logistic regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##### Author : Amir Shokri\n",
    "##### github link : https://github.com/amirshnll/Abalone\n",
    "##### dataset link : http://archive.ics.uci.edu/ml/datasets/Abalone\n",
    "##### email : amirsh.nll@gmail.com"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import StandardScaler  \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       0      1      2      3       4       5       6       7\n",
      "0      1  0.455  0.365  0.095  0.5140  0.2245  0.1010  0.1500\n",
      "1      1  0.350  0.265  0.090  0.2255  0.0995  0.0485  0.0700\n",
      "2     -1  0.530  0.420  0.135  0.6770  0.2565  0.1415  0.2100\n",
      "3      1  0.440  0.365  0.125  0.5160  0.2155  0.1140  0.1550\n",
      "4      0  0.330  0.255  0.080  0.2050  0.0895  0.0395  0.0550\n",
      "...   ..    ...    ...    ...     ...     ...     ...     ...\n",
      "4172  -1  0.565  0.450  0.165  0.8870  0.3700  0.2390  0.2490\n",
      "4173   1  0.590  0.440  0.135  0.9660  0.4390  0.2145  0.2605\n",
      "4174   1  0.600  0.475  0.205  1.1760  0.5255  0.2875  0.3080\n",
      "4175  -1  0.625  0.485  0.150  1.0945  0.5310  0.2610  0.2960\n",
      "4176   1  0.710  0.555  0.195  1.9485  0.9455  0.3765  0.4950\n",
      "\n",
      "[4177 rows x 8 columns]\n",
      "0       15\n",
      "1        7\n",
      "2        9\n",
      "3       10\n",
      "4        7\n",
      "        ..\n",
      "4172    11\n",
      "4173    10\n",
      "4174     9\n",
      "4175    10\n",
      "4176    12\n",
      "Name: 8, Length: 4177, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#read file\n",
    "df = pd.read_csv(\"D:\\\\abalone.txt\", header=None)\n",
    "for char in df:\n",
    "    df = df.replace('M','1')\n",
    "    df = df.replace('F','-1')\n",
    "    df = df.replace('I','0')\n",
    "df\n",
    "\n",
    "#separate the feature columns from the target column.\n",
    "features = [0,1,2,3,4,5,6,7]\n",
    "X = df[features]\n",
    "y = df[8]\n",
    "print(X)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#separate the Training data and Test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,random_state=1, test_size=0.2)\n",
    "# Feature scaling\n",
    "scaler = StandardScaler()  \n",
    "scaler.fit(X_train)\n",
    "X_train = scaler.transform(X_train)  \n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=2000, random_state=0, solver='newton-cg')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finally for the Logistic Regression\n",
    "lgisticRegr = LogisticRegression(solver='newton-cg', random_state=0 ,max_iter=2000)\n",
    "lgisticRegr.fit(X_train, y_train.values.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 8  8  7  8  9  6 11  9 11  8 10  5  9 11  7  9 10 11 10  9  8 10  7 10\n",
      "  9 11 11  9 11 11 10  9 10  9  6  8 10 13 13 10 10 11 11 10  8  8 11  9\n",
      " 10 10  9  8 10  5 11  9  9  9  8  7  8  8 11  9  8  8 10 10  9  8  9 11\n",
      "  6  9  8  7 11 10  9 10  7  8 10  8  9  8 10  7 11  9 13  8  8 11  6  8\n",
      "  9 12  6 10  8 10  7  8 13  8 10  9 11  9  7  9  9  7  6 10  8  7 10 11\n",
      "  9  7  7 13  8 11 10 10  8 10  8  7  8  8  9  7  9 13  8  9  7  9  8 10\n",
      "  8  9 10 11  9  8 10  6  9 10  8  9 10  9  9 11  9 10 13  6  8  8  9 10\n",
      "  8 13  6 10 11  8  8 10 10 10  7  8 10  9  8  7  7  9 11  7 10 11  6  9\n",
      "  6 11  8 11  7  4  7  9  4  9 11  8  8  8  9  8  8  7 10  8 10  8  8  8\n",
      " 10  9 11  9  8  8 10  9  8  9 10  9 10  9  9  8 10 11  8  5 11 10 10  9\n",
      " 13 10  9  9  7 11 10  7  8 10 10 11 11 10  5 13 10 11  7 10 10 10  7  9\n",
      "  8  9  9  9  8  9  8 13 11  8 11 13  7  8 10 11  9  8 10  6  6  7  5  6\n",
      "  6  9  7  8 10  9  8  9  7 10 10  9 11  8  8 11 11  9 13  8  8  8  8 11\n",
      " 10  8  7 16 11 11  8  5 10  9 10 10  8 11  8  9  9  9 10  8 10  7  8  8\n",
      " 10  4 11 16  6  7 11  9  9 10 18 10 10  9  9  4  9  8  5  9  8  9 13  9\n",
      "  9  5  9  9  8 10  6  8 11  6  9 10 11 10  9  9  8  9  8  4  9  7  6 13\n",
      " 11  7  8 10  8  9  7 10 10  8  9  7  8  8  9 16  9  7  9  9 10  7  9 13\n",
      "  8 10  9 10  9  8 16  7  9  9  8  8  9  9 11  8 13  4  6 13  9 10 10  8\n",
      " 10  8  8 10  9  8  8 10 11  8  8  9 10  7  9 11  8  8 11  9  4  7  8 11\n",
      "  9  8 11 13 10  7  9 11  8 11 10 11 10  9 16  9  6  8 10 10  9 10 11  4\n",
      "  7  7 10 10  7  8  7  9  9 11  8  9  8  7  5  7  7  9  9  8 11  6  9  8\n",
      " 11 11  9  9  9 10 16  9 10  7  6  7  7 10  9  8  8 16  9 13  8 10  9  5\n",
      " 11 10  9 11  9 11 10 10  9 11  7  7  9 11  9  9  6  9  8  8  9  9  9 11\n",
      "  9 11 10  8  8 10  8  8  9  7  8  9  8 10  9 10 10  8 10  7 10  8 10  8\n",
      "  8 11  8  9 11  9 10  4  8 12  8  9  4 13  9 18  8 10  8  6  9 10  4 10\n",
      "  9  8 10  8  7 10 11 11 10 11  8 11  9  7 11  7  7  9  9  8  9 11  7  9\n",
