Explore  and Preprocess the data set.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import the relevant libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Data Splitting Process\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Training Process\n",
    "\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Performance Measures \n",
    "\n",
    "from sklearn.base import BaseEstimator\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import precision_score, recall_score\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the MNIST data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_train = pd.read_csv(\"data/train.csv\")\n",
    "mnist_test  = pd.read_csv(\"data/test.csv\")\n",
    "\n",
    "# Take copies of the master dataframes\n",
    "train = mnist_train.copy()\n",
    "test = mnist_test.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42000, 785)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28000, 784)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
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     "execution_count": 5,
     "metadata": {},
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   "source": [
    "train.head()"
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  {
   "cell_type": "code",
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     "execution_count": 6,
     "metadata": {},
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   "source": [
    "train.tail()"
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  },
  {
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   "execution_count": 7,
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 784 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
       "0       0       0       0       0       0       0       0       0       0   \n",
       "1       0       0       0       0       0       0       0       0       0   \n",
       "2       0       0       0       0       0       0       0       0       0   \n",
       "3       0       0       0       0       0       0       0       0       0   \n",
       "4       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   pixel9  ...  pixel774  pixel775  pixel776  pixel777  pixel778  pixel779  \\\n",
       "0       0  ...         0         0         0         0         0         0   \n",
       "1       0  ...         0         0         0         0         0         0   \n",
       "2       0  ...         0         0         0         0         0         0   \n",
       "3       0  ...         0         0         0         0         0         0   \n",
       "4       0  ...         0         0         0         0         0         0   \n",
       "\n",
       "   pixel780  pixel781  pixel782  pixel783  \n",
       "0         0         0         0         0  \n",
       "1         0         0         0         0  \n",
       "2         0         0         0         0  \n",
       "3         0         0         0         0  \n",
       "4         0         0         0         0  \n",
       "\n",
       "[5 rows x 784 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "<p>5 rows × 784 columns</p>\n",
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      "text/plain": [
       "       pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
       "27995       0       0       0       0       0       0       0       0       0   \n",
       "27996       0       0       0       0       0       0       0       0       0   \n",
       "27997       0       0       0       0       0       0       0       0       0   \n",
       "27998       0       0       0       0       0       0       0       0       0   \n",
       "27999       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "       pixel9  ...  pixel774  pixel775  pixel776  pixel777  pixel778  \\\n",
       "27995       0  ...         0         0         0         0         0   \n",
       "27996       0  ...         0         0         0         0         0   \n",
       "27997       0  ...         0         0         0         0         0   \n",
       "27998       0  ...         0         0         0         0         0   \n",
       "27999       0  ...         0         0         0         0         0   \n",
       "\n",
       "       pixel779  pixel780  pixel781  pixel782  pixel783  \n",
       "27995         0         0         0         0         0  \n",
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       "27999         0         0         0         0         0  \n",
       "\n",
       "[5 rows x 784 columns]"
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "test.tail()"
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  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>42000.000000</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>42000.0</td>\n",
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       "      <td>42000.000000</td>\n",
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       "      <td>42000.0</td>\n",
       "      <td>42000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.456643</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.219286</td>\n",
       "      <td>0.117095</td>\n",
       "      <td>0.059024</td>\n",
       "      <td>0.02019</td>\n",
       "      <td>0.017238</td>\n",
       "      <td>0.002857</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.887730</td>\n",
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       "      <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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.312890</td>\n",
       "      <td>4.633819</td>\n",
       "      <td>3.274488</td>\n",
       "      <td>1.75987</td>\n",
       "      <td>1.894498</td>\n",
       "      <td>0.414264</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.000000</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>253.00000</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 785 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              label   pixel0   pixel1   pixel2   pixel3   pixel4   pixel5  \\\n",
       "count  42000.000000  42000.0  42000.0  42000.0  42000.0  42000.0  42000.0   \n",
       "mean       4.456643      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "std        2.887730      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "min        0.000000      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "25%        2.000000      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "50%        4.000000      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "75%        7.000000      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "max        9.000000      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "\n",
       "        pixel6   pixel7   pixel8  ...      pixel774      pixel775  \\\n",
       "count  42000.0  42000.0  42000.0  ...  42000.000000  42000.000000   \n",
       "mean       0.0      0.0      0.0  ...      0.219286      0.117095   \n",
       "std        0.0      0.0      0.0  ...      6.312890      4.633819   \n",
       "min        0.0      0.0      0.0  ...      0.000000      0.000000   \n",
       "25%        0.0      0.0      0.0  ...      0.000000      0.000000   \n",
       "50%        0.0      0.0      0.0  ...      0.000000      0.000000   \n",
       "75%        0.0      0.0      0.0  ...      0.000000      0.000000   \n",
       "max        0.0      0.0      0.0  ...    254.000000    254.000000   \n",
       "\n",
       "           pixel776     pixel777      pixel778      pixel779  pixel780  \\\n",
       "count  42000.000000  42000.00000  42000.000000  42000.000000   42000.0   \n",
       "mean       0.059024      0.02019      0.017238      0.002857       0.0   \n",
       "std        3.274488      1.75987      1.894498      0.414264       0.0   \n",
       "min        0.000000      0.00000      0.000000      0.000000       0.0   \n",
       "25%        0.000000      0.00000      0.000000      0.000000       0.0   \n",
       "50%        0.000000      0.00000      0.000000      0.000000       0.0   \n",
       "75%        0.000000      0.00000      0.000000      0.000000       0.0   \n",
       "max      253.000000    253.00000    254.000000     62.000000       0.0   \n",
       "\n",
       "       pixel781  pixel782  pixel783  \n",
       "count   42000.0   42000.0   42000.0  \n",
       "mean        0.0       0.0       0.0  \n",
       "std         0.0       0.0       0.0  \n",
       "min         0.0       0.0       0.0  \n",
       "25%         0.0       0.0       0.0  \n",
       "50%         0.0       0.0       0.0  \n",
       "75%         0.0       0.0       0.0  \n",
       "max         0.0       0.0       0.0  \n",
       "\n",
       "[8 rows x 785 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['label', 'pixel0', 'pixel1', 'pixel2', 'pixel3', 'pixel4', 'pixel5',\n",
      "       'pixel6', 'pixel7', 'pixel8',\n",
      "       ...\n",
      "       'pixel774', 'pixel775', 'pixel776', 'pixel777', 'pixel778', 'pixel779',\n",
      "       'pixel780', 'pixel781', 'pixel782', 'pixel783'],\n",
      "      dtype='object', length=785)\n"
     ]
    }
   ],
   "source": [
    "print(train.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['pixel0', 'pixel1', 'pixel2', 'pixel3', 'pixel4', 'pixel5', 'pixel6',\n",
      "       'pixel7', 'pixel8', 'pixel9',\n",
      "       ...\n",
      "       'pixel774', 'pixel775', 'pixel776', 'pixel777', 'pixel778', 'pixel779',\n",
      "       'pixel780', 'pixel781', 'pixel782', 'pixel783'],\n",
      "      dtype='object', length=784)\n"
     ]
    }
   ],
   "source": [
    "print(test.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocess the data set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().any().any()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "the results means that the data is already clean, so we don't have any missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Separate Features and Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42000, 784)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y = train.drop(labels = [\"label\"],axis = 1).to_numpy(), train[\"label\"]\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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