bl.en.u4cse21199 lab3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d2570916-6191-4746-87f1-7a380869ad0b",
   "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>UNIXTime</th>\n",
       "      <th>Data</th>\n",
       "      <th>Time</th>\n",
       "      <th>Radiation</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Pressure</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>WindDirection(Degrees)</th>\n",
       "      <th>Speed</th>\n",
       "      <th>TimeSunRise</th>\n",
       "      <th>TimeSunSet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1475229326</td>\n",
       "      <td>9/29/2016 0:00</td>\n",
       "      <td>23:55:26</td>\n",
       "      <td>1.21</td>\n",
       "      <td>48</td>\n",
       "      <td>30.46</td>\n",
       "      <td>59</td>\n",
       "      <td>177.39</td>\n",
       "      <td>5.62</td>\n",
       "      <td>6:13:00</td>\n",
       "      <td>18:13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1475229023</td>\n",
       "      <td>9/29/2016 0:00</td>\n",
       "      <td>23:50:23</td>\n",
       "      <td>1.21</td>\n",
       "      <td>48</td>\n",
       "      <td>30.46</td>\n",
       "      <td>58</td>\n",
       "      <td>176.78</td>\n",
       "      <td>3.37</td>\n",
       "      <td>6:13:00</td>\n",
       "      <td>18:13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1475228726</td>\n",
       "      <td>9/29/2016 0:00</td>\n",
       "      <td>23:45:26</td>\n",
       "      <td>1.23</td>\n",
       "      <td>48</td>\n",
       "      <td>30.46</td>\n",
       "      <td>57</td>\n",
       "      <td>158.75</td>\n",
       "      <td>3.37</td>\n",
       "      <td>6:13:00</td>\n",
       "      <td>18:13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1475228421</td>\n",
       "      <td>9/29/2016 0:00</td>\n",
       "      <td>23:40:21</td>\n",
       "      <td>1.21</td>\n",
       "      <td>48</td>\n",
       "      <td>30.46</td>\n",
       "      <td>60</td>\n",
       "      <td>137.71</td>\n",
       "      <td>3.37</td>\n",
       "      <td>6:13:00</td>\n",
       "      <td>18:13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1475228124</td>\n",
       "      <td>9/29/2016 0:00</td>\n",
       "      <td>23:35:24</td>\n",
       "      <td>1.17</td>\n",
       "      <td>48</td>\n",
       "      <td>30.46</td>\n",
       "      <td>62</td>\n",
       "      <td>104.95</td>\n",
       "      <td>5.62</td>\n",
       "      <td>6:13:00</td>\n",
       "      <td>18:13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32681</th>\n",
       "      <td>1480587604</td>\n",
       "      <td>12/1/2016 0:00</td>\n",
       "      <td>0:20:04</td>\n",
       "      <td>1.22</td>\n",
       "      <td>44</td>\n",
       "      <td>30.43</td>\n",
       "      <td>102</td>\n",
       "      <td>145.42</td>\n",
       "      <td>6.75</td>\n",
       "      <td>6:41:00</td>\n",
       "      <td>17:42:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32682</th>\n",
       "      <td>1480587301</td>\n",
       "      <td>12/1/2016 0:00</td>\n",
       "      <td>0:15:01</td>\n",
       "      <td>1.17</td>\n",
       "      <td>44</td>\n",
       "      <td>30.42</td>\n",
       "      <td>102</td>\n",
       "      <td>117.78</td>\n",
       "      <td>6.75</td>\n",
       "      <td>6:41:00</td>\n",
       "      <td>17:42:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32683</th>\n",
       "      <td>1480587001</td>\n",
       "      <td>12/1/2016 0:00</td>\n",
       "      <td>0:10:01</td>\n",
       "      <td>1.20</td>\n",
       "      <td>44</td>\n",
       "      <td>30.42</td>\n",
       "      <td>102</td>\n",
       "      <td>145.19</td>\n",
       "      <td>9.00</td>\n",
       "      <td>6:41:00</td>\n",
       "      <td>17:42:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32684</th>\n",
       "      <td>1480586702</td>\n",
       "      <td>12/1/2016 0:00</td>\n",
       "      <td>0:05:02</td>\n",
       "      <td>1.23</td>\n",
       "      <td>44</td>\n",
       "      <td>30.42</td>\n",
       "      <td>101</td>\n",
       "      <td>164.19</td>\n",
       "      <td>7.87</td>\n",
       "      <td>6:41:00</td>\n",
