {},
   "source": [
    "I used this because it will help me visualize the transformation of the dimensionality in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5d794dc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "newaps_df= pd.DataFrame(projected, columns =['C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9', 'C10'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83a779bc",
   "metadata": {},
   "source": [
    "I use this to reduce or renew the old column of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c40f77e9",
   "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>C1</th>\n",
       "      <th>C2</th>\n",
       "      <th>C3</th>\n",
       "      <th>C4</th>\n",
       "      <th>C5</th>\n",
       "      <th>C6</th>\n",
       "      <th>C7</th>\n",
       "      <th>C8</th>\n",
       "      <th>C9</th>\n",
       "      <th>C10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.425646</td>\n",
       "      <td>-1.118888</td>\n",
       "      <td>-0.387009</td>\n",
       "      <td>2.034901</td>\n",
       "      <td>-0.037306</td>\n",
       "      <td>0.806108</td>\n",
       "      <td>0.295730</td>\n",
       "      <td>0.403382</td>\n",
       "      <td>-0.459556</td>\n",
       "      <td>-1.650238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.655865</td>\n",
       "      <td>-0.679073</td>\n",
       "      <td>-0.094249</td>\n",
       "      <td>0.083405</td>\n",
       "      <td>-0.161884</td>\n",
       "      <td>0.172470</td>\n",
       "      <td>0.199945</td>\n",
       "      <td>-0.171099</td>\n",
       "      <td>0.196752</td>\n",
       "      <td>0.235187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.219174</td>\n",
       "      <td>-0.005376</td>\n",
       "      <td>0.887340</td>\n",
       "      <td>-0.121936</td>\n",
       "      <td>-0.153679</td>\n",
       "      <td>0.061415</td>\n",
       "      <td>-0.073181</td>\n",
       "      <td>0.102951</td>\n",
       "      <td>-0.145876</td>\n",
       "      <td>0.082371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-3.207559</td>\n",
       "      <td>0.324734</td>\n",
       "      <td>1.078454</td>\n",
       "      <td>-0.657870</td>\n",
       "      <td>0.206917</td>\n",
       "      <td>-0.284919</td>\n",
       "      <td>-0.163786</td>\n",
       "      <td>-0.397283</td>\n",
       "      <td>0.487013</td>\n",
       "      <td>0.972980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.332767</td>\n",
       "      <td>-0.042869</td>\n",
       "      <td>0.046820</td>\n",
       "      <td>0.956753</td>\n",
       "      <td>-0.568543</td>\n",
       "      <td>0.226628</td>\n",
       "      <td>0.005640</td>\n",
       "      <td>0.441972</td>\n",
       "      <td>-0.533197</td>\n",
       "      <td>-1.268607</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         C1        C2        C3        C4        C5        C6        C7  \\\n",
       "0  2.425646 -1.118888 -0.387009  2.034901 -0.037306  0.806108  0.295730   \n",
       "1 -0.655865 -0.679073 -0.094249  0.083405 -0.161884  0.172470  0.199945   \n",
       "2 -1.219174 -0.005376  0.887340 -0.121936 -0.153679  0.061415 -0.073181   \n",
       "3 -3.207559  0.324734  1.078454 -0.657870  0.206917 -0.284919 -0.163786   \n",
       "4  0.332767 -0.042869  0.046820  0.956753 -0.568543  0.226628  0.005640   \n",
       "\n",
       "         C8        C9       C10  \n",
       "0  0.403382 -0.459556 -1.650238  \n",
       "1 -0.171099  0.196752  0.235187  \n",
       "2  0.102951 -0.145876  0.082371  \n",
       "3 -0.397283  0.487013  0.972980  \n",
       "4  0.441972 -0.533197 -1.268607  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newaps_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a99005e1",
   "metadata": {},
   "source": [
    "This is the new column of the dataset that has 10 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "786046d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "newaps_df[\"class\"] = df[\"class\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c2a8bce",
   "metadata": {},
   "source": [
    "I use my new old column to formulate a new dataset columns. And the class is the target variable that I put in here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4757ac69",
   "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>C1</th>\n",
       "      <th>C2</th>\n",
       "      <th>C3</th>\n",
       "      <th>C4</th>\n",
       "      <th>C5</th>\n",
       "      <th>C6</th>\n",
       "      <th>C7</th>\n",
