kNN-week_sematic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EgDA6r3saifm"
   },
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
     "elapsed": 404,
     "status": "ok",
     "timestamp": 1699516726543,
     "user": {
      "displayName": "Xojiakbar Burxonov",
      "userId": "16264803897982475703"
     },
     "user_tz": 0
    },
    "id": "vTcE7H9vN0KM"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LG5ARI4PaoIs"
   },
   "source": [
    "# Read file using pandas.\n",
    "\n",
    "# View sample data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 226
    },
    "executionInfo": {
     "elapsed": 309,
     "status": "ok",
     "timestamp": 1699516749290,
     "user": {
      "displayName": "Xojiakbar Burxonov",
      "userId": "16264803897982475703"
     },
     "user_tz": 0
    },
    "id": "-hL50vmLOAxC",
    "outputId": "3c289193-ea3b-459e-fffc-0ab62f1d5061"
   },
   "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>sample_id</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>thickness</th>\n",
       "      <th>surface_area</th>\n",
       "      <th>mass</th>\n",
       "      <th>compactness</th>\n",
       "      <th>hardness</th>\n",
       "      <th>shell_top_radius</th>\n",
       "      <th>water_content</th>\n",
       "      <th>carbohydrate_content</th>\n",
       "      <th>variety</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>71</td>\n",
       "      <td>11.67</td>\n",
       "      <td>12.8025</td>\n",
       "      <td>8.055075</td>\n",
       "      <td>34.65</td>\n",
       "      <td>1375.50</td>\n",
       "      <td>0.93005</td>\n",
       "      <td>19.145</td>\n",
       "      <td>4.4604</td>\n",
       "      <td>0.048668</td>\n",
       "      <td>0.175</td>\n",
       "      <td>c_avellana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>72</td>\n",
       "      <td>13.86</td>\n",
       "      <td>13.0995</td>\n",
       "      <td>7.349907</td>\n",
       "      <td>38.10</td>\n",
       "      <td>1439.55</td>\n",
       "      <td>0.93401</td>\n",
       "      <td>8.780</td>\n",
       "      <td>4.7844</td>\n",
       "      <td>0.048826</td>\n",
       "      <td>0.167</td>\n",
       "      <td>c_avellana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>20.53</td>\n",
       "      <td>15.5925</td>\n",
       "      <td>9.565427</td>\n",
       "      <td>49.89</td>\n",
       "      <td>1623.30</td>\n",
       "      <td>0.96217</td>\n",
       "      <td>5.120</td>\n",
       "      <td>5.2893</td>\n",
       "      <td>0.049521</td>\n",
       "      <td>0.174</td>\n",
       "      <td>c_americana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>73</td>\n",
       "      <td>14.13</td>\n",
       "      <td>12.2220</td>\n",
       "      <td>7.182949</td>\n",
       "      <td>35.43</td>\n",
       "      <td>1412.25</td>\n",
       "      <td>0.90178</td>\n",
       "      <td>13.694</td>\n",
       "      <td>4.8168</td>\n",
       "      <td>0.049595</td>\n",
       "      <td>0.167</td>\n",
       "      <td>c_avellana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>15.85</td>\n",
       "      <td>14.7240</td>\n",
       "      <td>8.622661</td>\n",
       "      <td>43.29</td>\n",
       "      <td>1512.00</td>\n",
       "      <td>0.96261</td>\n",
       "      <td>10.925</td>\n",
       "      <td>4.6296</td>\n",
       "      <td>0.050384</td>\n",
       "      <td>0.173</td>\n",
       "      <td>c_americana</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sample_id  length    width  thickness  surface_area     mass  compactness  \\\n",
       "0         71   11.67  12.8025   8.055075         34.65  1375.50      0.93005   \n",
       "1         72   13.86  13.0995   7.349907         38.10  1439.55      0.93401   \n",
       "2          1   20.53  15.5925   9.565427         49.89  1623.30      0.96217   \n",
       "3         73   14.13  12.2220   7.182949         35.43  1412.25      0.90178   \n",
       "4          2   15.85  14.7240   8.622661         43.29  1512.00      0.96261   \n",
       "\n",
       "   hardness  shell_top_radius  water_content  carbohydrate_content  \\\n",
       "0    19.145            4.4604       0.048668                 0.175   \n",
       "1     8.780            4.7844       0.048826                 0.167   \n",
       "2     5.120            5.2893       0.049521                 0.174   \n",
       "3    13.694            4.8168       0.049595                 0.167   \n",
       "4    10.925            4.6296       0.050384                 0.173   \n",
       "\n",
       "       variety  \n",
       "0   c_avellana  \n",
       "1   c_avellana  \n",
       "2  c_americana  \n",
       "3   c_avellana  \n",
       "4  c_americana  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your code to read the dataset\n",
    "df=pd.read_csv(\"hazelnut.csv\")\n",
    "df.head()\n",
    "# Your code to Print sample dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WnGnTNqkawfY"
   },
   "source": [
    "# Print the shape of the pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 269,
     "status": "ok",
     "timestamp": 1699516753244,
     "user": {
      "displayName": "Xojiakbar Burxonov",
      "userId": "16264803897982475703"
     },
     "user_tz": 0
    },
    "id": "pcta_d82ODhH",
    "outputId": "6cd60842-7d7f-4c89-d776-49631e003ab7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(201, 12)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the shape of the dataframe\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s80O-FZObB4y"
   },
   "source": [
    "# Separate the dataframe into input matrix X and output vector y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "executionInfo": {
     "elapsed": 1,
     "status": "ok",
     "timestamp": 1699516754730,
     "user": {
      "displayName": "Xojiakbar Burxonov",
      "userId": "16264803897982475703"
     },
     "user_tz": 0
    },
    "id": "_xjUSDvYOLnT"
   },
   "outputs": [],
   "source": [
    "X = df.drop('variety',axis=1).values\n",
    "y = df['variety'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J3z_OFTnbWTQ"
   },
   "source": [
    "# Import the SKlearn methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "executionInfo": {
     "elapsed": 1370,
     "status": "ok",
     "timestamp": 1699516757806,
     "user": {
      "displayName": "Xojiakbar Burxonov",
      "userId": "16264803897982475703"
     },
     "user_tz": 0
    },
    "id": "3cqkFPjJONPW"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report, accuracy_score\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RiWAFigDbmqZ"
   },
   "source": [
    "# Split the data into train and test sets - X_train, X_test, y_train, y_test.\n",
    "\n",
