R2_Internal_Lab_Questions_updated.ipynb

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   },
   "outputs": [],
   "source": [
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MBpNtcUSoRIa"
   },
   "source": [
    "## K-Nearest-Neighbors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "em-qAehKoRIb"
   },
   "source": [
    "KNN falls in the supervised learning family of algorithms. Informally, this means that we are given a labelled dataset consiting of training observations (x,y) and would like to capture the relationship between x and y. More formally, our goal is to learn a function h:X→Y so that given an unseen observation x, h(x) can confidently predict the corresponding output y.\n",
    "\n",
    "In this module we will explore the inner workings of KNN, choosing the optimal K values and using KNN from scikit-learn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DT7IQDlboRIc"
   },
   "source": [
    "## Overview\n",
    "\n",
    "1.Read the problem statement.\n",
    "\n",
    "2.Get the dataset.\n",
    "\n",
    "3.Explore the dataset.\n",
    "\n",
    "4.Pre-processing of dataset.\n",
    "\n",
    "5.Visualization\n",
    "\n",
    "6.Transform the dataset for building machine learning model.\n",
    "\n",
    "7.Split data into train, test set.\n",
    "\n",
    "7.Build Model.\n",
    "\n",
    "8.Apply the model.\n",
    "\n",
    "9.Evaluate the model.\n",
    "\n",
    "10.Finding Optimal K value\n",
    "\n",
    "11.Repeat 7,8,9 steps."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8BsgPUvKoRIc"
   },
   "source": [
    "## Problem statement\n",
    "\n",
    "### Dataset\n",
    "\n",
    "The data set we’ll be using is the Iris Flower Dataset which was first introduced in 1936 by the famous statistician Ronald Fisher and consists of 50 observations from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals.\n",
    "\n",
    "**Attributes of the dataset:** https://archive.ics.uci.edu/ml/datasets/Iris\n",
    "\n",
    "**Train the KNN algorithm to be able to distinguish the species from one another given the measurements of the 4 features.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MYqTlwmboRId"
   },
   "source": [
    "## Question 1\n",
    "\n",
    "Import the data set and print 10 random rows from the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "igR1np_YoRIe",
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   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sepal Length (in cm)</th>\n",
       "      <th>Sepal Width in (cm)</th>\n",
       "      <th>Petal length (in cm)</th>\n",
       "      <th>Petal width (in cm)</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>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sepal Length (in cm)  Sepal Width in (cm)  Petal length (in cm)  \\\n",
       "0                   5.1                  3.5                   1.4   \n",
       "1                   4.9                  3.0                   1.4   \n",
       "2                   4.7                  3.2                   1.3   \n",
       "3                   4.6                  3.1                   1.5   \n",
       "4                   5.0                  3.6                   1.4   \n",
       "5                   5.4                  3.9                   1.7   \n",
       "6                   4.6                  3.4                   1.4   \n",
       "7                   5.0                  3.4                   1.5   \n",
       "8                   4.4                  2.9                   1.4   \n",
       "9                   4.9                  3.1                   1.5   \n",
       "\n",
       "   Petal width (in cm)        Class  \n",
       "0                  0.2  Iris-setosa  \n",
       "1                  0.2  Iris-setosa  \n",
       "2                  0.2  Iris-setosa  \n",
       "3                  0.2  Iris-setosa  \n",
       "4                  0.2  Iris-setosa  \n",
       "5                  0.4  Iris-setosa  \n",
       "6                  0.3  Iris-setosa  \n",
       "7                  0.2  Iris-setosa  \n",
       "8                  0.2  Iris-setosa  \n",
       "9                  0.1  Iris-setosa  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
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    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "iris_df = pd.read_csv('C:/Users/kulkarni1/Documents/AIMLWorkspace/Databases/Iris_external.csv')\n",
    "iris_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5SuXqTKmoRIg"
   },
   "source": [
    "## Data Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J8VyfAlVoRIg"
   },
   "source": [
    "## Question 2 - Estimating missing values\n",
    "\n",
    "*Its not good to remove the records having missing values all the time. We may end up loosing some data points. So, we will have to see how to replace those missing values with some estimated values (median) *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8a8HDPxIoRIh"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sepal Length (in cm)    0\n",
       "Sepal Width in (cm)     0\n",
       "Petal length (in cm)    0\n",
       "Petal width (in cm)     0\n",
       "Class                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There are no records with missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kvEFXK8aoRIj"
   },
   "source": [
    "## Question 3 - Dealing with categorical data\n",
    "\n",
    "Change all the classes to numericals (0to2)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <th>Petal length (in cm)</th>\n",
       "      <th>Petal width (in cm)</th>\n",
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       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