       "      <th>C8</th>\n",
       "      <th>C9</th>\n",
       "      <th>C10</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.425646</td>\n",
       "      <td>-1.118888</td>\n",
       "      <td>-0.387009</td>\n",
       "      <td>2.034901</td>\n",
       "      <td>-0.037306</td>\n",
       "      <td>0.806108</td>\n",
       "      <td>0.295730</td>\n",
       "      <td>0.403382</td>\n",
       "      <td>-0.459556</td>\n",
       "      <td>-1.650238</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.655865</td>\n",
       "      <td>-0.679073</td>\n",
       "      <td>-0.094249</td>\n",
       "      <td>0.083405</td>\n",
       "      <td>-0.161884</td>\n",
       "      <td>0.172470</td>\n",
       "      <td>0.199945</td>\n",
       "      <td>-0.171099</td>\n",
       "      <td>0.196752</td>\n",
       "      <td>0.235187</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.219174</td>\n",
       "      <td>-0.005376</td>\n",
       "      <td>0.887340</td>\n",
       "      <td>-0.121936</td>\n",
       "      <td>-0.153679</td>\n",
       "      <td>0.061415</td>\n",
       "      <td>-0.073181</td>\n",
       "      <td>0.102951</td>\n",
       "      <td>-0.145876</td>\n",
       "      <td>0.082371</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-3.207559</td>\n",
       "      <td>0.324734</td>\n",
       "      <td>1.078454</td>\n",
       "      <td>-0.657870</td>\n",
       "      <td>0.206917</td>\n",
       "      <td>-0.284919</td>\n",
       "      <td>-0.163786</td>\n",
       "      <td>-0.397283</td>\n",
       "      <td>0.487013</td>\n",
       "      <td>0.972980</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.332767</td>\n",
       "      <td>-0.042869</td>\n",
       "      <td>0.046820</td>\n",
       "      <td>0.956753</td>\n",
       "      <td>-0.568543</td>\n",
       "      <td>0.226628</td>\n",
       "      <td>0.005640</td>\n",
       "      <td>0.441972</td>\n",
       "      <td>-0.533197</td>\n",
       "      <td>-1.268607</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         C1        C2        C3        C4        C5        C6        C7  \\\n",
       "0  2.425646 -1.118888 -0.387009  2.034901 -0.037306  0.806108  0.295730   \n",
       "1 -0.655865 -0.679073 -0.094249  0.083405 -0.161884  0.172470  0.199945   \n",
       "2 -1.219174 -0.005376  0.887340 -0.121936 -0.153679  0.061415 -0.073181   \n",
       "3 -3.207559  0.324734  1.078454 -0.657870  0.206917 -0.284919 -0.163786   \n",
       "4  0.332767 -0.042869  0.046820  0.956753 -0.568543  0.226628  0.005640   \n",
       "\n",
       "         C8        C9       C10 class  \n",
       "0  0.403382 -0.459556 -1.650238   neg  \n",
       "1 -0.171099  0.196752  0.235187   neg  \n",
       "2  0.102951 -0.145876  0.082371   neg  \n",
       "3 -0.397283  0.487013  0.972980   neg  \n",
       "4  0.441972 -0.533197 -1.268607   neg  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newaps_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "718668e4",
   "metadata": {},
   "source": [
    "This is the renewing of columns from 171 to 10 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e43d2e5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_array= newaps_df.values                          "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daab1294",
   "metadata": {},
   "source": [
    "I use this because it is necessary to have a new name for the array values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2ac77af8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = df_array[:,0:10]\n",
    "y = df_array[:,10]\n",
    "X_train, X_validation, Y_train, Y_validation = train_test_split(X, y, test_size=0.20, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5684ab19",
   "metadata": {},
   "source": [
    "I use this because it helps me to guarantee the split of the dataset training. The test size=0.20 and random state=1 This is the limit to make sure that the data is consistent every time I run the code. (scikit-learn, 2018). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4a87b938",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score      \n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6e6b709",
   "metadata": {},
   "source": [
    "I used this library that has a combination of models to split the data for the data analysis I need to perform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b63cc848",
   "metadata": {},
   "outputs": [],
   "source": [
    "models = []\n",
    "models.append(('LR', LogisticRegression(solver='liblinear', multi_class='ovr')))\n",