    "# Use test size of 40%.\n",
    "\n",
    "## Hint: Use Sklearn function: https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 701,
     "status": "ok",
     "timestamp": 1699516761530,
     "user": {
      "displayName": "Xojiakbar Burxonov",
      "userId": "16264803897982475703"
     },
     "user_tz": 0
    },
    "id": "-epouREEOTsI",
    "outputId": "a171b13c-1e46-46b0-c107-1db60320c1c3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(134, 11)\n",
      "(67, 11)\n",
      "(134,)\n",
      "(67,)\n"
     ]
    }
   ],
   "source": [
    "# Your code to separate train and test sixe.\n",
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, random_state=42)\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "\n",
    "print(y_test.shape)\n",
    "# Your code to Print the shapes of the X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8S67VGukcmqn"
   },
   "source": [
    "# Scale the dataset between 0-1.\n",
    "\n",
    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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "executionInfo": {
     "elapsed": 250,
     "status": "ok",
     "timestamp": 1699516766500,
     "user": {
      "displayName": "Xojiakbar Burxonov",
      "userId": "16264803897982475703"
     },
     "user_tz": 0
    },
    "id": "ctkaWfWSW0Sp"
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mMx72o01dPG8"
   },
   "source": [
    "# Implement Knn classifer using sklearn.\n",
    "\n",
    "# Use number of neigbours as 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 75
    },
    "executionInfo": {
     "elapsed": 493,
     "status": "ok",
     "timestamp": 1699517772055,
     "user": {
      "displayName": "Xojiakbar Burxonov",
      "userId": "16264803897982475703"
     },
     "user_tz": 0
    },
    "id": "V8d0rGOLOSyo",
    "outputId": "497c6684-9943-415e-c08f-b02c089ebdf6"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" 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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" 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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 3)\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1699517772056,
     "user": {
      "displayName": "Xojiakbar Burxonov",
      "userId": "16264803897982475703"
     },
     "user_tz": 0
    },
    "id": "XnzNE3otISTi"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HUUlKK0IdmDl"
   },
   "source": [
    "# Make predictions on the test sample X_test.\n",
    "\n",
    "# Print predicted values.\n",
    "\n",
    "# Print actual test values.\n",
    "\n",
    "# Are predicted and actual values are equal?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 250,
     "status": "ok",
     "timestamp": 1699516772157,
     "user": {
      "displayName": "Xojiakbar Burxonov",
      "userId": "16264803897982475703"
     },
     "user_tz": 0
    },
    "id": "gdqSmGOuPiO6",
    "outputId": "5486a107-311a-4a87-9fa9-d51a4aa1734f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['c_avellana' 'c_americana' 'c_americana' 'c_avellana' 'c_cornuta'\n",
      " 'c_avellana' 'c_cornuta' 'c_avellana' 'c_americana' 'c_americana'\n",
      " 'c_avellana' 'c_cornuta' 'c_cornuta' 'c_avellana' 'c_americana'\n",
      " 'c_americana' 'c_americana' 'c_avellana' 'c_americana' 'c_cornuta'\n",
      " 'c_cornuta' 'c_americana' 'c_cornuta' 'c_americana' 'c_cornuta'\n",
      " 'c_americana' 'c_cornuta' 'c_avellana' 'c_americana' 'c_cornuta'\n",
      " 'c_cornuta' 'c_avellana' 'c_americana' 'c_americana' 'c_avellana'\n",
      " 'c_americana' 'c_avellana' 'c_cornuta' 'c_americana' 'c_cornuta'\n",
      " 'c_americana' 'c_americana' 'c_avellana' 'c_americana' 'c_cornuta'\n",
      " 'c_avellana' 'c_avellana' 'c_cornuta' 'c_cornuta' 'c_cornuta'\n",
      " 'c_americana' 'c_cornuta' 'c_cornuta' 'c_americana' 'c_avellana'\n",
      " 'c_cornuta' 'c_avellana' 'c_americana' 'c_cornuta' 'c_americana'\n",
      " 'c_cornuta' 'c_americana' 'c_avellana' 'c_avellana' 'c_americana'\n",
      " 'c_americana' 'c_cornuta']\n",
      "['c_avellana' 'c_americana' 'c_americana' 'c_avellana' 'c_cornuta'\n",
      " 'c_avellana' 'c_cornuta' 'c_avellana' 'c_americana' 'c_americana'\n",
      " 'c_avellana' 'c_cornuta' 'c_cornuta' 'c_avellana' 'c_americana'\n",
      " 'c_americana' 'c_americana' 'c_avellana' 'c_americana' 'c_cornuta'\n",
      " 'c_cornuta' 'c_americana' 'c_cornuta' 'c_americana' 'c_cornuta'\n",
      " 'c_americana' 'c_cornuta' 'c_avellana' 'c_americana' 'c_cornuta'\n",
      " 'c_cornuta' 'c_avellana' 'c_americana' 'c_americana' 'c_avellana'\n",
      " 'c_americana' 'c_avellana' 'c_cornuta' 'c_americana' 'c_cornuta'\n",
      " 'c_americana' 'c_americana' 'c_avellana' 'c_avellana' 'c_cornuta'\n",
      " 'c_cornuta' 'c_avellana' 'c_cornuta' 'c_cornuta' 'c_cornuta'\n",
      " 'c_americana' 'c_cornuta' 'c_cornuta' 'c_americana' 'c_avellana'\n",
      " 'c_cornuta' 'c_avellana' 'c_avellana' 'c_cornuta' 'c_americana'\n",
      " 'c_cornuta' 'c_americana' 'c_avellana' 'c_avellana' 'c_americana'\n",
      " 'c_americana' 'c_cornuta']\n"
     ]
    }
   ],
   "source": [
    "y_pred = knn.predict(X_test)\n",
    "print (y_pred)\n",
    "print (y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fAl-WrRNeLm1"
   },
   "source": [
    "# Print accuracy for training and test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1699516775068,
     "user": {
      "displayName": "Xojiakbar Burxonov",
      "userId": "16264803897982475703"
     },
     "user_tz": 0
    },
    "id": "O5jlDXwzOnyw",
    "outputId": "cfd5f07c-b2db-43f4-d3ce-9c9fa8ecf3dc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9850746268656716\n",
      "0.9552238805970149\n"
     ]
    }
   ],
   "source": [
    "print (knn.score(X_train, y_train))\n",
    "print (knn.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OLZO0adieT_7"
   },
   "source": [
    "# Print Classification report - Precision, Recall, and F-score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 538,
     "status": "ok",
     "timestamp": 1699517797578,
     "user": {
      "displayName": "Xojiakbar Burxonov",
      "userId": "16264803897982475703"
     },
     "user_tz": 0
    },
    "id": "2EMDwwlQO3jg",
    "outputId": "4da4b400-be21-4fe6-b967-6e5ee591f401"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      " c_americana       0.92      1.00      0.96        24\n",
      "  c_avellana       0.94      0.89      0.92        19\n",
      "   c_cornuta       1.00      0.96      0.98        24\n",
      "\n",
      "    accuracy                           0.96        67\n",
      "   macro avg       0.96      0.95      0.95        67\n",
      "weighted avg       0.96      0.96      0.96        67\n",
      "\n",
