ml_model_over_small_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width           Class\n",
       "0             5.1          3.5           1.4          0.2     Iris-setosa\n",
       "1             4.9          3.0           1.4          0.2     Iris-setosa\n",
       "2             4.7          3.2           1.3          0.2     Iris-setosa\n",
       "3             4.6          3.1           1.5          0.2     Iris-setosa\n",
       "4             5.0          3.6           1.4          0.2     Iris-setosa\n",
       "..            ...          ...           ...          ...             ...\n",
       "145           6.7          3.0           5.2          2.3  Iris-virginica\n",
       "146           6.3          2.5           5.0          1.9  Iris-virginica\n",
       "147           6.5          3.0           5.2          2.0  Iris-virginica\n",
       "148           6.2          3.4           5.4          2.3  Iris-virginica\n",
       "149           5.9          3.0           5.1          1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import accuracy_score\n",
    "import seaborn as sns\n",
    "df = pd.read_csv('iris.csv', header=None, names=['sepal_length', 'sepal_width', 'petal_length', 'petal_width','Class'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fd1ad6ca340>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 900x900 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "df['Class'] = le.fit_transform(df['Class'])\n",
    "sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((100, 4), (100,), (50, 4), (50,))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop('Class', axis = 1)\n",
    "y = df['Class']\n",
    "X_train,X_test,y_train,y_test = train_test_split(X, y, test_size=0.33, random_state=42, stratify=y)\n",
    "X_train.shape, y_train.shape, X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy of KNN:  0.96\n",
      "Training Accuracy of LogisticRegression:  0.97\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression()\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier()\n",
    "lr.fit(X_train,y_train)\n",
    "knn.fit(X_train,y_train)\n",
    "print(\"Training Accuracy of KNN: \", knn.score(X_train,y_train))\n",
    "print(\"Training Accuracy of LogisticRegression: \",lr.score(X_train,y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Accuracy KNN :  0.98\n",
      "Testing Accuracy LogisticRegresssion:  0.94\n"
     ]
    }
   ],
   "source": [
    "y_lr = lr.predict(X_test)\n",
    "y_knn = knn.predict(X_test)\n",
    "print(\"Testing Accuracy KNN : \", accuracy_score(y_knn, y_test))\n",
    "print(\"Testing Accuracy LogisticRegresssion: \", accuracy_score(y_lr, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "      <td>0.819232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width       Class\n",
       "count    150.000000   150.000000    150.000000   150.000000  150.000000\n",
       "mean       5.843333     3.054000      3.758667     1.198667    1.000000\n",
       "std        0.828066     0.433594      1.764420     0.763161    0.819232\n",
       "min        4.300000     2.000000      1.000000     0.100000    0.000000\n",
       "25%        5.100000     2.800000      1.600000     0.300000    0.000000\n",
       "50%        5.800000     3.000000      4.350000     1.300000    1.000000\n",
       "75%        6.400000     3.300000      5.100000     1.800000    2.000000\n",
       "max        7.900000     4.400000      6.900000     2.500000    2.000000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Petal Width')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, figsize=(15, 12), sharey=True)\n",
    "fig.suptitle('Iris Data')\n",
    "\n",
    "sns.boxplot(df['Class'], df['sepal_length'], ax=axes[0,0])\n",
    "axes[0,0].set_title('Sepal Length')\n",
    "\n",
    "sns.boxplot(df['Class'], df['sepal_width'], ax=axes[0,1])\n",
    "axes[0,1].set_title('Sepal Width')\n",
    "\n",
    "sns.boxplot(df['Class'], df['petal_length'], ax=axes[1,0])\n",
    "axes[1,0].set_title('Petal Length')\n",
    "\n",
    "sns.boxplot(df['Class'], df['petal_width'], ax=axes[1,1])\n",
    "axes[1,1].set_title('Petal Width')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Tree-based algorithms like Random Forest and Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy of Decision Tree:  1.0\n",
      "Training Accuracy of  Random Forest:  1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dt = DecisionTreeClassifier()\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier()\n",
    "dt.fit(X_train,y_train)\n",
    "rf.fit(X_train,y_train)\n",
    "print(\"Training Accuracy of Decision Tree: \", dt.score(X_train,y_train))\n",
    "print(\"Training Accuracy of  Random Forest: \",rf.score(X_train,y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Accuracy of Decision Tree :  0.98\n",
      "Testing Accuracy of Random Forest :  0.92\n"
     ]
    }
   ],
   "source": [
    "y_dt = dt.predict(X_test)\n",
    "y_rf = rf.predict(X_test)\n",
    "print(\"Testing Accuracy of Decision Tree : \", accuracy_score(y_dt, y_test))\n",
    "print(\"Testing Accuracy of Random Forest : \", accuracy_score(y_rf, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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"
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 },
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}