       "      <td>17:42:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32685</th>\n",
       "      <td>1480586402</td>\n",
       "      <td>12/1/2016 0:00</td>\n",
       "      <td>0:00:02</td>\n",
       "      <td>1.20</td>\n",
       "      <td>44</td>\n",
       "      <td>30.43</td>\n",
       "      <td>101</td>\n",
       "      <td>83.59</td>\n",
       "      <td>3.37</td>\n",
       "      <td>6:41:00</td>\n",
       "      <td>17:42:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32686 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         UNIXTime            Data      Time  Radiation  Temperature  Pressure  \\\n",
       "0      1475229326  9/29/2016 0:00  23:55:26       1.21           48     30.46   \n",
       "1      1475229023  9/29/2016 0:00  23:50:23       1.21           48     30.46   \n",
       "2      1475228726  9/29/2016 0:00  23:45:26       1.23           48     30.46   \n",
       "3      1475228421  9/29/2016 0:00  23:40:21       1.21           48     30.46   \n",
       "4      1475228124  9/29/2016 0:00  23:35:24       1.17           48     30.46   \n",
       "...           ...             ...       ...        ...          ...       ...   \n",
       "32681  1480587604  12/1/2016 0:00   0:20:04       1.22           44     30.43   \n",
       "32682  1480587301  12/1/2016 0:00   0:15:01       1.17           44     30.42   \n",
       "32683  1480587001  12/1/2016 0:00   0:10:01       1.20           44     30.42   \n",
       "32684  1480586702  12/1/2016 0:00   0:05:02       1.23           44     30.42   \n",
       "32685  1480586402  12/1/2016 0:00   0:00:02       1.20           44     30.43   \n",
       "\n",
       "       Humidity  WindDirection(Degrees)  Speed TimeSunRise TimeSunSet  \n",
       "0            59                  177.39   5.62     6:13:00   18:13:00  \n",
       "1            58                  176.78   3.37     6:13:00   18:13:00  \n",
       "2            57                  158.75   3.37     6:13:00   18:13:00  \n",
       "3            60                  137.71   3.37     6:13:00   18:13:00  \n",
       "4            62                  104.95   5.62     6:13:00   18:13:00  \n",
       "...         ...                     ...    ...         ...        ...  \n",
       "32681       102                  145.42   6.75     6:41:00   17:42:00  \n",
       "32682       102                  117.78   6.75     6:41:00   17:42:00  \n",
       "32683       102                  145.19   9.00     6:41:00   17:42:00  \n",
       "32684       101                  164.19   7.87     6:41:00   17:42:00  \n",
       "32685       101                   83.59   3.37     6:41:00   17:42:00  \n",
       "\n",
       "[32686 rows x 11 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df=pd.read_csv(\"SolarPrediction.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f7ef01fa-1356-4439-a65e-2d76f5e51b03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean of the temperature is  51.10325521630055\n",
      "The mean of the humidity is  75.01630667564095\n"
     ]
    }
   ],
   "source": [
    "#Calculate the mean for each class (also called as class centroid)(Suggestion: You may use numpy.mean() function for finding the average vector for all \n",
    "#vectors in a given class. Please define the axis property appropriately to use this function. EX: feat_vecs.mean(axis=0))\n",
    "temp=df['Temperature']\n",
    "hum=df['Humidity']\n",
    "temp_mean=np.mean(temp,axis=0)\n",
    "hum_mean=np.mean(hum,axis=0)\n",
    "print(\"The mean of the temperature is \",temp_mean)\n",
    "print(\"The mean of the humidity is \",hum_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b5a44ca9-4e77-483a-9e19-e0bc76378475",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23.913051459340394\n"
     ]
    }
   ],
   "source": [
    "interclass_distance=np.linalg.norm(temp_mean-hum_mean)\n",
    "print(interclass_distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f5bd1f8b-1fb7-44dd-aa34-c4ea8b14344f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.201062496782136\n",
      "25.989820931234355\n"
     ]
    }
   ],
   "source": [
    "#Calculate spread (standard deviation) for each class(Suggestion: You may use numpy.std() function for finding the standard deviation vector \n",