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       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
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       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2</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>2</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>2</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>2</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>2</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>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sepal Length (in cm)  Sepal Width in (cm)  Petal length (in cm)  \\\n",
       "0                     5.1                  3.5                   1.4   \n",
       "1                     4.9                  3.0                   1.4   \n",
       "2                     4.7                  3.2                   1.3   \n",
       "3                     4.6                  3.1                   1.5   \n",
       "4                     5.0                  3.6                   1.4   \n",
       "5                     5.4                  3.9                   1.7   \n",
       "6                     4.6                  3.4                   1.4   \n",
       "7                     5.0                  3.4                   1.5   \n",
       "8                     4.4                  2.9                   1.4   \n",
       "9                     4.9                  3.1                   1.5   \n",
       "10                    5.4                  3.7                   1.5   \n",
       "11                    4.8                  3.4                   1.6   \n",
       "12                    4.8                  3.0                   1.4   \n",
       "13                    4.3                  3.0                   1.1   \n",
       "14                    5.8                  4.0                   1.2   \n",
       "15                    5.7                  4.4                   1.5   \n",
       "16                    5.4                  3.9                   1.3   \n",
       "17                    5.1                  3.5                   1.4   \n",
       "18                    5.7                  3.8                   1.7   \n",
       "19                    5.1                  3.8                   1.5   \n",
       "20                    5.4                  3.4                   1.7   \n",
       "21                    5.1                  3.7                   1.5   \n",
       "22                    4.6                  3.6                   1.0   \n",
       "23                    5.1                  3.3                   1.7   \n",
       "24                    4.8                  3.4                   1.9   \n",
       "25                    5.0                  3.0                   1.6   \n",
       "26                    5.0                  3.4                   1.6   \n",
       "27                    5.2                  3.5                   1.5   \n",
       "28                    5.2                  3.4                   1.4   \n",
       "29                    4.7                  3.2                   1.6   \n",
       "..                    ...                  ...                   ...   \n",
       "120                   6.9                  3.2                   5.7   \n",
       "121                   5.6                  2.8                   4.9   \n",
       "122                   7.7                  2.8                   6.7   \n",
       "123                   6.3                  2.7                   4.9   \n",
       "124                   6.7                  3.3                   5.7   \n",
       "125                   7.2                  3.2                   6.0   \n",
       "126                   6.2                  2.8                   4.8   \n",
       "127                   6.1                  3.0                   4.9   \n",
       "128                   6.4                  2.8                   5.6   \n",
       "129                   7.2                  3.0                   5.8   \n",
       "130                   7.4                  2.8                   6.1   \n",
       "131                   7.9                  3.8                   6.4   \n",
       "132                   6.4                  2.8                   5.6   \n",
       "133                   6.3                  2.8                   5.1   \n",
       "134                   6.1                  2.6                   5.6   \n",
       "135                   7.7                  3.0                   6.1   \n",
       "136                   6.3                  3.4                   5.6   \n",
       "137                   6.4                  3.1                   5.5   \n",
       "138                   6.0                  3.0                   4.8   \n",
       "139                   6.9                  3.1                   5.4   \n",
       "140                   6.7                  3.1                   5.6   \n",
       "141                   6.9                  3.1                   5.1   \n",
       "142                   5.8                  2.7                   5.1   \n",
       "143                   6.8                  3.2                   5.9   \n",
       "144                   6.7                  3.3                   5.7   \n",
       "145                   6.7                  3.0                   5.2   \n",
       "146                   6.3                  2.5                   5.0   \n",
       "147                   6.5                  3.0                   5.2   \n",
       "148                   6.2                  3.4                   5.4   \n",
