Logistic Regression Binary Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression (Binary Classification)\n",
    "# By Giriraj Vaishnav"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Packages, Function and Classes\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       [7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,\n",
      "        7.039e-02]]), 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
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      "       1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
      "       1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
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      "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1]), 'frame': None, 'target_names': array(['malignant', 'benign'], dtype='<U9'), 'DESCR': '.. _breast_cancer_dataset:\\n\\nBreast cancer wisconsin (diagnostic) dataset\\n--------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 569\\n\\n    :Number of Attributes: 30 numeric, predictive attributes and the class\\n\\n    :Attribute Information:\\n        - radius (mean of distances from center to points on the perimeter)\\n        - texture (standard deviation of gray-scale values)\\n        - perimeter\\n        - area\\n        - smoothness (local variation in radius lengths)\\n        - compactness (perimeter^2 / area - 1.0)\\n        - concavity (severity of concave portions of the contour)\\n        - concave points (number of concave portions of the contour)\\n        - symmetry\\n        - fractal dimension (\"coastline approximation\" - 1)\\n\\n        The mean, standard error, and \"worst\" or largest (mean of the three\\n        worst/largest values) of these features were computed for each image,\\n        resulting in 30 features.  For instance, field 0 is Mean Radius, field\\n        10 is Radius SE, field 20 is Worst Radius.\\n\\n        - class:\\n                - WDBC-Malignant\\n                - WDBC-Benign\\n\\n    :Summary Statistics:\\n\\n    ===================================== ====== ======\\n                                           Min    Max\\n    ===================================== ====== ======\\n    radius (mean):                        6.981  28.11\\n    texture (mean):                       9.71   39.28\\n    perimeter (mean):                     43.79  188.5\\n    area (mean):                          143.5  2501.0\\n    smoothness (mean):                    0.053  0.163\\n    compactness (mean):                   0.019  0.345\\n    concavity (mean):                     0.0    0.427\\n    concave points (mean):                0.0    0.201\\n    symmetry (mean):                      0.106  0.304\\n    fractal dimension (mean):             0.05   0.097\\n    radius (standard error):              0.112  2.873\\n    texture (standard error):             0.36   4.885\\n    perimeter (standard error):           0.757  21.98\\n    area (standard error):                6.802  542.2\\n    smoothness (standard error):          0.002  0.031\\n    compactness (standard error):         0.002  0.135\\n    concavity (standard error):           0.0    0.396\\n    concave points (standard error):      0.0    0.053\\n    symmetry (standard error):            0.008  0.079\\n    fractal dimension (standard error):   0.001  0.03\\n    radius (worst):                       7.93   36.04\\n    texture (worst):                      12.02  49.54\\n    perimeter (worst):                    50.41  251.2\\n    area (worst):                         185.2  4254.0\\n    smoothness (worst):                   0.071  0.223\\n    compactness (worst):                  0.027  1.058\\n    concavity (worst):                    0.0    1.252\\n    concave points (worst):               0.0    0.291\\n    symmetry (worst):                     0.156  0.664\\n    fractal dimension (worst):            0.055  0.208\\n    ===================================== ====== ======\\n\\n    :Missing Attribute Values: None\\n\\n    :Class Distribution: 212 - Malignant, 357 - Benign\\n\\n    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\\n\\n    :Donor: Nick Street\\n\\n    :Date: November, 1995\\n\\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\\nhttps://goo.gl/U2Uwz2\\n\\nFeatures are computed from a digitized image of a fine needle\\naspirate (FNA) of a breast mass.  They describe\\ncharacteristics of the cell nuclei present in the image.\\n\\nSeparating plane described above was obtained using\\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\\nConstruction Via Linear Programming.\" Proceedings of the 4th\\nMidwest Artificial Intelligence and Cognitive Science Society,\\npp. 97-101, 1992], a classification method which uses linear\\nprogramming to construct a decision tree.  Relevant features\\nwere selected using an exhaustive search in the space of 1-4\\nfeatures and 1-3 separating planes.\\n\\nThe actual linear program used to obtain the separating plane\\nin the 3-dimensional space is that described in:\\n[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\\nProgramming Discrimination of Two Linearly Inseparable Sets\",\\nOptimization Methods and Software 1, 1992, 23-34].