      "Validation Accuracy:  0.9552238805970149\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print (classification_report(y_test, y_pred))\n",
    "knn_accuracy=accuracy_score(y_test, y_pred)\n",
    "\n",
    "print (print(\"Validation Accuracy: \", accuracy_score(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "serjU1XlefUp"
   },
   "source": [
    "# Display Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 450
    },
    "executionInfo": {
     "elapsed": 606,
     "status": "ok",
     "timestamp": 1699516785116,
     "user": {
      "displayName": "Xojiakbar Burxonov",
      "userId": "16264803897982475703"
     },
     "user_tz": 0
    },
    "id": "bfa62-tmPz3r",
    "outputId": "c8c4ae33-0e91-44cb-9be1-0f340a748f53"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix = cm, display_labels = knn.classes_)\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sCGB0cMEfieB"
   },
   "source": [
    "# Till now, we implemented the KNN for number of neigbours 3.\n",
    "# Now, we will check the accuracy for number of neighbours 1- 10 - all values, and will plot accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "executionInfo": {
     "elapsed": 773,
     "status": "ok",
     "timestamp": 1699516788378,
     "user": {
      "displayName": "Xojiakbar Burxonov",
      "userId": "16264803897982475703"
     },
     "user_tz": 0
    },
    "id": "NCwX5VVYSc3o"
   },
   "outputs": [],
   "source": [
    "# Your code to create an empty list train_scores\n",
    "train_scores=[]\n",
    "\n",
    "# Your code to create an empty list test_scores\n",
    "test_scores=[]\n",
    "# Your code to create a list neighbours having values [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]. You can create numpy array and use np.arange function.\n",
    "neighbours=[1,2,3,4,5,6,7,8,9,10]\n",
    "\n",
    "for neighbour in neighbours: # Your code to fill in the for loop.\n",
    "\n",
    "  knn = KNeighborsClassifier(n_neighbors = neighbour)\n",
    "\n",
    "  # Create a knn classifier object for particular neigbour value.\n",
    "\n",
    "  # Fit the classifier on X_train, y_train\n",
    "  knn.fit(X_train, y_train)\n",
    "  # Make predictions on train data.  Store the values in train_pred variable.\n",
    "  train_pred = knn.predict(X_train)\n",
    "  # Make predictions on test data.  Store the values in test_pred variable.\n",
    "  test_pred=knn.predict(X_test)\n",
    "  # Find accuracy for train data. Hint - use accuracy_score method in sklearn.\n",
    "  train_score = accuracy_score(y_train, train_pred)\n",
    "  # Find accuracy for test data. Hint - use accuracy_score method in sklearn.\n",
    "  test_score = accuracy_score(y_test, test_pred)\n",
    "  # Append training accuracy in list train_scores.\n",
    "  train_scores.append(train_score)\n",
    "  # Append test accuracy in list test-scores.\n",
    "  test_scores.append(test_score)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 443,
     "status": "ok",
     "timestamp": 1699517497191,
     "user": {
      "displayName": "Xojiakbar Burxonov",
      "userId": "16264803897982475703"
     },
     "user_tz": 0
    },
    "id": "k9UGsRfNItuV",
    "outputId": "7326d568-5619-47db-d5c7-a3825013b21f"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iLF1qgPlgFBm"
   },
   "source": [
    "# Plot the accuracy for number of neigbours 1-10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "executionInfo": {
     "elapsed": 815,
     "status": "ok",
     "timestamp": 1699516793140,
     "user": {
      "displayName": "Xojiakbar Burxonov",
      "userId": "16264803897982475703"
     },
     "user_tz": 0
    },
    "id": "Mkqadq4HTWIy",
    "outputId": "4b22be55-4e0b-4e06-cc94-18ec53bc628d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "# Your code to plot neigbours vs train scores. Proivide label name as the 'train accuracy'\n",
    "plt.plot(neighbours, train_scores, label='train accuracy')\n",
    "plt.xlabel('Number of Neighbors')\n",
    "plt.ylabel('Train Accuracy')\n",
    "plt.title('Train Accuracy vs. Number of Neighbors')\n",
    "\n",
    "# Your code to plot neigbours vs test scores. Provide label bame as the 'test accuracy'.\n",
    "#\n",
    "plt.plot(neighbours, test_scores, label='test accuracy')\n",
    "plt.xlabel('Number of Neighbors')\n",
    "plt.ylabel('Test Accuracy')\n",
    "plt.title('Test Accuracy vs. Number of Neighbors')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lkOYbGMetWL1"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "we6djldKjiqC"
   },
   "source": [
    "# Let's implement other ML algorithm that we studied -  Logistic Regression and MLP.\n",
    "\n",
    "# We will do the same task using Logistic Regression and MLP.\n",
    "\n",
    "## Implement Logistic Regression and MLP for the same problem and compare accuracy of the three classifiers using Bar plot:\n",
    "\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "executionInfo": {
     "elapsed": 255,
     "status": "ok",
     "timestamp": 1699516805151,
     "user": {
      "displayName": "Xojiakbar Burxonov",
      "userId": "16264803897982475703"
     },
     "user_tz": 0
    },
    "id": "OoOIZfINwvXo"
   },
   "outputs": [],
   "source": [
    "#\n",
    "test_scores_models=[]\n",
    "test_scores_models.append(test_scores[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 75
    },
    "executionInfo": {
     "elapsed": 270,
     "status": "ok",
     "timestamp": 1699516807149,
     "user": {
      "displayName": "Xojiakbar Burxonov",
      "userId": "16264803897982475703"
     },
     "user_tz": 0
    },
    "id": "CtV-uq2XwyEo",
    "outputId": "186ab1e6-d914-4fd4-ea52-c26220f7fb5b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LogisticRegression(random_state=0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logisticRegression = LogisticRegression(random_state=0)\n",
    "\n",
    "logisticRegression.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 75
    },
    "executionInfo": {
     "elapsed": 186,
     "status": "ok",
     "timestamp": 1699461552669,
     "user": {
      "displayName": "Xojiakbar Burxonov",
      "userId": "16264803897982475703"
     },
     "user_tz": 0
    },
    "id": "fL1wZ1obQK45",
    "outputId": "8652506e-9baf-4990-f67e-c6cdf86748d2"
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# Your code to implement Logistic regression and MLP based classifier.\n",
    "\n",
    "\n",
    "\n",
    "# You could split this code into multiple cells\n",
    "\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "executionInfo": {
     "elapsed": 276,
     "status": "ok",
     "timestamp": 1699516811345,
     "user": {
      "displayName": "Xojiakbar Burxonov",
      "userId": "16264803897982475703"
     },
     "user_tz": 0
    },
    "id": "To8OIi-Ix1N0"