       "149                   5.9                  3.0                   5.1   \n",
       "\n",
       "     Petal width (in cm)  Class  \n",
       "0                    0.2      0  \n",
       "1                    0.2      0  \n",
       "2                    0.2      0  \n",
       "3                    0.2      0  \n",
       "4                    0.2      0  \n",
       "5                    0.4      0  \n",
       "6                    0.3      0  \n",
       "7                    0.2      0  \n",
       "8                    0.2      0  \n",
       "9                    0.1      0  \n",
       "10                   0.2      0  \n",
       "11                   0.2      0  \n",
       "12                   0.1      0  \n",
       "13                   0.1      0  \n",
       "14                   0.2      0  \n",
       "15                   0.4      0  \n",
       "16                   0.4      0  \n",
       "17                   0.3      0  \n",
       "18                   0.3      0  \n",
       "19                   0.3      0  \n",
       "20                   0.2      0  \n",
       "21                   0.4      0  \n",
       "22                   0.2      0  \n",
       "23                   0.5      0  \n",
       "24                   0.2      0  \n",
       "25                   0.2      0  \n",
       "26                   0.4      0  \n",
       "27                   0.2      0  \n",
       "28                   0.2      0  \n",
       "29                   0.2      0  \n",
       "..                   ...    ...  \n",
       "120                  2.3      2  \n",
       "121                  2.0      2  \n",
       "122                  2.0      2  \n",
       "123                  1.8      2  \n",
       "124                  2.1      2  \n",
       "125                  1.8      2  \n",
       "126                  1.8      2  \n",
       "127                  1.8      2  \n",
       "128                  2.1      2  \n",
       "129                  1.6      2  \n",
       "130                  1.9      2  \n",
       "131                  2.0      2  \n",
       "132                  2.2      2  \n",
       "133                  1.5      2  \n",
       "134                  1.4      2  \n",
       "135                  2.3      2  \n",
       "136                  2.4      2  \n",
       "137                  1.8      2  \n",
       "138                  1.8      2  \n",
       "139                  2.1      2  \n",
       "140                  2.4      2  \n",
       "141                  2.3      2  \n",
       "142                  1.9      2  \n",
       "143                  2.3      2  \n",
       "144                  2.5      2  \n",
       "145                  2.3      2  \n",
       "146                  1.9      2  \n",
       "147                  2.0      2  \n",
       "148                  2.3      2  \n",
       "149                  1.8      2  \n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "labelEncoder = preprocessing.LabelEncoder()\n",
    "iris_df['Class'] = labelEncoder.fit_transform(iris_df['Class'])\n",
    "iris_df['Class'].unique()\n",
    "iris_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CEdhV6q-oRIj"
   },
   "source": [
    "## Question 4\n",
    "\n",
    "*Observe the association of each independent variable with target variable and drop variables from feature set having correlation in range -0.1 to 0.1 with target variable.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Izq1HCgloRIk"
   },
   "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 (in cm)</th>\n",
       "      <th>Sepal Width in (cm)</th>\n",
       "      <th>Petal length (in cm)</th>\n",
       "      <th>Petal width (in cm)</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sepal Length (in cm)</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sepal Width in (cm)</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Petal length (in cm)</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Petal width (in cm)</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Sepal Length (in cm)  Sepal Width in (cm)  \\\n",
       "Sepal Length (in cm)                 False                False   \n",
       "Sepal Width in (cm)                  False                False   \n",
       "Petal length (in cm)                 False                False   \n",
       "Petal width (in cm)                  False                False   \n",
       "Class                                False                False   \n",
       "\n",
       "                      Petal length (in cm)  Petal width (in cm)  Class  \n",
       "Sepal Length (in cm)                 False                False  False  \n",
       "Sepal Width in (cm)                  False                False  False  \n",
       "Petal length (in cm)                 False                False  False  \n",
       "Petal width (in cm)                  False                False  False  \n",
       "Class                                False                False  False  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_mat = iris_df.corr()\n",
    "corr_mat.abs() < 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There are no variables having correlation in range -0.1 to 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KJfDNNiJoRIl"
   },
   "source": [
    "## Question 5\n",
    "\n",
    "*Observe the independent variables variance and drop such variables having no variance or almost zero variance(variance < 0.1). They will be having almost no influence on the classification.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "c8fYDazvoRIm"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sepal Length (in cm)    False\n",
