{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fruit_label</th>\n",
       "      <th>fruit_name</th>\n",
       "      <th>fruit_subtype</th>\n",
       "      <th>mass</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>color_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>192</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>180</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>176</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>86</td>\n",
       "      <td>6.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>84</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fruit_label fruit_name fruit_subtype  mass  width  height  color_score\n",
       "0            1      apple  granny_smith   192    8.4     7.3         0.55\n",
       "1            1      apple  granny_smith   180    8.0     6.8         0.59\n",
       "2            1      apple  granny_smith   176    7.4     7.2         0.60\n",
       "3            2   mandarin      mandarin    86    6.2     4.7         0.80\n",
       "4            2   mandarin      mandarin    84    6.0     4.6         0.79"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (15, 15)\n",
    "fruits = pd.read_table('fruits.txt')\n",
    "fruits.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(59, 7)\n"
     ]
    }
   ],
   "source": [
    "print(fruits.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple' 'mandarin' 'orange' 'lemon']\n"
     ]
    }
   ],
   "source": [
    "print(fruits['fruit_name'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fruit_name\n",
       "apple       19\n",
       "lemon       16\n",
       "mandarin     5\n",
       "orange      19\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fruits.groupby('fruit_name').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = _"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='fruit_name'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_removed = fruits.drop('fruit_label', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fruit_name</th>\n",
       "      <th>fruit_subtype</th>\n",
       "      <th>mass</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>color_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>192</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>180</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>176</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mandarin</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>86</td>\n",
       "      <td>6.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mandarin</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>84</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  fruit_name fruit_subtype  mass  width  height  color_score\n",
       "0      apple  granny_smith   192    8.4     7.3         0.55\n",
       "1      apple  granny_smith   180    8.0     6.8         0.59\n",
       "2      apple  granny_smith   176    7.4     7.2         0.60\n",
       "3   mandarin      mandarin    86    6.2     4.7         0.80\n",
       "4   mandarin      mandarin    84    6.0     4.6         0.79"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_removed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mass              Axes(0.125,0.536818;0.352273x0.343182)\n",
       "width          Axes(0.547727,0.536818;0.352273x0.343182)\n",
       "height               Axes(0.125,0.125;0.352273x0.343182)\n",
       "color_score       Axes(0.547727,0.125;0.352273x0.343182)\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_removed.plot(kind='box', subplots=True, layout=(2,2), title='variables')\n",
    "# plt.savefig('fruits_box')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_removed.hist(bins=30)\n",
    "plt.savefig('fruits_hist')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/b3/cjvms6c10k77tgv6k_rw3jbh0000gn/T/ipykernel_36403/663915837.py:7: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.\n",
      "  cmap = cm.get_cmap('gnuplot')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import cm\n",
    "\n",
    "feature_names = ['mass', 'width', 'height', 'color_score']\n",
    "X = fruits[feature_names]\n",
    "y = fruits['fruit_label']\n",
    "\n",
    "cmap = cm.get_cmap('gnuplot')\n",
    "\n",
    "scatter = pd.plotting.scatter_matrix(X, c = y, marker = 'o', s=40, hist_kwds={'bins':15}, cmap = cmap)\n",
    "\n",
    "plt.savefig('fruits_scatter_matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mass</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>color_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>192</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>180</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>86</td>\n",
       "      <td>6.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mass  width  height  color_score\n",
       "0   192    8.4     7.3         0.55\n",
       "1   180    8.0     6.8         0.59\n",
       "2   176    7.4     7.2         0.60\n",
       "3    86    6.2     4.7         0.80\n",
       "4    84    6.0     4.6         0.79"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.27857143, 0.41176471, 0.49230769, 0.72972973],\n",
       "       [0.35      , 0.44117647, 0.93846154, 0.45945946],\n",
       "       [0.        , 0.        , 0.        , 0.7027027 ],\n",
       "       [0.27142857, 0.52941176, 0.50769231, 0.37837838],\n",
       "       [0.31428571, 0.41176471, 0.46153846, 0.67567568]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.75\n",
      "Accuracy on test set: 0.47\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "\n",
    "logreg.fit(X_train, y_train)\n",
    "\n",
    "score_on_train = logreg.score(X_train, y_train)\n",
    "score_on_test = logreg.score(X_test, y_test)\n",
    "\n",
    "print(f'Accuracy on training set: {score_on_train:.2f}')\n",
    "print(f'Accuracy on test set: {score_on_test:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 1.00\n",
      "Accuracy on test set: 0.87\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "clf = DecisionTreeClassifier().fit(X_train, y_train)\n",
    "\n",
    "\n",
    "print(f'Accuracy on training set: {clf.score(X_train, y_train):.2f}')\n",
    "print(f'Accuracy on test set: {clf.score(X_test, y_test):.2f}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.95\n",
      "Accuracy on test set: 1.00\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "print(f'Accuracy on training set: {knn.score(X_train, y_train):.2f}')\n",
    "print(f'Accuracy on test set: {knn.score(X_test, y_test):.2f}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.91\n",
      "Accuracy on test set: 0.80\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svm = SVC()\n",
    "svm.fit(X_train, y_train)\n",
    "\n",
    "print(f'Accuracy on training set: {svm.score(X_train, y_train):.2f}')\n",
    "print(f'Accuracy on test set: {svm.score(X_test, y_test):.2f}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4 0 0 0]\n",
      " [0 1 0 0]\n",
      " [0 0 8 0]\n",
      " [0 0 0 2]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       1.00      1.00      1.00         4\n",
      "           2       1.00      1.00      1.00         1\n",
      "           3       1.00      1.00      1.00         8\n",
      "           4       1.00      1.00      1.00         2\n",
      "\n",
      "    accuracy                           1.00        15\n",
      "   macro avg       1.00      1.00      1.00        15\n",
      "weighted avg       1.00      1.00      1.00        15\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "pred = knn.predict(X_test)\n",
    "\n",
    "print(confusion_matrix(y_test, pred))\n",
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x2829c4ee0>,\n",
       "  <matplotlib.axis.XTick at 0x2829c4eb0>,\n",
       "  <matplotlib.axis.XTick at 0x28298ae00>,\n",
       "  <matplotlib.axis.XTick at 0x282c914b0>,\n",
       "  <matplotlib.axis.XTick at 0x282c91f60>],\n",
       " [Text(0, 0, '0'),\n",
       "  Text(5, 0, '5'),\n",
       "  Text(10, 0, '10'),\n",
       "  Text(15, 0, '15'),\n",
       "  Text(20, 0, '20')])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "k_range = range(1, 20)\n",
    "scores = []\n",
    "for k in k_range:\n",
    "    knn = KNeighborsClassifier(n_neighbors = k)\n",
    "    knn.fit(X_train, y_train)\n",
    "    scores.append(knn.score(X_test, y_test))\n",
    "    \n",
    "plt.figure()\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('accuracy')\n",
    "plt.scatter(k_range, scores)\n",
    "plt.xticks([0,5,10,15,20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}