    "#for all vectors in a given class. Please define the axis property appropriately to use this function.)\n",
    "temp_std=np.std(temp,axis=0)\n",
    "hum_std=np.std(hum,axis=0)\n",
    "print(temp_std)\n",
    "print(hum_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "94b2cd92-3d6c-43f2-87ce-98ed149baa2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The interclass distance between temp and hum interclass_distance  19.78875843445222\n"
     ]
    }
   ],
   "source": [
    "interclass_distance=np.linalg.norm(temp_std-hum_std)\n",
    "print(\"The interclass distance between temp and hum interclass_distance \",interclass_distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d1f9d6af-6587-4524-b325-c4bf9496c738",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intraclass spread for Temperature: 1121.106825260192\n",
      "Intraclass spread for Humidity: 4698.769871843256\n"
     ]
    }
   ],
   "source": [
    "intra_class_spread_temp = np.mean(np.linalg.norm(temp - temp_mean))\n",
    "intra_class_spread_hum = np.mean(np.linalg.norm(hum - hum_mean))\n",
    "\n",
    "print(\"Intraclass spread for Temperature:\", intra_class_spread_temp)\n",
    "print(\"Intraclass spread for Humidity:\", intra_class_spread_hum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "79e54726-b6b2-4e7c-9fa3-b5fd622e920b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean of the temperature is  51.10325521630055\n"
     ]
    }
   ],
   "source": [
    "#Take any feature from your dataset. Observe the density pattern for that feature by plotting the histogram. Use buckets (data in ranges) for histogram \n",
    "#generation and study. Calculate the mean and variance from the available data.\n",
    "# Sample feature_data (replace this with your actual data)\n",
    "feature_data = np.array([10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60])\n",
    "import matplotlib.pyplot as plt\n",
    "# Create a histogram with 5 bins\n",
    "plt.hist(temp, bins=10, edgecolor='black')\n",
    "plt.xlabel('Temperature')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Temperature')\n",
    "plt.show()\n",
    "print(\"The mean of the temperature is \",temp_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "6f912860-a73d-4018-a36e-bf0dfd213937",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Take any two feature vectors from your dataset. Calculate the Minkwoski distance with r from 1 to 10. Make a plot of the distance and observe the \n",
    "#nature of this graph\n",
    "\n",
    "r_values = list(range(1, 11))\n",
    "distances = []\n",
    "\n",
    "for r in r_values:\n",
    "    distance = np.linalg.norm(hum - temp, ord=r)\n",
    "    distances.append(distance)\n",
    "\n",
    "plt.plot(r_values, distances, marker='o', linestyle='-')\n",
    "plt.xlabel('r (Minkowski Parameter)')\n",
    "plt.ylabel('Minkowski Distance')\n",
    "plt.title('Minkowski Distance vs. r')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "6f9d9f06-0731-4956-8763-affda511dee7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4. Divide dataset in your project into two parts – train & test set. To accomplish this, use the train\u0002test_split() function available in SciKit.\n",
    "from sklearn.model_selection import train_test_split\n",
    "df['Radiation_Class'] = pd.cut(df['Radiation'], bins=[0, 800, 1610], labels=['Low', 'High'])\n",
    "X= df.loc[:,\"Temperature\":\"Speed\"]\n",
    "y= df[\"Radiation_Class\"]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "da11f180-8856-479f-bc10-589f3e4cf218",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#A5\n",
    "import numpy as np\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "neigh = KNeighborsClassifier(n_neighbors=3)\n",
    "neigh.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f80d87c3-2d95-4b9e-b9b7-8a6d6edda73e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9478433771795656"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#A6\n",
    "neigh.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "61561709-23f5-47f2-9961-b46e965693da",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SNS\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['High'], dtype=object)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#A7\n",
