Image Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'sklearn.cross_validation'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-0a464743cd99>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensemble\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mRandomForestClassifier\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcross_validation\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'matplotlib'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'inline'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'sklearn.cross_validation'"
     ]
    }
   ],
   "source": [
    "#import the requirements\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.cross_validation import train_test_split\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: sklearn in c:\\programdata\\anaconda3\\lib\\site-packages (0.0)\n",
      "Requirement already satisfied: scikit-learn in c:\\programdata\\anaconda3\\lib\\site-packages (from sklearn) (0.22.1)\n",
      "Requirement already satisfied: scipy>=0.17.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.4.1)\n",
      "Requirement already satisfied: numpy>=1.11.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.18.1)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (0.14.1)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'sklearn.cross_validation'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-0a464743cd99>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensemble\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mRandomForestClassifier\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcross_validation\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'matplotlib'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'inline'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'sklearn.cross_validation'"
     ]
    }
   ],
   "source": [
    "#import the requirements\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.cross_validation import train_test_split\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the requirements\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reading the data from mnist.csv\n",
    "data = pd.read_csv(\"mnist.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       label  1x1  1x2  1x3  1x4  1x5  1x6  1x7  1x8  1x9  ...  28x19  28x20  \\\n",
      "0          5    0    0    0    0    0    0    0    0    0  ...      0      0   \n",
      "1          0    0    0    0    0    0    0    0    0    0  ...      0      0   \n",
      "2          4    0    0    0    0    0    0    0    0    0  ...      0      0   \n",
      "3          1    0    0    0    0    0    0    0    0    0  ...      0      0   \n",
      "4          9    0    0    0    0    0    0    0    0    0  ...      0      0   \n",
      "...      ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...    ...    ...   \n",
      "59995      8    0    0    0    0    0    0    0    0    0  ...      0      0   \n",
      "59996      3    0    0    0    0    0    0    0    0    0  ...      0      0   \n",
      "59997      5    0    0    0    0    0    0    0    0    0  ...      0      0   \n",
      "59998      6    0    0    0    0    0    0    0    0    0  ...      0      0   \n",
      "59999      8    0    0    0    0    0    0    0    0    0  ...      0      0   \n",
      "\n",
      "       28x21  28x22  28x23  28x24  28x25  28x26  28x27  28x28  \n",
      "0          0      0      0      0      0      0      0      0  \n",
      "1          0      0      0      0      0      0      0      0  \n",
      "2          0      0      0      0      0      0      0      0  \n",
      "3          0      0      0      0      0      0      0      0  \n",
      "4          0      0      0      0      0      0      0      0  \n",
      "...      ...    ...    ...    ...    ...    ...    ...    ...  \n",
      "59995      0      0      0      0      0      0      0      0  \n",
      "59996      0      0      0      0      0      0      0      0  \n",
      "59997      0      0      0      0      0      0      0      0  \n",
      "59998      0      0      0      0      0      0      0      0  \n",
      "59999      0      0      0      0      0      0      0      0  \n",
      "\n",
      "[60000 rows x 785 columns]\n"
     ]
    }
   ],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "   label  1x1  1x2  1x3  1x4  1x5  1x6  1x7  1x8  1x9  ...  28x19  28x20  \\\n",
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#viewing column heads\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#extracting data from the dataset and viewing them up close\n",
    "a = data.iloc[3,1:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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      "   0   0   0   0  64 251 253 220   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0  24 193 253 220\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0]\n"
     ]
    }
   ],
   "source": [
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2910958a9c8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#reshaping the data according to requirements\n",
    "a = a.reshape(28,28).astype('uint8')\n",
    "plt.imshow(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#preapring the data\n",
    "#separating labels and data values\n",
    "df_x = data.iloc[:,1:]\n",
    "df_y = data.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#spliting the data\n",
    "x_train, x_test, y_train, y_test = train_test_split(df_x, df_y, test_size = 0.2, random_state=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>1x1</th>\n",
       "      <th>1x2</th>\n",
       "      <th>1x3</th>\n",
       "      <th>1x4</th>\n",
       "      <th>1x5</th>\n",
       "      <th>1x6</th>\n",
       "      <th>1x7</th>\n",
       "      <th>1x8</th>\n",
       "      <th>1x9</th>\n",
       "      <th>1x10</th>\n",
       "      <th>...</th>\n",
       "      <th>28x19</th>\n",
       "      <th>28x20</th>\n",
       "      <th>28x21</th>\n",
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       "      <th>28x25</th>\n",
       "      <th>28x26</th>\n",
       "      <th>28x27</th>\n",
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       "  </thead>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30510</th>\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",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 784 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       1x1  1x2  1x3  1x4  1x5  1x6  1x7  1x8  1x9  1x10  ...  28x19  28x20  \\\n",
       "20379    0    0    0    0    0    0    0    0    0     0  ...      0      0   \n",
       "53032    0    0    0    0    0    0    0    0    0     0  ...      0      0   \n",
       "27005    0    0    0    0    0    0    0    0    0     0  ...      0      0   \n",
       "30510    0    0    0    0    0    0    0    0    0     0  ...      0      0   \n",
       "508      0    0    0    0    0    0    0    0    0     0  ...      0      0   \n",
       "\n",
       "       28x21  28x22  28x23  28x24  28x25  28x26  28x27  28x28  \n",
       "20379      0      0      0      0      0      0      0      0  \n",
       "53032      0      0      0      0      0      0      0      0  \n",
       "27005      0      0      0      0      0      0      0      0  \n",
       "30510      0      0      0      0      0      0      0      0  \n",
       "508        0      0      0      0      0      0      0      0  \n",
       "\n",
       "[5 rows x 784 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check data\n",
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20379    4\n",
       "53032    4\n",
       "27005    7\n",
       "30510    8\n",
       "508      1\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# call rf classifier\n",
    "rf = RandomForestClassifier(n_estimators=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit the model\n",
    "rf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#prediction on test data\n",
    "pred = rf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 7, 6, ..., 6, 4, 2], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#check prediction accuracy\n",
    "a = y_test.values\n",
    "\n",
    "# calculate number of correctly predicted values\n",
    "count =0\n",
    "for i in range (len(pred)):\n",
    "    if pred[i] == a[i]:\n",
    "        count = count+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11606"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total values that the prediction code was run on\n",
    "len(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#accuracy values\n",
    "acc = count/len(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9671666666666666"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}