Classification_Task3_200020039.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7C5rAxwPGDQf"
   },
   "source": [
    "# Importing useful libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "id": "8qvrslgsF4Mn"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "from pandas import Series, DataFrame\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6aYOLI2BHF6m"
   },
   "source": [
    "## Loading the dataset.\n",
    "The dataset can be found [here](https://github.com/shreedharmalpani/Intro-To-ML-Hello-FOSS/blob/main/iris.csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "id": "_hccks2pF4Mq"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"iris.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "id": "um0L09IOF4Ms"
   },
   "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>species</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>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>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>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>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>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "id": "cbx8gP4zF4Mt"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   sepal_length  150 non-null    float64\n",
      " 1   sepal_width   150 non-null    float64\n",
      " 2   petal_length  150 non-null    float64\n",
      " 3   petal_width   150 non-null    float64\n",
      " 4   species       150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vjH1pAqoJna2"
   },
   "source": [
    "# Data Cleaning & Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PNfSyZF1F4Mu"
   },
   "source": [
    "### 1) Remove unneeded columns\n",
    "### 2) Check for duplicate rows \n",
    "### 2) Check for rows with missing values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "id": "HKFsVhubF4Mx"
   },
   "outputs": [],
   "source": [
    "#code\n",
    "df.isna().sum() # No missing entry\n",
    "df = df.drop_duplicates(keep = 'first')\n",
    "df = df.reset_index(drop = True)\n",
    "# df.info()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L1cnZS5XF4M2"
   },
   "source": [
    "### EDA (Exploratory Data Analysis) with Iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "id": "DjsnhAraF4M7",
    "outputId": "d8d58921-fa2f-4d75-c884-268986342894"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Displaying a scatter plot to show the distribution of Sepal Length vs width the dataset\n",
    "\n",
    "fig = df[df.species == 'setosa'].plot(kind='scatter', x='petal_length', y='petal_width', color='orange', label='Setosa')\n",
    "df[df.species == 'versicolor'].plot(kind='scatter', x='petal_length', y='petal_width', color='blue', label='Versicolor', ax=fig)\n",
    "df[df.species == 'virginica'].plot(kind='scatter', x='petal_length', y='petal_width', color='green', label='Virginica', ax=fig)\n",
    "\n",
    "fig.set_xlabel('Petal Length')\n",
    "fig.set_ylabel('Petal Width')\n",
    "fig.set_title('Petal Length Vs Width')\n",
    "\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(10, 7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "id": "O_eYByFvF4M5",
    "outputId": "b7404bc7-4879-482e-ca9d-8d9680a8f6a7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Display a scatter plot to show the distribution of Sepal Length vs width the dataset (Like previous Petal lenght vs width scatter plot)\n",
    "\n",
    "\n",
    "#code\n",
    "\n",
    "fig = df[df.species == 'setosa'].plot(kind='scatter', x='sepal_length', y='sepal_width', color='orange', label='Setosa')\n",
    "df[df.species == 'versicolor'].plot(kind='scatter', x='sepal_length', y='sepal_width', color='blue', label='Versicolor', ax=fig)\n",
    "df[df.species == 'virginica'].plot(kind='scatter', x='sepal_length', y='sepal_width', color='green', label='Virginica', ax=fig)\n",
    "\n",
    "\n",
    "fig.set_xlabel('Sepal Length')\n",
    "fig.set_ylabel('Sepal Width')\n",
    "fig.set_title('Sepal Length Vs Width')\n",
    "\n",
    "\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(10, 7)\n",
    "plt.show()\n",
    "#example plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "id": "EIh_yKQAF4M6",
    "outputId": "91706df0-4dd1-4a8b-92e7-41e04e6e602d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x20180054970>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300.75x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300.75x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the FacetGrid plot using the seaborn library\n",
    "g = sns.FacetGrid(df, hue='species') #sns.FacetGrid(...)\\ \n",
    "g.map_dataframe(sns.scatterplot, x=\"sepal_length\", y=\"sepal_width\") #.map(...)\\\n",
    "g.add_legend() #.add_legend()\n",
    "\n",
    "h = sns.FacetGrid(df, hue='species') #sns.FacetGrid(...)\\ \n",
    "h.map_dataframe(sns.scatterplot, x=\"petal_length\", y=\"petal_width\") #.map(...)\\\n",
    "h.add_legend() #.add_legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "id": "yadZQxoKF4M8",