      "  9  9  8  9  8  7  6  8  9 10  9  6 11  6  8  9  9  5  9  8  8 11  7 11\n",
      "  8  7 13  9 10 11  8 13  8  9 11  7 10  9  8  9  9  9  9  7  5  6  9  9\n",
      "  6  7  9  9  9 10  7 10  6  9  9  7  8  7 10  7  8 10 10  9 11  9  8  9\n",
      "  7  8  9  5 11  7  7 11 10 11  9  9  7  7  7  9  7 18  9 10  8 16  8  9\n",
      "  8 10 10  9  9 11 11  9 10  8  9 13  8  8  9 10 10  9 10  8  9  8  9  9\n",
      " 10 10  8 10  7  8  7 10 10  7  9  8  7  7 10  5  7  7  8  8 10  7  9  8\n",
      "  8 10  9  7 10 11 13  7  7  8  8  8  9 11  8  9 11 11 10 10 10 13  9 13\n",
      "  9 10  8 11 10  5  7 10 10 10 11 10  7  7  9 11 10  9  7  8  9  6  9  6\n",
      "  8  9  7  9 13 13  7 10 11  6  9 10  8  6 11  9 11  9 11 11]\n"
     ]
    }
   ],
   "source": [
    "#In the prediction step, the model is used to predict the response for given data.\n",
    "predictions = lgisticRegr.predict(X_test)\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
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      " [ 0  0  6  4  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  1  8  6  5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  1  2 16 23  9  2  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  1  9 27 36  6  1  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  1 18 46 32  7  2  0  0  0  0  1  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  1  9 38 51 26  7  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  2  9 20 49 36 24  1  1  0  0  1  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0 11 26 28 24  0  2  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  3 10 14 16 14  0  5  0  0  1  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  2  1  9  9 14  1  3  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  1  4  8  5  0  5  0  0  1  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  4 10  4  0  3  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  3  2  1  0  2  0  0  1  0  1  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  3  4  2  0  2  0  0  0  0  1  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  2  0  5  3  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  1  0  3  0  0  1  0  0  2  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  1  0  3  0  0  0  0  0  0  0  0  0  0]\n",
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      " [ 0  0  0  0  0  0  0  0  0  0  0  2  0  0  0  0  0  0  0  0  0  0]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           2       0.00      0.00      0.00         1\n",
      "           3       0.00      0.00      0.00         2\n",
      "           4       0.55      0.55      0.55        11\n",
      "           5       0.53      0.40      0.46        20\n",
      "           6       0.46      0.30      0.36        53\n",
      "           7       0.28      0.34      0.31        80\n",
      "           8       0.26      0.43      0.33       107\n",
      "           9       0.25      0.39      0.30       132\n",
      "          10       0.23      0.25      0.24       143\n",
      "          11       0.24      0.26      0.25        91\n",
      "          12       0.00      0.00      0.00        63\n",
      "          13       0.10      0.08      0.09        39\n",
      "          14       0.00      0.00      0.00        24\n",
      "          15       0.00      0.00      0.00        21\n",
      "          16       0.12      0.10      0.11        10\n",
      "          17       0.00      0.00      0.00        12\n",
      "          18       0.00      0.00      0.00        10\n",
      "          19       0.00      0.00      0.00         7\n",
      "          20       0.00      0.00      0.00         4\n",
      "          21       0.00      0.00      0.00         3\n",
      "          22       0.00      0.00      0.00         1\n",
      "          23       0.00      0.00      0.00         2\n",
      "\n",
      "    accuracy                           0.26       836\n",
      "   macro avg       0.14      0.14      0.14       836\n",
      "weighted avg       0.22      0.26      0.23       836\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "# Last thing: evaluation of algorithm performance in classifying \n",
    "cnf_matrix =confusion_matrix(y_test,predictions)\n",
    "print(confusion_matrix(y_test,predictions))  \n",
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 257.44, 'Predicted label')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create heatmap\n",
    "class_names=[0,1] \n",
    "fig, ax = plt.subplots()\n",
    "tick_marks = np.arange(len(class_names))\n",
    "plt.xticks(tick_marks, class_names)\n",
    "plt.yticks(tick_marks, class_names)\n",
    "sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n",
    "ax.xaxis.set_label_position(\"top\")\n",
    "plt.tight_layout()\n",
    "plt.title('Confusion matrix', y=1.1)\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}