       "Sepal Width in (cm)     False\n",
       "Petal length (in cm)    False\n",
       "Petal width (in cm)     False\n",
       "Class                   False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_mat = iris_df.var()\n",
    "var_mat < 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There are no variables having no or almost zero variance (<0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "79S0_e9goRIo"
   },
   "source": [
    "## Question 6\n",
    "\n",
    "*Plot the scatter matrix for all the variables.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yqtNPRDWoRIo"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x2a357e4e630>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 942.375x900 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#import matplotlib.pyplot as plt\n",
    "#from pandas.plotting import scatter_matrix\n",
    "#%matplotlib inline\n",
    "\n",
    "#scatter_matrix(iris_df, alpha=0.2, figsize=(10, 10))\n",
    "#plt.show()\n",
    "\n",
    "import seaborn as sns\n",
    "sns.pairplot(iris_df, hue=\"Class\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Zj8ZZmlOoRIq"
   },
   "source": [
    "## Split the dataset into training and test sets\n",
    "\n",
    "## Question 7\n",
    "\n",
    "*Split the dataset into training and test sets with 80-20 ratio.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jaJH_RdDoRIr"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x = iris_df.drop(columns= \"Class\")\n",
    "y = iris_df['Class']\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30, 4) (30,)\n"
     ]
    }
   ],
   "source": [
    "print(x_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(120, 4) (120,)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape, y_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KpjOe5ZCoRIt"
   },
   "source": [
    "## Question 8 - Model\n",
    "\n",
    "*Build the model and train and test on training and test sets respectively using **scikit-learn**. Print the Accuracy of the model with different values of **k=3,5,9**.*\n",
    "\n",
    "**Hint:** For accuracy you can check **accuracy_score()** in scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score with K =  3\n",
      "0.9333333333333333\n",
      "Accuracy Score with K =  5\n",
      "0.9666666666666667\n",
      "Accuracy Score with K =  9\n",
      "0.9666666666666667\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "sc=StandardScaler()\n",
    "\n",
    "scaledX_train=sc.fit_transform(x_train)\n",
    "scaledX_test=sc.transform(x_test)\n",
    "\n",
    "for ii in 3, 5, 9:\n",
    "\n",
    "    knn=KNeighborsClassifier(n_neighbors=ii)\n",
    "    knn.fit(scaledX_train,y_train)\n",
    "\n",
    "#    print(\"Training Accuracy\")\n",
    "#    print(knn.score(scaledX_train,y_train))\n",
    "\n",
    "#    print(\"Testing Accuracy\")\n",
    "#    print(knn.score(scaledX_test,y_test))\n",
    "\n",
    "    y_pred = knn.predict(scaledX_test)\n",
    "\n",
    "    print(\"Accuracy Score with K = \", ii)\n",
    "    print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CC2jIcvRoRIw"
   },
   "source": [
    "## Question 9 - Finding Optimal value of k.\n",
    "\n",
    "Run the KNN with no of neighbours to be 1,3,5..19 and *Find the **optimal number of neighbours** from the above list using the Miss classification error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uD9tQx20oRIw"
   },
   "source": [
    "Hint:\n",
    "\n",
    "Misclassification error (MSE) = 1 - Test accuracy score. Calculated MSE for each model with neighbours = 1,3,5...19 and find the model with lowest MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "D_q46Mn3oRIx"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    K  Missed Accuracy\n",
      "0   1         0.066667\n",
      "1   3         0.066667\n",
      "2   5         0.033333\n",
      "3   7         0.066667\n",
      "4   9         0.033333\n",
      "5  11         0.033333\n",
      "6  13         0.066667\n",
      "7  15         0.066667\n",
      "8  17         0.066667\n",
      "9  19         0.033333\n",
      "Optimal number of Neighbours with minimal Missed Accuracy is:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "K                  5.000000\n",
       "Missed Accuracy    0.033333\n",
       "Name: 2, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missed_acc = list()\n",
    "for ii in range(1, 20, 2):\n",
    "\n",
    "    knn=KNeighborsClassifier(n_neighbors=ii)\n",
    "    knn.fit(scaledX_train,y_train)\n",
    "\n",
    "    y_pred = knn.predict(scaledX_test)\n",
    "\n",
    "    row = list()\n",
    "    row.append(ii)\n",
    "    row.append(1 - (accuracy_score(y_test, y_pred)))\n",
    "    missed_acc.append(row)\n",
    "\n",
    "missed_acc_df = pd.DataFrame(missed_acc, columns=['K', 'Missed Accuracy'])\n",
    "missed_acc_df.astype({'K': 'int64'})\n",
    "print(missed_acc_df)\n",
    "\n",
    "print ('Optimal number of Neighbours with minimal Missed Accuracy is:')\n",
    "missed_acc_df.loc[missed_acc_df['Missed Accuracy'].idxmin()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8JriZKaDoRIy"
   },
   "source": [
    "## Question 10\n",
    "\n",
    "*Plot misclassification error vs k (with k value on X-axis) using matplotlib.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qB4RffGwoRIz"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2a3594b2c50>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plot\n",
    "plot.plot(missed_acc_df['K'], missed_acc_df['Missed Accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "R2_Internal_Lab_Questions.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}