\\n\\nThis database is also available through the UW CS ftp server:\\n\\nftp ftp.cs.wisc.edu\\ncd math-prog/cpo-dataset/machine-learn/WDBC/\\n\\n.. topic:: References\\n\\n   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \\n     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \\n     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\\n     San Jose, CA, 1993.\\n   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \\n     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \\n     July-August 1995.\\n   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\\n     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \\n     163-171.', 'feature_names': array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
      "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
      "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
      "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
      "       'smoothness error', 'compactness error', 'concavity error',\n",
      "       'concave points error', 'symmetry error',\n",
      "       'fractal dimension error', 'worst radius', 'worst texture',\n",
      "       'worst perimeter', 'worst area', 'worst smoothness',\n",
      "       'worst compactness', 'worst concavity', 'worst concave points',\n",
      "       'worst symmetry', 'worst fractal dimension'], dtype='<U23'), 'filename': 'C:\\\\Users\\\\Om Prakash\\\\anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\datasets\\\\data\\\\breast_cancer.csv'}\n"
     ]
    }
   ],
   "source": [
    "# Read the Cancer Dataset file\n",
    "\n",
    "cancer = load_breast_cancer()\n",
    "print(cancer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Divide the data into\"Attributes\" and \"labels\"\n",
    "\n",
    "x_data = cancer.data[:,:1] # mean radius\n",
    "labels = cancer.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split 80% of the data to the training set while 20% of the data to test set\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(x_data, labels, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Model and Train it\n",
    "\n",
    "model = LogisticRegression(solver='liblinear', random_state=0)\n",
    "H = model.fit(X_train, y_train)\n",
    "\n",
    "# model = LogisticRegression(solver='liblinear', random_state=0).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Model Coeff (m) = [[-0.49524206]]\n",
      "Logistic Regression Model Coeff (b) = [7.54061572]\n"
     ]
    }
   ],
   "source": [
    "# Getting Results\n",
    "\n",
    "print('Logistic Regression Model Coeff (m) =' , model.coef_)\n",
    "print('Logistic Regression Model Coeff (b) =' , model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 0 0 0 1 0 1 1 0 1 1 1 1 0 1 0 1 0 1 1 1\n",
      " 0 1 1 1 1 0 1 1 0 1 1 1 1 0 1 0 1 1 1 1 1 1 0 1 0 1 1 0 1 0 0 0 1 1 0 1 1\n",
      " 0 1 1 1 1 1 0 0 0 1 0 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 0 1 0 0 1\n",
      " 0 0 1]\n"
     ]
    }
   ],
   "source": [
    "# Predicting the data\n",
    "\n",
    "y_predict=model.predict(X_test)\n",
    "print(y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Logistic Regression Model')"
      ]
     },
     "execution_count": 8,
     "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": [
    "# Scatter plot for Logistic Regression Model\n",
    "\n",
    "colors = {0:'red', 1:'blue'}\n",
    "def sigmoid(x):\n",
    "    return (1 / (1 +np.exp(-(model.intercept_[0] +(model.coef_[0][0] * x)))))\n",
    "x1 = np.arange(np.min(X_train), np.max(X_train), 0.01)\n",
    "y1 = [sigmoid(n) for n in x1]\n",
    "plt.scatter(X_train,y_train,facecolors='none',edgecolors=pd.DataFrame(cancer.target)[0].apply(lambda X_train:colors[X_train]),cmap=colors)\n",
    "plt.plot(x1,y1)\n",
    "plt.xlabel(\"Mean Radius\")\n",
    "plt.ylabel(\"Probability\")\n",
    "plt.title('Logistic Regression Model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Prediction Probability : \n",
      " [[0.28815287 0.71184713]\n",
      " [0.26924273 0.73075727]\n",
      " [0.35495662 0.64504338]\n",
      " [0.38261492 0.61738508]\n",
      " [0.25206829 0.74793171]\n",
      " [0.12735274 0.87264726]\n",
      " [0.17179642 0.82820358]\n",
      " [0.14851656 0.85148344]\n",
      " [0.02382539 0.97617461]\n",
      " [0.07875153 0.92124847]\n",
      " [0.43400977 0.56599023]\n",
      " [0.42551428 0.57448572]\n",
      " [0.05280865 0.94719135]\n",
      " [0.6898163  0.3101837 ]\n",
      " [0.4279373  0.5720627 ]\n",
      " [0.52892432 0.47107568]\n",
      " [0.04467502 0.95532498]\n",
      " [0.93193494 0.06806506]\n",
      " [0.89485926 0.10514074]\n",
      " [0.94426261 0.05573739]\n",
      " [0.1574998  0.8425002 ]\n",
      " [0.62295633 0.37704367]\n",
      " [0.29531567 0.70468433]\n",
      " [0.1574998  0.8425002 ]\n",
      " [0.95446663 0.04553337]\n",
      " [0.16350487 0.83649513]\n",
      " [0.10503629 0.89496371]\n",
