handwritten_digit_reco.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "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": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000, 784)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=mnist.data\n",
    "y=mnist.target\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'], dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    7877\n",
       "7    7293\n",
       "3    7141\n",
       "2    6990\n",
       "9    6958\n",
       "0    6903\n",
       "6    6876\n",
       "8    6825\n",
       "4    6824\n",
       "5    6313\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(y).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx=(y=='0')|(y=='3')\n",
    "X_new=X[idx]\n",
    "y_new=y[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14044, 784)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "xtrain, xtest, ytrain, ytest = train_test_split(X_new,y_new,test_size=.2,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "255.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain=xtrain/255.0\n",
    "xtest=xtest/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca=PCA(0.94)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_train=pca.fit_transform(xtrain)\n",
    "pca_test=pca.transform(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11235, 113)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "log=LogisticRegression(C=.01)\n",
    "log.fit(pca_train,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9932360270558918"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log.score(pca_test,ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: opencv-python in c:\\users\\user\\anaconda3\\lib\\site-packages (4.1.1.26)\n",
      "Requirement already satisfied: numpy>=1.14.5 in c:\\users\\user\\anaconda3\\lib\\site-packages (from opencv-python) (1.15.4)\n"
     ]
    }
   ],
   "source": [
    "!pip install opencv-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img=cv2.imread('t1.png',0)\n",
    "img.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 105)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "test=cv2.resize(img,(28,28))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x17c63fda4e0>"
      ]
     },
     "execution_count": 100,
     "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(test,cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test=test/255.0\n",
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test.reshape(1,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 784)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "test1 = pca.transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 113)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['3'], dtype=object)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log.predict(test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['3']\n",
      "['3']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['3']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['3']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['3']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['3']\n"
     ]
    }
   ],
   "source": [
    "for i in [.0001,.001,.01,.1,10,100]:\n",
    "    log = LogisticRegression(C=i)\n",
    "    log.fit(pca_train,ytrain)\n",
    "    print(log.predict(test1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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