Task 1_iris data analysis_Nexus.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bf190506",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ec8cdb48",
   "metadata": {},
   "outputs": [
    {
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       " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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       " 'frame': None,\n",
       " 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'),\n",
       " 'DESCR': '.. _iris_dataset:\\n\\nIris plants dataset\\n--------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 150 (50 in each of three classes)\\n    :Number of Attributes: 4 numeric, predictive attributes and the class\\n    :Attribute Information:\\n        - sepal length in cm\\n        - sepal width in cm\\n        - petal length in cm\\n        - petal width in cm\\n        - class:\\n                - Iris-Setosa\\n                - Iris-Versicolour\\n                - Iris-Virginica\\n                \\n    :Summary Statistics:\\n\\n    ============== ==== ==== ======= ===== ====================\\n                    Min  Max   Mean    SD   Class Correlation\\n    ============== ==== ==== ======= ===== ====================\\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\\n    ============== ==== ==== ======= ===== ====================\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: 33.3% for each of 3 classes.\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\nfrom Fisher\\'s paper. Note that it\\'s the same as in R, but not as in the UCI\\nMachine Learning Repository, which has two wrong data points.\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\n.. topic:: References\\n\\n   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n     Mathematical Statistics\" (John Wiley, NY, 1950).\\n   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n     Structure and Classification Rule for Recognition in Partially Exposed\\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n     on Information Theory, May 1972, 431-433.\\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n     conceptual clustering system finds 3 classes in the data.\\n   - Many, many more ...',\n",
       " 'feature_names': ['sepal length (cm)',\n",
       "  'sepal width (cm)',\n",
       "  'petal length (cm)',\n",
       "  'petal width (cm)'],\n",
       " 'filename': 'iris.csv',\n",
       " 'data_module': 'sklearn.datasets.data'}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7ed9ec6f",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                  5.1               3.5                1.4               0.2\n",
       "1                  4.9               3.0                1.4               0.2\n",
       "2                  4.7               3.2                1.3               0.2\n",
       "3                  4.6               3.1                1.5               0.2\n",
       "4                  5.0               3.6                1.4               0.2\n",
       "..                 ...               ...                ...               ...\n",
       "145                6.7               3.0                5.2               2.3\n",
       "146                6.3               2.5                5.0               1.9\n",
       "147                6.5               3.0                5.2               2.0\n",
       "148                6.2               3.4                5.4               2.3\n",
       "149                5.9               3.0                5.1               1.8\n",
       "\n",
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(iris.data,columns=iris.feature_names)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "92329d9d",
   "metadata": {},
   "outputs": [
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3fad9266",
   "metadata": {},
   "outputs": [
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       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                  5.1               3.5                1.4               0.2   \n",
       "1                  4.9               3.0                1.4               0.2   \n",
       "2                  4.7               3.2                1.3               0.2   \n",
       "3                  4.6               3.1                1.5               0.2   \n",
       "4                  5.0               3.6                1.4               0.2   \n",
       "..                 ...               ...                ...               ...   \n",
       "145                6.7               3.0                5.2               2.3   \n",
       "146                6.3               2.5                5.0               1.9   \n",
       "147                6.5               3.0                5.2               2.0   \n",
       "148                6.2               3.4                5.4               2.3   \n",
       "149                5.9               3.0                5.1               1.8   \n",
       "\n",
       "     target  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  \n",
       "..      ...  \n",
       "145       2  \n",
       "146       2  \n",
       "147       2  \n",
       "148       2  \n",
       "149       2  \n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target'] = iris.target\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8e4474cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "      <th>flower_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                  5.1               3.5                1.4               0.2   \n",
       "1                  4.9               3.0                1.4               0.2   \n",
       "2                  4.7               3.2                1.3               0.2   \n",
       "3                  4.6               3.1                1.5               0.2   \n",
       "4                  5.0               3.6                1.4               0.2   \n",
       "..                 ...               ...                ...               ...   \n",
       "145                6.7               3.0                5.2               2.3   \n",
       "146                6.3               2.5                5.0               1.9   \n",
       "147                6.5               3.0                5.2               2.0   \n",
       "148                6.2               3.4                5.4               2.3   \n",
       "149                5.9               3.0                5.1               1.8   \n",
       "\n",
       "     target flower_name  \n",
       "0         0      setosa  \n",
       "1         0      setosa  \n",
       "2         0      setosa  \n",
       "3         0      setosa  \n",
       "4         0      setosa  \n",
       "..      ...         ...  \n",
       "145       2   virginica  \n",
       "146       2   virginica  \n",
       "147       2   virginica  \n",
       "148       2   virginica  \n",
       "149       2   virginica  \n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['flower_name'] =df.target.apply(lambda x: iris.target_names[x])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43397419",
   "metadata": {},
   "source": [
    "## Data Science Task (Flower type prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8398deab",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = df.drop(['target','flower_name'], axis='columns')\n",
    "y = df.target\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "#using the train-test-split method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5f14b6cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "model = SVC()\n",
    "#using Support Vector Machine algorithm for classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6d0923a1",
   "metadata": {},
   "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>SVC()</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\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3f20a817",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "073c3b2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\2mris\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[7,5,6,1.4]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aa751ac",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7e79ebd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df0 = df[:50]\n",
    "df1 = df[50:100]\n",
    "df2 = df[100:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "557eebec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "811ff8a9",
   "metadata": {},
   "source": [
    "#### Sepal length vs Sepal Width (Setosa vs Versicolor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3657336c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2ec5e085b90>"
      ]
     },
     "execution_count": 27,
     "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.xlabel('Sepal Length')\n",
    "plt.ylabel('Sepal Width')\n",
    "plt.scatter(df0['sepal length (cm)'], df0['sepal width (cm)'],color=\"red\")\n",
    "plt.scatter(df1['sepal length (cm)'], df1['sepal width (cm)'],color=\"blue\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f9e26d4",
   "metadata": {},
   "source": [
    "#### Petal length vs Petal Width (Setosa vs Versicolor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3946e6e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2ec5e1b9d50>"
      ]
     },
     "execution_count": 29,
     "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.xlabel('Petal Length')\n",
    "plt.ylabel('Petal Width')\n",
    "plt.scatter(df0['petal length (cm)'], df0['petal width (cm)'],color=\"red\")\n",
    "plt.scatter(df1['petal length (cm)'], df1['petal width (cm)'],color=\"blue\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b14f256",
   "metadata": {},
   "source": [
    "#### Sepal length vs Sepal Width (Versicolor vs. Virginica)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cffc7e35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2ec5e262550>"
      ]
     },
     "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.xlabel('Sepal Length')\n",
    "plt.ylabel('Sepal Width')\n",
    "plt.scatter(df1['sepal length (cm)'], df1['sepal width (cm)'],color=\"red\")\n",
    "plt.scatter(df2['sepal length (cm)'], df2['sepal width (cm)'],color=\"blue\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e7f784a",
   "metadata": {},
   "source": [
    "#### Petal length vs Petal Width (Versicolor vs. Virginica)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bf33018a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2ec60405f50>"
      ]
     },
     "execution_count": 31,
     "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.xlabel('Petal Length')\n",
    "plt.ylabel('Petal Width')\n",
    "plt.scatter(df1['petal length (cm)'], df1['petal width (cm)'],color=\"red\")\n",
    "plt.scatter(df2['petal length (cm)'], df2['petal width (cm)'],color=\"blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6308400e",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}