   },
   "outputs": [],
   "source": [
    "y_pred=LogisticRegression(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 823,
     "status": "ok",
     "timestamp": 1699516814439,
     "user": {
      "displayName": "Xojiakbar Burxonov",
      "userId": "16264803897982475703"
     },
     "user_tz": 0
    },
    "id": "ddats363vsKI",
    "outputId": "2f1c5e0d-f1ad-4b1f-acbc-3bd5351cb4dd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9925373134328358\n",
      "0.9552238805970149\n"
     ]
    }
   ],
   "source": [
    "from numpy.ma.core import logical_and\n",
    "print (logisticRegression.score(X_train, y_train))\n",
    "print (logisticRegression.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "executionInfo": {
     "elapsed": 267,
     "status": "ok",
     "timestamp": 1699516816931,
     "user": {
      "displayName": "Xojiakbar Burxonov",
      "userId": "16264803897982475703"
     },
     "user_tz": 0
    },
    "id": "96KtjRg_yjLR"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.9253731343283582, 0.9552238805970149, 0.9552238805970149]\n"
     ]
    }
   ],
   "source": [
    "test_scores_models.append(logisticRegression.score(X_test,y_test))\n",
    "print(test_scores_models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1699517861212,
     "user": {
      "displayName": "Xojiakbar Burxonov",
      "userId": "16264803897982475703"
     },
     "user_tz": 0
    },
    "id": "bMsIfQNcy5Uw",
    "outputId": "0db91deb-f001-45f8-c406-f62185b742c6"
   },
   "outputs": [
    {
     "ename": "InvalidParameterError",
     "evalue": "The 'y_pred' parameter of classification_report must be an array-like or a sparse matrix. Got LogisticRegression(penalty=array([[ 0.03900614, -1.54175796, -1.13895409, -1.22907072, -1.39544204,\n        -1.55053497, -0.48060946,  0.64166894, -0.75466805, -0.11809933,\n        -0.53152465],\n       [-1.6459564 , -0.28036122, -0.49853236, -0.1144398 , -0.48402086,\n        -0.42585156, -0.42108458, -0.13340314, -0.68276184, -1.36081309,\n        -0.53152465],\n       [-1.55998892, -0.52556731, -0.97884866, -0.86547871, -0....\n       [-1.49121494, -0.21198644,  0.09464515,  0.45528118, -0.02315128,\n        -0.0380297 ,  0.4930475 , -0.6494938 , -0.6531534 , -1.12208682,\n        -1.20985134],\n       [-0.9066361 ,  0.57078686,  0.37023647,  0.86193694,  0.22104081,\n         0.22568917,  0.49729928,  0.60297774,  0.2287552 ,  0.38010919,\n         0.14680205],\n       [ 0.76113294,  1.4384392 ,  1.46997707,  1.89473584,  1.47639451,\n         1.4589627 ,  0.79492367, -0.4123542 ,  1.6182371 , -0.99582935,\n         1.16429208]])) instead.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInvalidParameterError\u001b[0m                     Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[22], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(classification_report(y_test, y_pred))\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mValidation Accuracy: \u001b[39m\u001b[38;5;124m\"\u001b[39m, accuracy_score(y_test, y_pred))\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:201\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    198\u001b[0m to_ignore \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mself\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcls\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m    199\u001b[0m params \u001b[38;5;241m=\u001b[39m {k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m params\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m to_ignore}\n\u001b[0;32m--> 201\u001b[0m validate_parameter_constraints(\n\u001b[1;32m    202\u001b[0m     parameter_constraints, params, caller_name\u001b[38;5;241m=\u001b[39mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\n\u001b[1;32m    203\u001b[0m )\n\u001b[1;32m    205\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    206\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m    207\u001b[0m         skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m    208\u001b[0m             prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m    209\u001b[0m         )\n\u001b[1;32m    210\u001b[0m     ):\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:95\u001b[0m, in \u001b[0;36mvalidate_parameter_constraints\u001b[0;34m(parameter_constraints, params, caller_name)\u001b[0m\n\u001b[1;32m     89\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     90\u001b[0m     constraints_str \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m     91\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin([\u001b[38;5;28mstr\u001b[39m(c)\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mfor\u001b[39;00m\u001b[38;5;250m \u001b[39mc\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01min\u001b[39;00m\u001b[38;5;250m \u001b[39mconstraints[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m or\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     92\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconstraints[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     93\u001b[0m     )\n\u001b[0;32m---> 95\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidParameterError(\n\u001b[1;32m     96\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparam_name\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m parameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcaller_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     97\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconstraints_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. Got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparam_val\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     98\u001b[0m )\n",
      "\u001b[0;31mInvalidParameterError\u001b[0m: The 'y_pred' parameter of classification_report must be an array-like or a sparse matrix. Got LogisticRegression(penalty=array([[ 0.03900614, -1.54175796, -1.13895409, -1.22907072, -1.39544204,\n        -1.55053497, -0.48060946,  0.64166894, -0.75466805, -0.11809933,\n        -0.53152465],\n       [-1.6459564 , -0.28036122, -0.49853236, -0.1144398 , -0.48402086,\n        -0.42585156, -0.42108458, -0.13340314, -0.68276184, -1.36081309,\n        -0.53152465],\n       [-1.55998892, -0.52556731, -0.97884866, -0.86547871, -0....\n       [-1.49121494, -0.21198644,  0.09464515,  0.45528118, -0.02315128,\n        -0.0380297 ,  0.4930475 , -0.6494938 , -0.6531534 , -1.12208682,\n        -1.20985134],\n       [-0.9066361 ,  0.57078686,  0.37023647,  0.86193694,  0.22104081,\n         0.22568917,  0.49729928,  0.60297774,  0.2287552 ,  0.38010919,\n         0.14680205],\n       [ 0.76113294,  1.4384392 ,  1.46997707,  1.89473584,  1.47639451,\n         1.4589627 ,  0.79492367, -0.4123542 ,  1.6182371 , -0.99582935,\n         1.16429208]])) instead."