      " [0.32279815 0.67720185]\n",
      " [0.28815287 0.71184713]\n",
      " [0.85259212 0.14740788]\n",
      " [0.23208687 0.76791313]\n",
      " [0.93379578 0.06620422]\n",
      " [0.26247677 0.73752323]\n",
      " [0.61360614 0.38639386]\n",
      " [0.06192253 0.93807747]\n",
      " [0.22081103 0.77918897]\n",
      " [0.27512902 0.72487098]\n",
      " [0.86634062 0.13365938]\n",
      " [0.45479412 0.54520588]\n",
      " [0.48191654 0.51808346]\n",
      " [0.48810143 0.51189857]\n",
      " [0.04133403 0.95866597]\n",
      " [0.59704369 0.40295631]\n",
      " [0.08093447 0.91906553]\n",
      " [0.27217591 0.72782409]\n",
      " [0.95838557 0.04161443]\n",
      " [0.03745748 0.96254252]\n",
      " [0.35609135 0.64390865]\n",
      " [0.15553836 0.84446164]\n",
      " [0.41585901 0.58414099]\n",
      " [0.87579239 0.12420761]\n",
      " [0.45479412 0.54520588]\n",
      " [0.8315427  0.1684573 ]\n",
      " [0.14236215 0.85763785]\n",
      " [0.20263607 0.79736393]\n",
      " [0.134681   0.865319  ]\n",
      " [0.12625603 0.87374397]\n",
      " [0.18785697 0.81214303]\n",
      " [0.18115121 0.81884879]\n",
      " [0.96198065 0.03801935]\n",
      " [0.32714365 0.67285635]\n",
      " [0.60298644 0.39701356]\n",
      " [0.11674528 0.88325472]\n",
      " [0.23742477 0.76257523]\n",
      " [0.76416379 0.23583621]\n",
      " [0.17109292 0.82890708]\n",
      " [0.97035663 0.02964337]\n",
      " [0.75144068 0.24855932]\n",
      " [0.93410129 0.06589871]\n",
      " [0.05451874 0.94548126]\n",
      " [0.40149852 0.59850148]\n",
      " [0.61007769 0.38992231]\n",
      " [0.13759302 0.86240698]\n",
      " [0.42309483 0.57690517]\n",
      " [0.84496559 0.15503441]\n",
      " [0.24927759 0.75072241]\n",
      " [0.03571225 0.96428775]\n",
      " [0.25394023 0.74605977]\n",
      " [0.14602885 0.85397115]\n",
      " [0.03859877 0.96140123]\n",
      " [0.66382854 0.33617146]\n",
      " [0.92080748 0.07919252]\n",
      " [0.56814924 0.43185076]\n",
      " [0.20024611 0.79975389]\n",
      " [0.59823458 0.40176542]\n",
      " [0.19166408 0.80833592]\n",
      " [0.2984169  0.7015831 ]\n",
      " [0.02983998 0.97016002]\n",
      " [0.72563651 0.27436349]\n",
      " [0.93470838 0.06529162]\n",
      " [0.05552294 0.94447706]\n",
      " [0.4868641  0.5131359 ]\n",
      " [0.47079898 0.52920102]\n",
      " [0.49924213 0.50075787]\n",
      " [0.05300718 0.94699282]\n",
      " [0.06601335 0.93398665]\n",
      " [0.53385718 0.46614282]\n",
      " [0.23922275 0.76077725]\n",
      " [0.14055777 0.85944223]\n",
      " [0.132389   0.867611  ]\n",
      " [0.04153071 0.95846929]\n",
      " [0.19475193 0.80524807]\n",
      " [0.46833194 0.53166806]\n",
      " [0.35836566 0.64163434]\n",
      " [0.8322353  0.1677647 ]\n",
      " [0.04970286 0.95029714]\n",
      " [0.79070325 0.20929675]\n",
      " [0.23832259 0.76167741]\n",
      " [0.54738559 0.45261441]\n",
      " [0.74772227 0.25227773]\n",
      " [0.22683328 0.77316672]\n",
      " [0.92080748 0.07919252]\n",
      " [0.82160602 0.17839398]\n",
      " [0.47697253 0.52302747]]\n",
      "\n",
      "Prediction :  [1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 0 0 0 1 0 1 1 0 1 1 1 1 0 1 0 1 0 1 1 1\n",
      " 0 1 1 1 1 0 1 1 0 1 1 1 1 0 1 0 1 1 1 1 1 1 0 1 0 1 1 0 1 0 0 0 1 1 0 1 1\n",
      " 0 1 1 1 1 1 0 0 0 1 0 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 0 1 0 0 1\n",
      " 0 0 1]\n",
      "\n",
      "Score :  0.8859649122807017\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the Model\n",
    "\n",
    "print(\"\\nPrediction Probability : \\n\",model.predict_proba(X_test))\n",
    "\n",
    "print(\"\\nPrediction : \",model.predict(X_test))\n",
    "\n",
    "print(\"\\nScore : \",model.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Confusion Matrix : \n",
      " [[35 12]\n",
      " [ 1 66]]\n"
     ]
    }
   ],
   "source": [
    "# Print Confusion Matrix\n",
    "\n",
    "print(\"\\nConfusion Matrix : \\n\",confusion_matrix(y_test, model.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Confusion Matrix \n",
    "\n",
    "cm = confusion_matrix(y_test, model.predict(X_test))\n",
    "\n",
    "import seaborn as sn\n",
    "import pandas as pd\n",
    "\n",
    "df_cm = pd.DataFrame(cm, range(2), range(2))\n",
    "plt.figure(figsize=(10,7))\n",
    "sn.set(font_scale=1.4) # for label size\n",
    "sn.heatmap(df_cm, annot=True, annot_kws={\"size\": 16}) # font size\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.74      0.84        47\n",
      "           1       0.85      0.99      0.91        67\n",
      "\n",
      "    accuracy                           0.89       114\n",
      "   macro avg       0.91      0.86      0.88       114\n",
      "weighted avg       0.90      0.89      0.88       114\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print Classification Report\n",
    "\n",
    "print(classification_report(y_test, model.predict(X_test)))"
   ]
  }
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