fruit_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fruit prediction by using its attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = 'D:\\Fruits.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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",
       "  </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"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(59, 7)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple' 'mandarin' 'orange' 'lemon']\n"
     ]
    }
   ],
   "source": [
    "print(df['fruit_name'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fruit_name\n",
      "apple       19\n",
      "lemon       16\n",
      "mandarin     5\n",
      "orange      19\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.groupby('fruit_name').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26693d4d7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.countplot(df['fruit_name'], label=\"Count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2669218f4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.drop('fruit_label', axis=1).plot(kind='box', subplots=True,\n",
    "layout=(2,2), sharex=False, sharey=False, figsize=(9,9), \n",
    "                                          title='Box Plot for each input variable')\n",
    "plt.savefig('fruits_box')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like perhaps color_score has a near Gaussian distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pylab as pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x266942cd550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.drop('fruit_label', axis=1).hist(bins=30, figsize=(9,9))\n",
    "pl.suptitle(\"Histogram for each numeric input variable\")\n",
    "plt.savefig('fruits_hist')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.tools.plotting import scatter_matrix\n",
    "from matplotlib import cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Baba\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: FutureWarning: pandas.scatter_matrix is deprecated. Use pandas.plotting.scatter_matrix instead\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x266946bfef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_names = (['mass','height','color_score'])\n",
    "X = df[feature_names]\n",
    "y = df['fruit_label']\n",
    "\n",
    "cmap = cm.get_cmap('gnuplot')\n",
    "scatter = pd.scatter_matrix(X, c = y, marker = 'o', s= 40, hist_kwds ={'bins':15}, figsize=(9,9), cmap = cmap)\n",
    "plt.suptitle('Scatter-matrix for each input variable')\n",
    "plt.savefig('fruits_scatter_matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>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>count</th>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.542373</td>\n",
       "      <td>163.118644</td>\n",
       "      <td>7.105085</td>\n",
       "      <td>7.693220</td>\n",
       "      <td>0.762881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.208048</td>\n",
       "      <td>55.018832</td>\n",
       "      <td>0.816938</td>\n",
       "      <td>1.361017</td>\n",
       "      <td>0.076857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>0.810000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>362.000000</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>0.930000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fruit_label        mass     width      height  color_score\n",
       "count    59.000000   59.000000  59.000000  59.000000    59.000000\n",
       "mean      2.542373  163.118644   7.105085   7.693220     0.762881\n",
       "std       1.208048   55.018832   0.816938   1.361017     0.076857\n",
       "min       1.000000   76.000000   5.800000   4.000000     0.550000\n",
       "25%       1.000000  140.000000   6.600000   7.200000     0.720000\n",
       "50%       3.000000  158.000000   7.200000   7.600000     0.750000\n",
       "75%       4.000000  177.000000   7.500000   8.200000     0.810000\n",
       "max       4.000000  362.000000   9.600000  10.500000     0.930000"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Training and Test Sets and Apply Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Logistic regression classifier on training set: 0.59\n",
      "Accuracy of Logistic regression classifier on test set: 0.40\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, y_train)\n",
    "\n",
    "print('Accuracy of Logistic regression classifier on training set: {:.2f}'\n",
    "     .format(logreg.score(X_train, y_train)))\n",
    "print('Accuracy of Logistic regression classifier on test set: {:.2f}'\n",
    "     .format(logreg.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Decision Tree classifier on training set: 0.59\n",
      "Accuracy of Decision Tree classifier on test set: 0.40\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "clf = DecisionTreeClassifier().fit(X_train, y_train)\n",
    "print('Accuracy of Decision Tree classifier on training set: {:.2f}'\n",
    "     .format(logreg.score(X_train, y_train)))\n",
    "print('Accuracy of Decision Tree classifier on test set: {:.2f}'\n",
    "     .format(logreg.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-Nearest Neigbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of K-NN classifier on training set: 0.93\n",
      "Accuracy of K-NN classifier on test set: 0.87\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train, y_train)\n",
    "print('Accuracy of K-NN classifier on training set: {:.2f}'\n",
    "     .format(knn.score(X_train, y_train)))\n",
    "print('Accuracy of K-NN classifier on test set: {:.2f}'\n",
    "     .format(knn.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is the better prediction than above two algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gaussian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of GNB classifier on training set: 0.93\n",
      "Accuracy of GNB classifier on test set: 0.87\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(X_train, y_train)\n",
    "print('Accuracy of GNB classifier on training set: {:.2f}'\n",
    "     .format(knn.score(X_train, y_train)))\n",
    "print('Accuracy of GNB classifier on test set: {:.2f}'\n",
    "     .format(knn.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This algorithm also predicts better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4 0 0 0]\n",
      " [0 1 0 0]\n",
      " [2 0 6 0]\n",
      " [0 0 0 2]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.67      1.00      0.80         4\n",
      "          2       1.00      1.00      1.00         1\n",
      "          3       1.00      0.75      0.86         8\n",
      "          4       1.00      1.00      1.00         2\n",
      "\n",
      "avg / total       0.91      0.87      0.87        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",
    "print(confusion_matrix(y_test,pred))\n",
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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