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "print(\"Validation Accuracy: \", accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 381
    },
    "executionInfo": {
     "elapsed": 384,
     "status": "error",
     "timestamp": 1699516860925,
     "user": {
      "displayName": "Xojiakbar Burxonov",
      "userId": "16264803897982475703"
     },
     "user_tz": 0
    },
    "id": "AyViPaQYGTBR",
    "outputId": "0a6a2320-0efc-4142-c979-2e8355771e21"
   },
   "outputs": [
    {
     "ename": "InvalidParameterError",
     "evalue": "The 'y_pred' parameter of confusion_matrix must be an array-like. Got LogisticRegression(penalty=array([[ 0.03900614, -1.54175796, -1.13895409, -1.22907072, -1.39544204,\n        -1.55053497, -0.48060946,  0.64166894, -0.75466805, -0.11809933,\n        -0.53152465],\n       [-1.6459564 , -0.28036122, -0.49853236, -0.1144398 , -0.48402086,\n        -0.42585156, -0.42108458, -0.13340314, -0.68276184, -1.36081309,\n        -0.53152465],\n       [-1.55998892, -0.52556731, -0.97884866, -0.86547871, -0....\n       [-1.49121494, -0.21198644,  0.09464515,  0.45528118, -0.02315128,\n        -0.0380297 ,  0.4930475 , -0.6494938 , -0.6531534 , -1.12208682,\n        -1.20985134],\n       [-0.9066361 ,  0.57078686,  0.37023647,  0.86193694,  0.22104081,\n         0.22568917,  0.49729928,  0.60297774,  0.2287552 ,  0.38010919,\n         0.14680205],\n       [ 0.76113294,  1.4384392 ,  1.46997707,  1.89473584,  1.47639451,\n         1.4589627 ,  0.79492367, -0.4123542 ,  1.6182371 , -0.99582935,\n         1.16429208]])) instead.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInvalidParameterError\u001b[0m                     Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[26], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m cm \u001b[38;5;241m=\u001b[39m confusion_matrix(y_test, y_pred)\n\u001b[1;32m      2\u001b[0m disp \u001b[38;5;241m=\u001b[39m ConfusionMatrixDisplay(confusion_matrix \u001b[38;5;241m=\u001b[39m cm, display_labels \u001b[38;5;241m=\u001b[39m knn\u001b[38;5;241m.\u001b[39mclasses_)\n\u001b[1;32m      3\u001b[0m disp\u001b[38;5;241m.\u001b[39mplot()\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:201\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    198\u001b[0m to_ignore \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mself\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcls\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m    199\u001b[0m params \u001b[38;5;241m=\u001b[39m {k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m params\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m to_ignore}\n\u001b[0;32m--> 201\u001b[0m validate_parameter_constraints(\n\u001b[1;32m    202\u001b[0m     parameter_constraints, params, caller_name\u001b[38;5;241m=\u001b[39mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\n\u001b[1;32m    203\u001b[0m )\n\u001b[1;32m    205\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    206\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m    207\u001b[0m         skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m    208\u001b[0m             prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m    209\u001b[0m         )\n\u001b[1;32m    210\u001b[0m     ):\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:95\u001b[0m, in \u001b[0;36mvalidate_parameter_constraints\u001b[0;34m(parameter_constraints, params, caller_name)\u001b[0m\n\u001b[1;32m     89\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     90\u001b[0m     constraints_str \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m     91\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin([\u001b[38;5;28mstr\u001b[39m(c)\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mfor\u001b[39;00m\u001b[38;5;250m \u001b[39mc\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01min\u001b[39;00m\u001b[38;5;250m \u001b[39mconstraints[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m or\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     92\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconstraints[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     93\u001b[0m     )\n\u001b[0;32m---> 95\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidParameterError(\n\u001b[1;32m     96\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparam_name\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m parameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcaller_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     97\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconstraints_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. Got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparam_val\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     98\u001b[0m )\n",
      "\u001b[0;31mInvalidParameterError\u001b[0m: The 'y_pred' parameter of confusion_matrix must be an array-like. Got LogisticRegression(penalty=array([[ 0.03900614, -1.54175796, -1.13895409, -1.22907072, -1.39544204,\n        -1.55053497, -0.48060946,  0.64166894, -0.75466805, -0.11809933,\n        -0.53152465],\n       [-1.6459564 , -0.28036122, -0.49853236, -0.1144398 , -0.48402086,\n        -0.42585156, -0.42108458, -0.13340314, -0.68276184, -1.36081309,\n        -0.53152465],\n       [-1.55998892, -0.52556731, -0.97884866, -0.86547871, -0....\n       [-1.49121494, -0.21198644,  0.09464515,  0.45528118, -0.02315128,\n        -0.0380297 ,  0.4930475 , -0.6494938 , -0.6531534 , -1.12208682,\n        -1.20985134],\n       [-0.9066361 ,  0.57078686,  0.37023647,  0.86193694,  0.22104081,\n         0.22568917,  0.49729928,  0.60297774,  0.2287552 ,  0.38010919,\n         0.14680205],\n       [ 0.76113294,  1.4384392 ,  1.46997707,  1.89473584,  1.47639451,\n         1.4589627 ,  0.79492367, -0.4123542 ,  1.6182371 , -0.99582935,\n         1.16429208]])) instead."
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix = cm, display_labels = knn.classes_)\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "executionInfo": {
     "elapsed": 254,
     "status": "ok",
     "timestamp": 1699516872843,
     "user": {
      "displayName": "Xojiakbar Burxonov",
      "userId": "16264803897982475703"
     },
     "user_tz": 0
    },
    "id": "qPOcOO9c0F1Z"
   },
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 661,
     "status": "ok",
     "timestamp": 1699516876089,
     "user": {
      "displayName": "Xojiakbar Burxonov",
      "userId": "16264803897982475703"
     },
     "user_tz": 0
    },
    "id": "oqZ56-0-0Q8h",
    "outputId": "f81bba6e-6368-4736-96fc-a25dbb58f396"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 1.02442988\n",
      "Iteration 2, loss = 0.98820983\n",
      "Iteration 3, loss = 0.95328757\n",