    "outputId": "639d4b96-87a7-45d0-f358-ef061d08d230"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:title={'center':'sepal_width'}>], dtype=object)"
      ]
     },
     "execution_count": 100,
     "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"
    },
    {
     "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": [
    "#Plot the distritbution of the features using histgram\n",
    "# g = sns.FacetGrid(df)\n",
    "# g.map_dataframe(sns.histplot, x=\"petal_length\")\n",
    "# g.map_dataframe(sns.histplot, x=\"petal_width\")\n",
    "# g.map_dataframe(sns.histplot, x=\"sepal_length\")\n",
    "# g.map_dataframe(sns.histplot, x=\"sepal_width\")\n",
    "fig1, axes1 = plt.subplots(1, 2)\n",
    "df1 = pd.DataFrame(data= df[\"petal_length\"])\n",
    "df2 = pd.DataFrame(data= df[\"sepal_length\"])\n",
    "fig2, axes2 = plt.subplots(1, 2)\n",
    "df3 = pd.DataFrame(data= df[\"petal_width\"])\n",
    "df4 = pd.DataFrame(data= df[\"sepal_width\"])\n",
    "\n",
    "df1.hist('petal_length', ax=axes1[0])\n",
    "df2.hist('sepal_length', ax=axes1[1])\n",
    "df3.hist('petal_width', ax=axes2[0])\n",
    "df4.hist('sepal_width', ax=axes2[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xb-AFaG3PU0D"
   },
   "source": [
    "## Importing alll the necessary packages to use the various classification algorithms\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "id": "cJVjbgAjF4M_"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression # for Logistic Regression Algorithm\n",
    "from sklearn.model_selection import train_test_split # to split the dataset for training and testing \n",
    "from sklearn import svm # for suport vector machine algorithm\n",
    "from sklearn import metrics # for checking the model accuracy\n",
    "from sklearn.tree import DecisionTreeClassifier # for using DTA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "id": "8LizCSuWF4NA"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(147, 5)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WW5Hp1fFF4NC"
   },
   "source": [
    "Now, when we train any algorithm, the number of features and their correlation plays an important role. If there are features and many of the features are highly correlated, then training an algorithm with all the featues will reduce the accuracy. Thus features selection should be done carefully. This dataset has less featues but still we will see the correlation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "id": "YABeXMklF4ND",
    "outputId": "a212e140-712f-4e6f-f8f4-b6664bf29ff1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "sns.heatmap(df.corr(), annot=True, cmap='cubehelix_r') # draws heatmap with input as correlation matrix calculated by df.corr() \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gsd6QaoaF4NE"
   },
   "source": [
    "Observation--->\n",
    "The Sepal Width and Length are not correlated The Petal Width and Length are highly correlated\n",
    "We will use all the features for training the algorithm and check the accuracy.\n",
    "\n",
    "Then we will use 1 Petal Feature and 1 Sepal Feature to check the accuracy of the algorithm as we are using only 2 features that are not correlated. Thus we can have a variance in the dataset which may help in better accuracy. We will check it later.\n",
    "\n",
    "Steps To Be followed When Applying an Algorithm\n",
    "\n",
    "Split the dataset into training and testing dataset. The testing dataset is generally smaller than training one as it will help in training the model better.\n",
    "\n",
    "Select any algorithm based on the problem (classification or regression) whatever you feel may be good.\n",
    "Then pass the training dataset to the algorithm to train it. We use the .fit() method\n",
    "Then pass the testing data to the trained algorithm to predict the outcome. We use the .predict() method.\n",
    "We then check the accuracy by passing the predicted outcome and the actual output to the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QAD_cNirF4NF"
   },
   "source": [
    "# Splitting The Data into Training And Testing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "id": "ZqSRd9GzF4NF",
    "outputId": "9504d228-e1ff-4c00-a125-63dd9e0f7d67"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(102, 5)\n",
      "(45, 5)\n"
     ]
    }
   ],
   "source": [
    "train, test = train_test_split(df, test_size=0.3) # our main data split into train and test\n",
    "# the attribute test_size=0.3 splits the data into 70% and 30% ratio. train=70% and test=30%\n",
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "id": "yO2J2FpjF4NG"
   },
   "outputs": [],
   "source": [
    "train_X = train[['sepal_length','sepal_width','petal_length','petal_width']] # taking the training data features\n",
    "train_y = train.species # output of the training data\n",
    "\n",
