LogisticRegression Algorithm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Packages"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "sklearn -> for dataset and learning model\n",
    "numpy   -> for handling data numeric matrix\n",
    "matplotlib, seaborn -> for data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading Digits Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image data shape:  (1797, 64)\n",
      "Lable data shape:  (1797,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "digits = load_digits()\n",
    "\n",
    "print(\"Image data shape: \", digits.data.shape)\n",
    "print(\"Lable data shape: \", digits.target.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spliting Dataset 2 Training & Testing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data shape:  (1437, 64) | Training lable shape:  (1437,)\n",
      "Testing Data shape:  (360, 64) | Testing lable shape:  (360,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(np.array(digits.data), digits.target, test_size = 0.2, random_state = 100)\n",
    "print(\"Training Data shape: \", X_train.shape, \"| Training lable shape: \", y_train.shape)\n",
    "print(\"Testing Data shape: \", X_test.shape, \"| Testing lable shape: \", y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train & Testing Using 21 Folds 4 Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yousry/DSS_Env/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/home/yousry/DSS_Env/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/home/yousry/DSS_Env/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/home/yousry/DSS_Env/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/home/yousry/DSS_Env/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/home/yousry/DSS_Env/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/home/yousry/DSS_Env/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/home/yousry/DSS_Env/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/home/yousry/DSS_Env/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/home/yousry/DSS_Env/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/home/yousry/DSS_Env/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/home/yousry/DSS_Env/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/home/yousry/DSS_Env/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/home/yousry/DSS_Env/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/home/yousry/DSS_Env/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/home/yousry/DSS_Env/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/home/yousry/DSS_Env/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/home/yousry/DSS_Env/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/home/yousry/DSS_Env/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/home/yousry/DSS_Env/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "/home/yousry/DSS_Env/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 99.9%\n",
      "Testing Score: 97.5%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yousry/DSS_Env/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegressionCV(Cs = 10, multi_class = \"multinomial\", cv = 21)\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "training_score = lr.score(X_train, y_train)\n",
    "testing_score = lr.score(X_test, y_test)\n",
    "\n",
    "print(\"Training Score: %.1f%%\" % (training_score * 100))\n",
    "print(\"Testing Score: %.1f%%\" % (testing_score * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pass testing data to predict its lables then calculate prediction accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Model Accuracy: 97.5%')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy = \"Model Accuracy: {:.1f}%\".format(testing_score * 100)\n",
    "\n",
    "y_pred = lr.predict(X_test)\n",
    "confusion_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
    "plt.figure(figsize=(10, 10))\n",
    "sns.heatmap(confusion_matrix, square = True, annot = True, cmap = \"Dark2_r\", linewidths = 0.5)\n",
    "plt.xlabel(\"Predicted Values\")\n",
    "plt.ylabel(\"Real Values\")\n",
    "plt.title(accuracy, size = 22)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}