LogisticRegression.ipynb

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      "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names'])\n",
      "(178, 13)\n",
      ".. _wine_dataset:\n",
      "\n",
      "Wine recognition dataset\n",
      "------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      ":Number of Instances: 178\n",
      ":Number of Attributes: 13 numeric, predictive attributes and the class\n",
      ":Attribute Information:\n",
      "    - Alcohol\n",
      "    - Malic acid\n",
      "    - Ash\n",
      "    - Alcalinity of ash\n",
      "    - Magnesium\n",
      "    - Total phenols\n",
      "    - Flavanoids\n",
      "    - Nonflavanoid phenols\n",
      "    - Proanthocyanins\n",
      "    - Color intensity\n",
      "    - Hue\n",
      "    - OD280/OD315 of diluted wines\n",
      "    - Proline\n",
      "    - class:\n",
      "        - class_0\n",
      "        - class_1\n",
      "        - class_2\n",
      "\n",
      ":Summary Statistics:\n",
      "\n",
      "============================= ==== ===== ======= =====\n",
      "                                Min   Max   Mean     SD\n",
      "============================= ==== ===== ======= =====\n",
      "Alcohol:                      11.0  14.8    13.0   0.8\n",
      "Malic Acid:                   0.74  5.80    2.34  1.12\n",
      "Ash:                          1.36  3.23    2.36  0.27\n",
      "Alcalinity of Ash:            10.6  30.0    19.5   3.3\n",
      "Magnesium:                    70.0 162.0    99.7  14.3\n",
      "Total Phenols:                0.98  3.88    2.29  0.63\n",
      "Flavanoids:                   0.34  5.08    2.03  1.00\n",
      "Nonflavanoid Phenols:         0.13  0.66    0.36  0.12\n",
      "Proanthocyanins:              0.41  3.58    1.59  0.57\n",
      "Colour Intensity:              1.3  13.0     5.1   2.3\n",
      "Hue:                          0.48  1.71    0.96  0.23\n",
      "OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71\n",
      "Proline:                       278  1680     746   315\n",
      "============================= ==== ===== ======= =====\n",
      "\n",
      ":Missing Attribute Values: None\n",
      ":Class Distribution: class_0 (59), class_1 (71), class_2 (48)\n",
      ":Creator: R.A. Fisher\n",
      ":Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      ":Date: July, 1988\n",
      "\n",
      "This is a copy of UCI ML Wine recognition datasets.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\n",
      "\n",
      "The data is the results of a chemical analysis of wines grown in the same\n",
      "region in Italy by three different cultivators. There are thirteen different\n",
      "measurements taken for different constituents found in the three types of\n",
      "wine.\n",
      "\n",
      "Original Owners:\n",
      "\n",
      "Forina, M. et al, PARVUS -\n",
      "An Extendible Package for Data Exploration, Classification and Correlation.\n",
      "Institute of Pharmaceutical and Food Analysis and Technologies,\n",
      "Via Brigata Salerno, 16147 Genoa, Italy.\n",
      "\n",
      "Citation:\n",
      "\n",
      "Lichman, M. (2013). UCI Machine Learning Repository\n",
      "[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\n",
      "School of Information and Computer Science.\n",
      "\n",
      "|details-start|\n",
      "**References**\n",
      "|details-split|\n",
      "\n",
      "(1) S. Aeberhard, D. Coomans and O. de Vel,\n",
      "Comparison of Classifiers in High Dimensional Settings,\n",
      "Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of\n",