    "test_X = test[['sepal_length','sepal_width','petal_length','petal_length']] # taking test data feature\n",
    "test_y = test.species # output value of the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "id": "yR9D2qgQF4NG"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>125</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
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       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.4</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
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       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>6.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
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       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width\n",
       "125           6.1          3.0           4.9          1.8\n",
       "47            5.0          3.3           1.4          0.2\n",
       "15            5.7          4.4           1.5          0.4\n",
       "52            6.5          2.8           4.6          1.5\n",
       "90            5.8          2.6           4.0          1.2"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "id": "KcGbNGkcF4NH"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_length</th>\n",
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       "  </thead>\n",
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       "      <th>114</th>\n",
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       "      <td>3.0</td>\n",
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       "      <td>5.5</td>\n",
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       "      <th>98</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>5.2</td>\n",
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       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>4.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_length\n",
       "114           6.5          3.0           5.5           5.5\n",
       "98            6.3          3.3           6.0           6.0\n",
       "1             4.9          3.0           1.4           1.4\n",
       "142           6.7          3.0           5.2           5.2\n",
       "36            4.4          3.0           1.3           1.3"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "id": "5sFmts-IF4NI"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "125     virginica\n",
       "47         setosa\n",
       "15         setosa\n",
       "52     versicolor\n",
       "90     versicolor\n",
       "Name: species, dtype: object"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S_w4Me2bF4NL"
   },
   "source": [
    "## Logistic Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "id": "gOQ5JrqrF4NL"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of Logistic Regression is:  0.7333333333333333\n"
     ]
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(train_X, train_y)\n",
    "prediction = model.predict(test_X)\n",
    "print('The accuracy of Logistic Regression is: ', metrics.accuracy_score(prediction, test_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e1NNX-EGF4NJ"
   },
   "source": [
    "## Support Vector Machine SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "id": "zSJmVzqnF4NK"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of Support Vector Machine is:  0.8\n"
     ]
    }
   ],
   "source": [
    "#implement using Support Vector Machine (SVM)\n",
    "model_SVM = svm.SVC(kernel='rbf')\n",
    "model_SVM.fit(train_X, train_y)\n",
    "prediction_SVM = model_SVM.predict(test_X)\n",
    "\n",
    "print('The accuracy of Support Vector Machine is: ', metrics.accuracy_score(prediction_SVM, test_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GWfemKzPF4NN"
   },
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "id": "iRXy3EZIF4NN",
    "outputId": "f470e075-fc92-4f3f-c343-7a8257e2c5d2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of Decision Tree is:  0.4666666666666667\n"
     ]
    }
   ],
   "source": [
    "#implementing using Decision Tree\n",
    "model_DT = DecisionTreeClassifier(random_state=0)\n",
    "model_DT.fit(train_X, train_y)\n",
    "prediction_DT = model_DT.predict(test_X)\n",
    "\n",
    "\n",
    "print('The accuracy of Decision Tree is: ', metrics.accuracy_score(prediction_DT, test_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uB2Co6f_F4NQ"
   },
   "source": [
    "### We used all the features of iris in above models. Now we will use Petals and Sepals Seperately"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1_v6cAZMF4NQ"
   },
   "source": [
    "### Creating Petals And Sepals Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "id": "e1Q-1b9YF4NQ"
   },
   "outputs": [],
   "source": [
    "petal = df[['petal_length','petal_width','species']]\n",
    "sepal = df[['sepal_length','sepal_width','species']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Lv-nab5oF4NQ"
   },