      "Iteration 4, loss = 0.91967990\n",
      "Iteration 5, loss = 0.88738468\n",
      "Iteration 6, loss = 0.85639555\n",
      "Iteration 7, loss = 0.82663890\n",
      "Iteration 8, loss = 0.79808574\n",
      "Iteration 9, loss = 0.77077092\n",
      "Iteration 10, loss = 0.74466062\n",
      "Iteration 11, loss = 0.71965070\n",
      "Iteration 12, loss = 0.69573321\n",
      "Iteration 13, loss = 0.67294480\n",
      "Iteration 14, loss = 0.65120159\n",
      "Iteration 15, loss = 0.63042272\n",
      "Iteration 16, loss = 0.61057525\n",
      "Iteration 17, loss = 0.59164120\n",
      "Iteration 18, loss = 0.57357876\n",
      "Iteration 19, loss = 0.55631200\n",
      "Iteration 20, loss = 0.53980112\n",
      "Iteration 21, loss = 0.52400209\n",
      "Iteration 22, loss = 0.50887320\n",
      "Iteration 23, loss = 0.49440965\n",
      "Iteration 24, loss = 0.48056816\n",
      "Iteration 25, loss = 0.46731084\n",
      "Iteration 26, loss = 0.45461256\n",
      "Iteration 27, loss = 0.44244690\n",
      "Iteration 28, loss = 0.43076242\n",
      "Iteration 29, loss = 0.41954682\n",
      "Iteration 30, loss = 0.40878184\n",
      "Iteration 31, loss = 0.39845060\n",
      "Iteration 32, loss = 0.38851859\n",
      "Iteration 33, loss = 0.37896202\n",
      "Iteration 34, loss = 0.36975186\n",
      "Iteration 35, loss = 0.36089364\n",
      "Iteration 36, loss = 0.35237519\n",
      "Iteration 37, loss = 0.34417148\n",
      "Iteration 38, loss = 0.33626627\n",
      "Iteration 39, loss = 0.32865120\n",
      "Iteration 40, loss = 0.32130239\n",
      "Iteration 41, loss = 0.31421075\n",
      "Iteration 42, loss = 0.30736801\n",
      "Iteration 43, loss = 0.30076763\n",
      "Iteration 44, loss = 0.29440059\n",
      "Iteration 45, loss = 0.28825673\n",
      "Iteration 46, loss = 0.28231709\n",
      "Iteration 47, loss = 0.27657454\n",
      "Iteration 48, loss = 0.27102271\n",
      "Iteration 49, loss = 0.26565493\n",
      "Iteration 50, loss = 0.26046362\n",
      "Iteration 51, loss = 0.25544282\n",
      "Iteration 52, loss = 0.25059497\n",
      "Iteration 53, loss = 0.24590425\n",
      "Iteration 54, loss = 0.24136941\n",
      "Iteration 55, loss = 0.23697899\n",
      "Iteration 56, loss = 0.23272121\n",
      "Iteration 57, loss = 0.22859782\n",
      "Iteration 58, loss = 0.22461081\n",
      "Iteration 59, loss = 0.22074692\n",
      "Iteration 60, loss = 0.21700646\n",
      "Iteration 61, loss = 0.21337715\n",
      "Iteration 62, loss = 0.20985715\n",
      "Iteration 63, loss = 0.20643615\n",
      "Iteration 64, loss = 0.20311325\n",
      "Iteration 65, loss = 0.19987864\n",
      "Iteration 66, loss = 0.19672769\n",
      "Iteration 67, loss = 0.19366714\n",
      "Iteration 68, loss = 0.19069591\n",
      "Iteration 69, loss = 0.18780706\n",
      "Iteration 70, loss = 0.18499705\n",
      "Iteration 71, loss = 0.18226397\n",
      "Iteration 72, loss = 0.17960463\n",
      "Iteration 73, loss = 0.17701533\n",
      "Iteration 74, loss = 0.17449830\n",
      "Iteration 75, loss = 0.17204732\n",
      "Iteration 76, loss = 0.16965912\n",
      "Iteration 77, loss = 0.16732812\n",
      "Iteration 78, loss = 0.16505216\n",
      "Iteration 79, loss = 0.16283090\n",
      "Iteration 80, loss = 0.16066167\n",
      "Iteration 81, loss = 0.15854491\n",
      "Iteration 82, loss = 0.15647593\n",
      "Iteration 83, loss = 0.15445088\n",
      "Iteration 84, loss = 0.15247030\n",
      "Iteration 85, loss = 0.15053313\n",
      "Iteration 86, loss = 0.14864489\n",
      "Iteration 87, loss = 0.14679538\n",
      "Iteration 88, loss = 0.14497808\n",
      "Iteration 89, loss = 0.14319610\n",
      "Iteration 90, loss = 0.14144664\n",
      "Iteration 91, loss = 0.13973178\n",
      "Iteration 92, loss = 0.13805115\n",
      "Iteration 93, loss = 0.13640102\n",
      "Iteration 94, loss = 0.13478100\n",
      "Iteration 95, loss = 0.13319303\n",
      "Iteration 96, loss = 0.13163401\n",
      "Iteration 97, loss = 0.13010122\n",
      "Iteration 98, loss = 0.12860308\n",
      "Iteration 99, loss = 0.12713459\n",
      "Iteration 100, loss = 0.12568980\n",
      "Iteration 101, loss = 0.12426704\n",
      "Iteration 102, loss = 0.12286789\n",
      "Iteration 103, loss = 0.12149167\n",
      "Iteration 104, loss = 0.12013440\n",
      "Iteration 105, loss = 0.11879690\n",
      "Iteration 106, loss = 0.11748096\n",
      "Iteration 107, loss = 0.11618718\n",
      "Iteration 108, loss = 0.11490880\n",
      "Iteration 109, loss = 0.11365228\n",
      "Iteration 110, loss = 0.11241755\n",
      "Iteration 111, loss = 0.11121632\n",
      "Iteration 112, loss = 0.11004385\n",
      "Iteration 113, loss = 0.10889378\n",
      "Iteration 114, loss = 0.10776747\n",
      "Iteration 115, loss = 0.10665626\n",
      "Iteration 116, loss = 0.10555861\n",
      "Iteration 117, loss = 0.10448453\n",
      "Iteration 118, loss = 0.10342708\n",
      "Iteration 119, loss = 0.10238195\n",
      "Iteration 120, loss = 0.10134839\n",
      "Iteration 121, loss = 0.10032773\n",
      "Iteration 122, loss = 0.09932287\n",
      "Iteration 123, loss = 0.09833602\n",
      "Iteration 124, loss = 0.09736350\n",
      "Iteration 125, loss = 0.09640014\n",
      "Iteration 126, loss = 0.09544780\n",
      "Iteration 127, loss = 0.09450752\n",
      "Iteration 128, loss = 0.09357578\n",
      "Iteration 129, loss = 0.09265464\n",
      "Iteration 130, loss = 0.09174605\n",
      "Iteration 131, loss = 0.09085497\n",
      "Iteration 132, loss = 0.08997479\n",
      "Iteration 133, loss = 0.08910456\n",
      "Iteration 134, loss = 0.08824452\n",
      "Iteration 135, loss = 0.08739459\n",
      "Iteration 136, loss = 0.08655587\n",
      "Iteration 137, loss = 0.08572483\n",
      "Iteration 138, loss = 0.08490109\n",
      "Iteration 139, loss = 0.08408992\n",
      "Iteration 140, loss = 0.08329298\n",
      "Iteration 141, loss = 0.08250552\n",
      "Iteration 142, loss = 0.08173395\n",
      "Iteration 143, loss = 0.08097064\n",
      "Iteration 144, loss = 0.08021285\n",
      "Iteration 145, loss = 0.07946090\n",
      "Iteration 146, loss = 0.07871608\n",
      "Iteration 147, loss = 0.07798271\n",
      "Iteration 148, loss = 0.07725626\n",
      "Iteration 149, loss = 0.07653671\n",
      "Iteration 150, loss = 0.07582478\n",
      "Iteration 151, loss = 0.07512119\n",
      "Iteration 152, loss = 0.07442490\n",
      "Iteration 153, loss = 0.07373496\n",
      "Iteration 154, loss = 0.07305278\n",
      "Iteration 155, loss = 0.07237958\n",
      "Iteration 156, loss = 0.07171336\n",
      "Iteration 157, loss = 0.07105575\n",
      "Iteration 158, loss = 0.07040539\n",
      "Iteration 159, loss = 0.06976358\n",