    "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('CART', DecisionTreeClassifier()))\n",
    "models.append(('SVM', SVC(gamma='auto')))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60be3993",
   "metadata": {},
   "source": [
    "It helps me assess the performance of the best models, which perform well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "89dd190d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: 0.985063 (0.001456)\n",
      "LDA: 0.981958 (0.002016)\n",
      "KNN: 0.989167 (0.001079)\n",
      "CART: 0.986563 (0.001244)\n",
      "SVM: 0.984938 (0.000632)\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "names = []\n",
    "for name, model in models:\n",
    "\tkfold = StratifiedKFold(n_splits=10, random_state=1, shuffle=True)\n",
    "\tcv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy')\n",
    "\tresults.append(cv_results)\n",
    "\tnames.append(name)\n",
    "\tprint('%s: %f (%f)' % (name, cv_results.mean(), cv_results.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca4b61ca",
   "metadata": {},
   "source": [
    "It helps me decide which model I will use in the further continuation of my data analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1e50afcb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.boxplot(results, labels=names)\n",
    "pyplot.title('Algorithm Comparison')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa7123bc",
   "metadata": {},
   "source": [
    "I use the graph to compare the models and analyse which models are adequate for the distribution of different algorithms. The model that is most affected by the curse of dimensionality is the KNN model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c5d6b828",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression(solver='liblinear', multi_class='ovr')\n",
    "model.fit(X_train, Y_train)\n",
    "predictions = model.predict(X_validation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a394e3f0",
   "metadata": {},
   "source": [
    "I apply this code to have an important basis for the proper evaluation of the dataset and to have a good result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "001ee828",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9858333333333333\n",
      "[[11771    38]\n",
      " [  132    59]]\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(Y_validation, predictions))\n",
    "print(confusion_matrix(Y_validation, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7db53ba7",
   "metadata": {},
   "source": [
    "I used this to analyze if the test is working properly and locate where and which part it performs well."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03bc150a",
   "metadata": {},
   "source": [
    "In my opinion, the curse of dimensionality occurs when you have big data with high attributes that cause problems when training models. For me to avoid the curse of dimensionality, it is necessary to use the PCA as a tool to reduce the large dataset columns to get an accurate variance, and by reducing the columns, the PCA maintains the extensive and vital information of the dataset. I used regularisation techniques to prevent overfitting. The curse  of dimentionality must be reduced to ensure the accuracy and quality of the graph models that are used for testing."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be1b9eb1",
   "metadata": {},
   "source": [
    "Conclusion:\n",
    "   \n",
    "   Overall, In this class assignment, I encountered several challenges that I managed to figure out. And I use some different alternatives and solutions to solve the challenges. It enhanced me to widen my knowledge of both technical and detailed reports. I found a solution to different problems in this class assignment through, the use of watching videos. I encounter errors, but I strive hard to find informative solutions to fix them. The outcome of this APS failure dataset is to establish the correct reduction of the curse of dimensionality using the PCA technique. To train and test the machine learning models and visualiize the most efficient model that is KNN that I will use to my future analysis. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8e55c2c",
   "metadata": {},
   "source": [
    "References:\n",
    "\n",
    "AlmaBetter. (n.d.). Methods and Attributes in Python. [online] Available at: https://www.almabetter.com/bytes/tutorials/python/methods-and-attributes-in-python [Accessed 5 Nov. 2023].\n",
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