   "source": [
    "### For Iris Petal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "id": "DuOqLUWZF4NQ"
   },
   "outputs": [],
   "source": [
    "train_p,test_p = train_test_split(petal, test_size=0.3, random_state=0) #petals\n",
    "train_x_p = train_p[['petal_width','petal_length']]  # taking the training data's Petal features\n",
    "train_y_p = train_p.species  # output of the  training data\n",
    "\n",
    "test_x_p = test_p[['petal_width','petal_length']]   # taking the test data's Petal features\n",
    "test_y_p = test_p.species    # output of the test data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bgNB8kaNF4NU"
   },
   "source": [
    "### For Iris Sepal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "id": "6hVj5MW3F4NU"
   },
   "outputs": [],
   "source": [
    "#Similarly define the split for sepals\n",
    "#define the training and test data's Sepal features followed by the output of the training and test data\n",
    "\n",
    "#use naming- train_s,test_s ; train_x_s, train_y_s; test_x_s, test_y_s\n",
    "\n",
    "train_s,test_s = train_test_split(sepal, test_size=0.3, random_state=0) #sepals\n",
    "train_x_s = train_s[['sepal_width','sepal_length']]  # taking the training data's Sepal features\n",
    "train_y_s = train_s.species  # output of the  training data\n",
    "\n",
    "test_x_s = test_s[['sepal_width','sepal_length']]   # taking the test data's Sepal features\n",
    "test_y_s = test_s.species    # output of the test data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y08e1O6aU9mx"
   },
   "source": [
    "Implementing the algorithms just like we did on the complete dataset but separately on sepals and petals and calculating accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TeMWnQr6F4NV"
   },
   "source": [
    "## SVM Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "id": "jhlutJ78F4NV"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the SVM using Petals is: 1.0\n",
      "The accuracy of the SVM using Sepals is: 0.28888888888888886\n"
     ]
    }
   ],
   "source": [
    "model_SVM_p = svm.SVC(kernel='rbf')\n",
    "model_SVM_p.fit(train_x_p, train_y_p)\n",
    "prediction_SVM_p = model_SVM_p.predict(test_x_p)\n",
    "print('The accuracy of the SVM using Petals is:',metrics.accuracy_score(prediction_SVM_p,test_y_p))\n",
    "\n",
    "model_SVM_s = svm.SVC(kernel='rbf')\n",
    "model_SVM_s.fit(train_x_s, train_y_s)\n",
    "prediction_SVM_s = model_SVM_s.predict(test_x_s)\n",
    "print('The accuracy of the SVM using Sepals is:',metrics.accuracy_score(prediction_SVM_s,test_y_s))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Mli7zcq_F4NV"
   },
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "id": "2DqK_dFCF4NV"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Logistic Regression using Petals is: 1.0\n",
      "The accuracy of the Logistic Regression using Sepals is: 0.7777777777777778\n"
     ]
    }
   ],
   "source": [
    "model_LR_p = LogisticRegression()\n",
    "model_LR_p.fit(train_x_p, train_y_p)\n",
    "prediction_LR_p = model_LR_p.predict(test_x_p)\n",
    "print('The accuracy of the Logistic Regression using Petals is:',metrics.accuracy_score(prediction_LR_p,test_y_p))\n",
    "\n",
    "model_LR_s = LogisticRegression()\n",
    "model_LR_s.fit(train_x_s, train_y_s)\n",
    "prediction_LR_s = model_LR_s.predict(test_x_s)\n",
    "print('The accuracy of the Logistic Regression using Sepals is:',metrics.accuracy_score(prediction_LR_s,test_y_s))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aM-7Zx95F4NW"
   },
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "id": "S8tXp-gMF4NW"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Decision Tree using Petals is: 0.9777777777777777\n",
      "The accuracy of the Decision Tree using Sepals is: 0.7111111111111111\n"
     ]
    }
   ],
   "source": [
    "model_DT_p = DecisionTreeClassifier(random_state=0)\n",
    "model_DT_p.fit(train_x_p, train_y_p)\n",
    "prediction_DT_p = model_DT_p.predict(test_x_p)\n",
    "print('The accuracy of the Decision Tree using Petals is:',metrics.accuracy_score(prediction_DT_p,test_y_p))\n",
    "\n",
    "model_DT_s = DecisionTreeClassifier(random_state=0)\n",
    "model_DT_s.fit(train_x_s, train_y_s)\n",
    "prediction_DT_s = model_DT_s.predict(test_x_s)\n",
    "print('The accuracy of the Decision Tree using Sepals is:',metrics.accuracy_score(prediction_DT_s,test_y_s))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6ec0NUyJF4NW"
   },
   "source": [
    "\n",
    "\n",
    "\n",
    "### Question:\n",
    "Does Using Petals over Sepals for training the data give a much better accuracy? Why?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Yes, all petals machine learning models have given excellent accuracy to R sqaured rule. This can also be seen in the sepals and petals plot for different species </b>"
   ]
  }
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