      "Iteration 160, loss = 0.06912793\n",
      "Iteration 161, loss = 0.06850012\n",
      "Iteration 162, loss = 0.06788207\n",
      "Iteration 163, loss = 0.06726901\n",
      "Iteration 164, loss = 0.06666571\n",
      "Iteration 165, loss = 0.06607047\n",
      "Iteration 166, loss = 0.06548193\n",
      "Iteration 167, loss = 0.06490002\n",
      "Iteration 168, loss = 0.06432599\n",
      "Iteration 169, loss = 0.06375795\n",
      "Iteration 170, loss = 0.06319580\n",
      "Iteration 171, loss = 0.06263870\n",
      "Iteration 172, loss = 0.06208870\n",
      "Iteration 173, loss = 0.06154735\n",
      "Iteration 174, loss = 0.06101075\n",
      "Iteration 175, loss = 0.06048091\n",
      "Iteration 176, loss = 0.05995503\n",
      "Iteration 177, loss = 0.05943819\n",
      "Iteration 178, loss = 0.05892567\n",
      "Iteration 179, loss = 0.05841878\n",
      "Iteration 180, loss = 0.05791727\n",
      "Iteration 181, loss = 0.05742271\n",
      "Iteration 182, loss = 0.05693363\n",
      "Iteration 183, loss = 0.05644979\n",
      "Iteration 184, loss = 0.05596971\n",
      "Iteration 185, loss = 0.05549352\n",
      "Iteration 186, loss = 0.05502396\n",
      "Iteration 187, loss = 0.05456065\n",
      "Iteration 188, loss = 0.05410123\n",
      "Iteration 189, loss = 0.05364494\n",
      "Iteration 190, loss = 0.05319261\n",
      "Iteration 191, loss = 0.05274530\n",
      "Iteration 192, loss = 0.05230675\n",
      "Iteration 193, loss = 0.05187284\n",
      "Iteration 194, loss = 0.05144174\n",
      "Iteration 195, loss = 0.05101505\n",
      "Iteration 196, loss = 0.05059944\n",
      "Iteration 197, loss = 0.05018971\n",
      "Iteration 198, loss = 0.04978451\n",
      "Iteration 199, loss = 0.04938576\n",
      "Iteration 200, loss = 0.04898910\n",
      "Iteration 201, loss = 0.04859572\n",
      "Iteration 202, loss = 0.04820673\n",
      "Iteration 203, loss = 0.04782214\n",
      "Iteration 204, loss = 0.04744431\n",
      "Iteration 205, loss = 0.04707081\n",
      "Iteration 206, loss = 0.04669958\n",
      "Iteration 207, loss = 0.04633271\n",
      "Iteration 208, loss = 0.04597041\n",
      "Iteration 209, loss = 0.04561157\n",
      "Iteration 210, loss = 0.04525655\n",
      "Iteration 211, loss = 0.04490492\n",
      "Iteration 212, loss = 0.04455757\n",
      "Iteration 213, loss = 0.04421432\n",
      "Iteration 214, loss = 0.04387281\n",
      "Iteration 215, loss = 0.04353620\n",
      "Iteration 216, loss = 0.04320443\n",
      "Iteration 217, loss = 0.04287600\n",
      "Iteration 218, loss = 0.04255011\n",
      "Iteration 219, loss = 0.04222603\n",
      "Iteration 220, loss = 0.04190676\n",
      "Iteration 221, loss = 0.04159159\n",
      "Iteration 222, loss = 0.04127913\n",
      "Iteration 223, loss = 0.04096820\n",
      "Iteration 224, loss = 0.04066059\n",
      "Iteration 225, loss = 0.04035612\n",
      "Iteration 226, loss = 0.04005607\n",
      "Iteration 227, loss = 0.03975841\n",
      "Iteration 228, loss = 0.03946537\n",
      "Iteration 229, loss = 0.03917619\n",
      "Iteration 230, loss = 0.03888823\n",
      "Iteration 231, loss = 0.03860179\n",
      "Iteration 232, loss = 0.03831999\n",
      "Iteration 233, loss = 0.03804069\n",
      "Iteration 234, loss = 0.03776412\n",
      "Iteration 235, loss = 0.03748981\n",
      "Iteration 236, loss = 0.03721909\n",
      "Iteration 237, loss = 0.03695098\n",
      "Iteration 238, loss = 0.03668648\n",
      "Iteration 239, loss = 0.03642271\n",
      "Iteration 240, loss = 0.03615940\n",
      "Iteration 241, loss = 0.03590164\n",
      "Iteration 242, loss = 0.03564611\n",
      "Iteration 243, loss = 0.03539258\n",
      "Iteration 244, loss = 0.03514127\n",
      "Iteration 245, loss = 0.03489169\n",
      "Iteration 246, loss = 0.03464526\n",
      "Iteration 247, loss = 0.03439999\n",
      "Iteration 248, loss = 0.03415692\n",
      "Iteration 249, loss = 0.03391540\n",
      "Iteration 250, loss = 0.03367587\n",
      "Iteration 251, loss = 0.03343752\n",
      "Iteration 252, loss = 0.03320417\n",
      "Iteration 253, loss = 0.03297310\n",
      "Iteration 254, loss = 0.03274311\n",
      "Iteration 255, loss = 0.03251447\n",
      "Iteration 256, loss = 0.03228957\n",
      "Iteration 257, loss = 0.03206565\n",
      "Iteration 258, loss = 0.03184320\n",
      "Iteration 259, loss = 0.03162236\n",
      "Iteration 260, loss = 0.03140424\n",
      "Iteration 261, loss = 0.03118792\n",
      "Iteration 262, loss = 0.03097340\n",
      "Iteration 263, loss = 0.03076052\n",
      "Iteration 264, loss = 0.03054991\n",
      "Iteration 265, loss = 0.03034158\n",
      "Iteration 266, loss = 0.03013406\n",
      "Iteration 267, loss = 0.02992839\n",
      "Iteration 268, loss = 0.02972600\n",
      "Iteration 269, loss = 0.02952499\n",
      "Iteration 270, loss = 0.02932475\n",
      "Iteration 271, loss = 0.02912589\n",
      "Iteration 272, loss = 0.02892946\n",
      "Iteration 273, loss = 0.02873459\n",
      "Iteration 274, loss = 0.02854094\n",
      "Iteration 275, loss = 0.02834948\n",
      "Iteration 276, loss = 0.02815962\n",
      "Iteration 277, loss = 0.02797133\n",
      "Iteration 278, loss = 0.02778454\n",
      "Iteration 279, loss = 0.02759904\n",
      "Iteration 280, loss = 0.02741547\n",
      "Iteration 281, loss = 0.02723323\n",
      "Iteration 282, loss = 0.02705283\n",
      "Iteration 283, loss = 0.02687423\n",
      "Iteration 284, loss = 0.02669710\n",
      "Iteration 285, loss = 0.02652042\n",
      "Iteration 286, loss = 0.02634651\n",
      "Iteration 287, loss = 0.02617452\n",
      "Iteration 288, loss = 0.02600384\n",
      "Iteration 289, loss = 0.02583372\n",
      "Iteration 290, loss = 0.02566545\n",
      "Iteration 291, loss = 0.02549916\n",
      "Iteration 292, loss = 0.02533368\n",
      "Iteration 293, loss = 0.02516985\n",
      "Iteration 294, loss = 0.02500727\n",
      "Iteration 295, loss = 0.02484673\n",
      "Iteration 296, loss = 0.02468858\n",
      "Iteration 297, loss = 0.02453156\n",
      "Iteration 298, loss = 0.02437576\n",
      "Iteration 299, loss = 0.02422053\n",
      "Iteration 300, loss = 0.02406654\n",
      "Iteration 301, loss = 0.02391457\n",
      "Iteration 302, loss = 0.02376425\n",
      "Iteration 303, loss = 0.02361455\n",
      "Iteration 304, loss = 0.02346610\n",
      "Iteration 305, loss = 0.02331970\n",
      "Iteration 306, loss = 0.02317464\n",
      "Iteration 307, loss = 0.02303102\n",
      "Iteration 308, loss = 0.02288829\n",
      "Iteration 309, loss = 0.02274624\n",
      "Iteration 310, loss = 0.02260531\n",
      "Iteration 311, loss = 0.02246542\n",
      "Iteration 312, loss = 0.02232747\n",