      "Mathematics and Statistics, James Cook University of North Queensland.\n",
      "(Also submitted to Technometrics).\n",
      "\n",
      "The data was used with many others for comparing various\n",
      "classifiers. The classes are separable, though only RDA\n",
      "has achieved 100% correct classification.\n",
      "(RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data))\n",
      "(All results using the leave-one-out technique)\n",
      "\n",
      "(2) S. Aeberhard, D. Coomans and O. de Vel,\n",
      "\"THE CLASSIFICATION PERFORMANCE OF RDA\"\n",
      "Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of\n",
      "Mathematics and Statistics, James Cook University of North Queensland.\n",
      "(Also submitted to Journal of Chemometrics).\n",
      "\n",
      "|details-end|\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "data=datasets.load_wine()\n",
    "\n",
    "print(data.keys())\n",
    "print(data['data'].shape)\n",
    "print(data['DESCR'])\n",
    "# print(data['data'])\n",
    "# print(data['target'])\n",
    "# print(data['target_names'])\n",
    "# print(data['feature_names'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We will be using proline to classify the wine is class0 or above,\n",
    "### We used the proline as it is the most important feature in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(178, 13)\n",
      "(178,)\n",
      "['class_0' 'class_1' 'class_2']\n",
      "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]\n"
     ]
    }
   ],
   "source": [
    "wine=data['data']\n",
    "target=data['target']\n",
    "target_names=data['target_names']\n",
    "\n",
    "print(wine.shape)\n",
    "print(target.shape)\n",
    "print(target_names)\n",
    "x=wine[:,12:]\n",
    "y=(target==0).astype(int)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
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       "\n",
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       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf=LogisticRegression()\n",
    "clf.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=clf.predict(x)\n",
    "example_pred=clf.predict(([[800]]))\n",
    "print(example_pred)\n",
    "\n",
    "print(\"The prediction\")\n",
    "for i in range(predictions.shape[0]):\n",
    "    if predictions[i]==0:\n",
    "        print(\"Class 0\")\n",
    "    else:\n",
    "        print(\"Above Class 0\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.9157303370786517\n",
      "[[1065.]\n",
      " [1050.]\n",
      " [1185.]\n",
      " [1480.]\n",
      " [ 735.]\n",
      " [1450.]\n",
      " [1290.]\n",
      " [1295.]\n",
      " [1045.]\n",
      " [1045.]\n",
      " [1510.]\n",
      " [1280.]\n",
      " [1320.]\n",
      " [1150.]\n",
      " [1547.]\n",
      " [1310.]\n",
      " [1280.]\n",
      " [1130.]\n",
      " [1680.]\n",
      " [ 845.]\n",
      " [ 780.]\n",
      " [ 770.]\n",
      " [1035.]\n",
      " [1015.]\n",
      " [ 845.]\n",
      " [ 830.]\n",
      " [1195.]\n",
      " [1285.]\n",
      " [ 915.]\n",
      " [1035.]\n",
      " [1285.]\n",
      " [1515.]\n",
      " [ 990.]\n",
      " [1235.]\n",
      " [1095.]\n",
      " [ 920.]\n",
      " [ 880.]\n",
      " [1105.]\n",
      " [1020.]\n",
      " [ 760.]\n",
      " [ 795.]\n",
      " [1035.]\n",
      " [1095.]\n",
      " [ 680.]\n",
      " [ 885.]\n",
      " [1080.]\n",
      " [1065.]\n",
      " [ 985.]\n",
      " [1060.]\n",
      " [1260.]\n",
      " [1150.]\n",
      " [1265.]\n",
      " [1190.]\n",
      " [1375.]\n",
      " [1060.]\n",
      " [1120.]\n",
      " [ 970.]\n",
      " [1270.]\n",
      " [1285.]\n",
      " [ 520.]\n",
