Handwritten_doc_recog.py.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_openml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist = fetch_openml('mnist_784')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x,y = mnist['data'],mnist['target']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000, 784)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in c:\\users\\talk2\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (3.2.1)"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 19.2.3, however version 20.1.1 is available.\n",
      "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in c:\\users\\talk2\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib) (2.4.7)\n",
      "Requirement already satisfied: numpy>=1.11 in c:\\users\\talk2\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib) (1.18.4)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\talk2\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib) (2.8.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\talk2\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\talk2\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib) (0.10.0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\talk2\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from python-dateutil>=2.1->matplotlib) (1.14.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "some_digit = x[36001]\n",
    "some_digit_image = some_digit.reshape(28,28) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x207029d1070>"
      ]
     },
     "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": [
    "plt.imshow(some_digit_image, cmap=matplotlib.cm.binary, interpolation=\"nearest\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[36001]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test = x[:60000],x[60000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train, y_test = y[:60000],y[60000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "shuffle_index = np.random.permutation(60000)\n",
    "x_train, y_train = x_train[shuffle_index],y_train[shuffle_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = y_train.astype(np.int8)\n",
    "y_test = y_test.astype(np.int8)\n",
    "y_train_2 =(y_train==2)\n",
    "y_test_2 = (y_test==2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, ..., False, False, False])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#clf = LogisticRegresion(tol = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#clf.fit(x_train, y_train_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(237, 4)\n"
     ]
    },
    {
     "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>Gender</th>\n",
       "      <th>Age Range</th>\n",
       "      <th>Head Size(cm^3)</th>\n",
       "      <th>Brain Weight(grams)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4512</td>\n",
       "      <td>1530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3738</td>\n",
       "      <td>1297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4261</td>\n",
       "      <td>1335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3777</td>\n",
       "      <td>1282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4177</td>\n",
       "      <td>1590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Age Range  Head Size(cm^3)  Brain Weight(grams)\n",
       "0       1          1             4512                 1530\n",
       "1       1          1             3738                 1297\n",
       "2       1          1             4261                 1335\n",
       "3       1          1             3777                 1282\n",
       "4       1          1             4177                 1590"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = (20.0,10.0)\n",
    "\n",
    "data = pd.read_csv(\"D:\\ML_task_1\\headbrain.csv\")\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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  "language_info": {
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   "file_extension": ".py",
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