      "Iteration 313, loss = 0.02219030\n",
      "Iteration 314, loss = 0.02205413\n",
      "Iteration 315, loss = 0.02191880\n",
      "Iteration 316, loss = 0.02178341\n",
      "Iteration 317, loss = 0.02164998\n",
      "Iteration 318, loss = 0.02151813\n",
      "Iteration 319, loss = 0.02138747\n",
      "Iteration 320, loss = 0.02125696\n",
      "Iteration 321, loss = 0.02112782\n",
      "Iteration 322, loss = 0.02099963\n",
      "Iteration 323, loss = 0.02087270\n",
      "Iteration 324, loss = 0.02074635\n",
      "Iteration 325, loss = 0.02062114\n",
      "Iteration 326, loss = 0.02049751\n",
      "Iteration 327, loss = 0.02037497\n",
      "Iteration 328, loss = 0.02025299\n",
      "Iteration 329, loss = 0.02013153\n",
      "Iteration 330, loss = 0.02001116\n",
      "Iteration 331, loss = 0.01989229\n",
      "Iteration 332, loss = 0.01977380\n",
      "Iteration 333, loss = 0.01965671\n",
      "Iteration 334, loss = 0.01954005\n",
      "Iteration 335, loss = 0.01942432\n",
      "Iteration 336, loss = 0.01930902\n",
      "Iteration 337, loss = 0.01919552\n",
      "Iteration 338, loss = 0.01908274\n",
      "Iteration 339, loss = 0.01896994\n",
      "Iteration 340, loss = 0.01885853\n",
      "Iteration 341, loss = 0.01874812\n",
      "Iteration 342, loss = 0.01863883\n",
      "Iteration 343, loss = 0.01852991\n",
      "Iteration 344, loss = 0.01842165\n",
      "Iteration 345, loss = 0.01831463\n",
      "Iteration 346, loss = 0.01820809\n",
      "Iteration 347, loss = 0.01810259\n",
      "Iteration 348, loss = 0.01799823\n",
      "Iteration 349, loss = 0.01789411\n",
      "Iteration 350, loss = 0.01779105\n",
      "Iteration 351, loss = 0.01768904\n",
      "Iteration 352, loss = 0.01758782\n",
      "Iteration 353, loss = 0.01748687\n",
      "Iteration 354, loss = 0.01738701\n",
      "Iteration 355, loss = 0.01728788\n",
      "Iteration 356, loss = 0.01718962\n",
      "Iteration 357, loss = 0.01709267\n",
      "Iteration 358, loss = 0.01699623\n",
      "Iteration 359, loss = 0.01690000\n",
      "Iteration 360, loss = 0.01680497\n",
      "Iteration 361, loss = 0.01671071\n",
      "Iteration 362, loss = 0.01661717\n",
      "Iteration 363, loss = 0.01652474\n",
      "Iteration 364, loss = 0.01643241\n",
      "Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n"
     ]
    },
    {
     "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>MLPClassifier(max_iter=400, random_state=1, verbose=True)</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\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(max_iter=400, random_state=1, verbose=True)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MLPClassifier(max_iter=400, random_state=1, verbose=True)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MLP= MLPClassifier(random_state=1,\n",
    "                   max_iter=400,\n",
    "                   verbose=True,\n",
    "                   )\n",
    "MLP.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 247,
     "status": "ok",
     "timestamp": 1699516880298,
     "user": {
      "displayName": "Xojiakbar Burxonov",
      "userId": "16264803897982475703"
     },
     "user_tz": 0
    },
    "id": "BGnlG_qI0shP",
    "outputId": "138f3f18-0a98-4324-940f-f359b50348ad"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['c_avellana' 'c_americana' 'c_americana' 'c_avellana' 'c_cornuta'\n",
      " 'c_avellana' 'c_cornuta' 'c_avellana' 'c_americana' 'c_americana'\n",
      " 'c_avellana' 'c_cornuta' 'c_cornuta' 'c_avellana' 'c_americana'\n",
      " 'c_americana' 'c_americana' 'c_avellana' 'c_americana' 'c_cornuta'\n",
      " 'c_cornuta' 'c_americana' 'c_cornuta' 'c_americana' 'c_cornuta'\n",
      " 'c_americana' 'c_cornuta' 'c_avellana' 'c_americana' 'c_cornuta'\n",
      " 'c_cornuta' 'c_avellana' 'c_americana' 'c_americana' 'c_avellana'\n",
      " 'c_americana' 'c_avellana' 'c_cornuta' 'c_americana' 'c_cornuta'\n",
      " 'c_americana' 'c_americana' 'c_avellana' 'c_americana' 'c_cornuta'\n",
      " 'c_avellana' 'c_avellana' 'c_cornuta' 'c_cornuta' 'c_cornuta'\n",
      " 'c_americana' 'c_cornuta' 'c_cornuta' 'c_americana' 'c_avellana'\n",
      " 'c_cornuta' 'c_avellana' 'c_americana' 'c_cornuta' 'c_americana'\n",
      " 'c_cornuta' 'c_americana' 'c_avellana' 'c_avellana' 'c_americana'\n",
      " 'c_americana' 'c_cornuta']\n"
     ]
    }
   ],
   "source": [
    "y_pred=MLP.predict(X_test)\n",
    "print(y_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 490
    },
    "executionInfo": {
     "elapsed": 833,
     "status": "ok",
     "timestamp": 1699517138493,
     "user": {
      "displayName": "Xojiakbar Burxonov",
      "userId": "16264803897982475703"
     },
     "user_tz": 0
    },
    "id": "zwPlZTKUGabj",
    "outputId": "16054627-e64e-4beb-ac72-5c4fc76917fa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Train Accuracy vs. Number of Neighbors')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(neighbours, train_scores, label='train accuracy')\n",
    "plt.xlabel('Number of Neighbors')\n",
    "plt.ylabel('Train Accuracy')\n",
    "plt.title('Train Accuracy vs. Number of Neighbors')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "executionInfo": {
     "elapsed": 362,
     "status": "ok",
     "timestamp": 1699517742403,
     "user": {
      "displayName": "Xojiakbar Burxonov",
      "userId": "16264803897982475703"
     },
     "user_tz": 0
    },
    "id": "X8PVs1OqITry",
    "outputId": "b8e34657-68c0-4b32-fbee-fbaadc51c466"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "models = ['Logistic Regression', 'MLP Classifier', 'K-Nearest Neighbors']\n",
    "accuracy = test_scores_models  # Example accuracy values for each model\n",
    "\n",
    "plt.bar(models, accuracy)\n",
    "plt.xlabel('Models')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Accuracy Comparison of Different Models')\n",
    "plt.ylim(0, 1)  # Set the y-axis limits to ensure the accuracy values are displayed properly\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UXaPDEBHJkif"
   },
   "source": [
    "# **Important** -  Lab logbook requirement:\n",
    "# Put bar plot comparing the accracy of three models in you lablogbook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9253731343283582, 0.9552238805970149, 0.9552238805970149]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_scores_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 3 artists>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a bar plot to compare the accuracies of three models\n",
    "\n",
    "models=['k-NN', 'Logistic Regression' , 'MLP classifer']\n",
    " \n",
    "plt.bar(models, test_scores_models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}