      " [ 680.]\n",
      " [ 450.]\n",
      " [ 630.]\n",
      " [ 420.]\n",
      " [ 355.]\n",
      " [ 678.]\n",
      " [ 502.]\n",
      " [ 510.]\n",
      " [ 750.]\n",
      " [ 718.]\n",
      " [ 870.]\n",
      " [ 410.]\n",
      " [ 472.]\n",
      " [ 985.]\n",
      " [ 886.]\n",
      " [ 428.]\n",
      " [ 392.]\n",
      " [ 500.]\n",
      " [ 750.]\n",
      " [ 463.]\n",
      " [ 278.]\n",
      " [ 714.]\n",
      " [ 630.]\n",
      " [ 515.]\n",
      " [ 520.]\n",
      " [ 450.]\n",
      " [ 495.]\n",
      " [ 562.]\n",
      " [ 680.]\n",
      " [ 625.]\n",
      " [ 480.]\n",
      " [ 450.]\n",
      " [ 495.]\n",
      " [ 290.]\n",
      " [ 345.]\n",
      " [ 937.]\n",
      " [ 625.]\n",
      " [ 428.]\n",
      " [ 660.]\n",
      " [ 406.]\n",
      " [ 710.]\n",
      " [ 562.]\n",
      " [ 438.]\n",
      " [ 415.]\n",
      " [ 672.]\n",
      " [ 315.]\n",
      " [ 510.]\n",
      " [ 488.]\n",
      " [ 312.]\n",
      " [ 680.]\n",
      " [ 562.]\n",
      " [ 325.]\n",
      " [ 607.]\n",
      " [ 434.]\n",
      " [ 385.]\n",
      " [ 407.]\n",
      " [ 495.]\n",
      " [ 345.]\n",
      " [ 372.]\n",
      " [ 564.]\n",
      " [ 625.]\n",
      " [ 465.]\n",
      " [ 365.]\n",
      " [ 380.]\n",
      " [ 380.]\n",
      " [ 378.]\n",
      " [ 352.]\n",
      " [ 466.]\n",
      " [ 342.]\n",
      " [ 580.]\n",
      " [ 630.]\n",
      " [ 530.]\n",
      " [ 560.]\n",
      " [ 600.]\n",
      " [ 650.]\n",
      " [ 695.]\n",
      " [ 720.]\n",
      " [ 515.]\n",
      " [ 580.]\n",
      " [ 590.]\n",
      " [ 600.]\n",
      " [ 780.]\n",
      " [ 520.]\n",
      " [ 550.]\n",
      " [ 855.]\n",
      " [ 830.]\n",
      " [ 415.]\n",
      " [ 625.]\n",
      " [ 650.]\n",
      " [ 550.]\n",
      " [ 500.]\n",
      " [ 480.]\n",
      " [ 425.]\n",
      " [ 675.]\n",
      " [ 640.]\n",
      " [ 725.]\n",
      " [ 480.]\n",
      " [ 880.]\n",
      " [ 660.]\n",
      " [ 620.]\n",
      " [ 520.]\n",
      " [ 680.]\n",
      " [ 570.]\n",
      " [ 675.]\n",
      " [ 615.]\n",
      " [ 520.]\n",
      " [ 695.]\n",
      " [ 685.]\n",
      " [ 750.]\n",
      " [ 630.]\n",
      " [ 510.]\n",
      " [ 470.]\n",
      " [ 660.]\n",
      " [ 740.]\n",
      " [ 750.]\n",
      " [ 835.]\n",
      " [ 840.]\n",
      " [ 560.]]\n"
     ]
    }
   ],
   "source": [
    "score=clf.score(x,y)\n",
    "print(\"Accuracy: \",score)\n",
    "print(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[278.]\n",
      "[1680.]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "l=np.min(x,axis=0)\n",
    "u=np.max(x,axis=0)\n",
    "\n",
    "print(l)\n",
    "print(u)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9.99897022e-01 1.02978401e-04]\n",
      " [9.99894668e-01 1.05332484e-04]\n",
      " [9.99892260e-01 1.07740376e-04]\n",
      " ...\n",
      " [1.58290341e-06 9.99998417e-01]\n",
      " [1.54752340e-06 9.99998452e-01]\n",
      " [1.51293418e-06 9.99998487e-01]]\n"
     ]
    }
   ],
   "source": [
    "X_new=np.linspace(l,u,1000).reshape(-1,1)\n",
    "X_prob=clf.predict_proba(X_new)\n",
    "print(X_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X_new,X_prob[:,1],\"r-\",label=\"Class 0\")\n",
    "plt.xlabel(\"Proline\")\n",
    "plt.ylabel(\"Probability\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Thus, we will be using the proline to classify the wine is class0 or above,\n",
    "### We also saw the sigmoid function's graph and it is a good fit for the classification problem with accuracy of 0.9157 on the test data"
   ]
  }
 ],
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