    "neigh.predict([[62,30.43,53,65.07,7.87]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "bdf30fa2-d97a-422b-92f4-5203a1b27045",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9478433771795656\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SNS\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['High']\n",
      "Both predictions are correct with accuracy score: k=1: 1.0 and k=3: 0.9478433771795656\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#A8\n",
    "k_3_score=neigh.score(X_test, y_test)\n",
    "print(k_3_score)\n",
    "neigh = KNeighborsClassifier(n_neighbors=1) \n",
    "neigh.fit(X, y)\n",
    "k_1_score=neigh.score(X_test, y_test)\n",
    "print(neigh.predict([[62,30.43,\t53,65.07,7.87]]))\n",
    "print(f'Both predictions are correct with accuracy score: k=1: {k_1_score} and k=3: {k_3_score}')\n",
    "\n",
    "x_axis= np.arange(1,12)\n",
    "y_axis=[]\n",
    "\n",
    "for i in range(1,12):\n",
    "    neigh = KNeighborsClassifier(n_neighbors=i) \n",
    "    neigh.fit(X, y)\n",
    "    y_axis.append(neigh.score(X_test, y_test))\n",
    "\n",
    "plt.bar(x_axis,y_axis,color='blue')\n",
    "plt.xlabel('K values')\n",
    "plt.ylabel('Accuracy Score')\n",
    "plt.title('K values vs Accuarcy Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3d937477-9001-4b6c-8bff-74be55eb90ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CF (train): [[ 1652   847]\n",
      " [  481 23168]]\n",
      "CF (test): [[ 424  230]\n",
      " [ 111 5773]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision (train): 0.7744960150023441\n",
      "recall (train): 0.6610644257703081\n",
      "f1_score (train): 0.7132987910189982\n",
      "\n",
      "precision (test): 0.7925233644859813\n",
      "recall (test): 0.6483180428134556\n",
      "f1_score (test): 0.7132043734230445\n"
     ]
    }
   ],
   "source": [
    "#A9\n",
    "from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score\n",
    "import seaborn as sns\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "neigh = KNeighborsClassifier(n_neighbors=3) \n",
    "neigh.fit(X, y)\n",
    "\n",
    "y_train_pred = neigh.predict(X_train)\n",
    "y_test_pred = neigh.predict(X_test)\n",
    "\n",
    "confusion_matrix_train = confusion_matrix(y_train, y_train_pred, labels=['High', 'Low'])\n",
    "confusion_matrix_test = confusion_matrix(y_test, y_test_pred, labels=['High', 'Low'])\n",
    "print(f'CF (train): {confusion_matrix_train}')\n",
    "print(f'CF (test): {confusion_matrix_test}')\n",
    "sns.heatmap(confusion_matrix_train,\n",
    "            annot=True,\n",
    "            fmt='g',\n",
    "            xticklabels=['High','Low'],\n",
    "            yticklabels=['High','Low'])\n",
    "plt.ylabel('Prediction',fontsize=13)\n",
    "plt.xlabel('Actual',fontsize=13)\n",
    "plt.title('Confusion Matrix training',fontsize=17)\n",
    "plt.show()\n",
    "sns.heatmap(confusion_matrix_test,\n",
    "            annot=True,\n",
    "            fmt='g',\n",
    "            xticklabels=['High','Low'],\n",
    "            yticklabels=['High','Low'])\n",
    "plt.ylabel('Prediction',fontsize=13)\n",
    "plt.xlabel('Actual',fontsize=13)\n",
    "plt.title('Confusion Matrix',fontsize=17)\n",
    "plt.show()\n",
    "precision_train = precision_score(y_train, y_train_pred, pos_label='High')\n",
    "recall_train = recall_score(y_train, y_train_pred, pos_label='High')\n",
    "f1_score_train = f1_score(y_train, y_train_pred, pos_label='High')\n",
    "\n",
    "print(f'precision (train): {precision_train}')\n",
    "print(f'recall (train): {recall_train}')\n",
    "print(f'f1_score (train): {f1_score_train}\\n')\n",
    "\n",
    "precision_test = precision_score(y_test, y_test_pred, pos_label='High')\n",
    "recall_test = recall_score(y_test, y_test_pred, pos_label='High')\n",
    "f1_score_test = f1_score(y_test, y_test_pred, pos_label='High')\n",
    "\n",
    "print(f'precision (test): {precision_test}')\n",
    "print(f'recall (test): {recall_test}')\n",
    "print(f'f1_score (test): {f1_score_test}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93463981-831d-4f6a-80ab-1718dfc6cb99",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}