Exploratory data analysis (EDA) On IRIS.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=\"center\">The Iris dataset for Exploratory data analysis (EDA) On IRIS and  KNN</h1>\n",
    "\n",
    "![](https://lh3.googleusercontent.com/-CTVMFX2_Af0/XRbC0eWP1eI/AAAAAAAAe7c/BWRe831n5IwjNdpqkstApWPfpGN2RLh6gCK8BGAs/s0/2019-06-28.png)\n",
    "\n",
    "\n",
    "![](https://lh3.googleusercontent.com/-_INEf8fVrdQ/XRbCuoagUcI/AAAAAAAAe7Y/EAQBstVSRysrdMMt8Cj54TiAItbCJsCLQCK8BGAs/s0/2019-06-28.png)\n",
    "\n",
    "* Features:\n",
    "    * Petal length  \n",
    "    * Petal width  \n",
    "    * Sepal length  \n",
    "    * Sepal width\n",
    "* Target variable: Species\n",
    "    * Versicolor  \n",
    "    * Virginica  \n",
    "    * Setosa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])\n"
     ]
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "import pandas as pd  \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt  \n",
    "plt.style.use('ggplot')\n",
    "iris = datasets.load_iris()  \n",
    "type(iris)\n",
    "print(iris.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "       [5.1, 3.8, 1.6, 0.2],\n",
       "       [4.6, 3.2, 1.4, 0.2],\n",
       "       [5.3, 3.7, 1.5, 0.2],\n",
       "       [5. , 3.3, 1.4, 0.2],\n",
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       "       [6.9, 3.1, 4.9, 1.5],\n",
       "       [5.5, 2.3, 4. , 1.3],\n",
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       "       [5.2, 2.7, 3.9, 1.4],\n",
       "       [5. , 2. , 3.5, 1. ],\n",
       "       [5.9, 3. , 4.2, 1.5],\n",
       "       [6. , 2.2, 4. , 1. ],\n",
       "       [6.1, 2.9, 4.7, 1.4],\n",
       "       [5.6, 2.9, 3.6, 1.3],\n",
       "       [6.7, 3.1, 4.4, 1.4],\n",
       "       [5.6, 3. , 4.5, 1.5],\n",
       "       [5.8, 2.7, 4.1, 1. ],\n",
       "       [6.2, 2.2, 4.5, 1.5],\n",
       "       [5.6, 2.5, 3.9, 1.1],\n",
       "       [5.9, 3.2, 4.8, 1.8],\n",
       "       [6.1, 2.8, 4. , 1.3],\n",
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       "       [5.7, 2.6, 3.5, 1. ],\n",
       "       [5.5, 2.4, 3.8, 1.1],\n",
       "       [5.5, 2.4, 3.7, 1. ],\n",
       "       [5.8, 2.7, 3.9, 1.2],\n",
       "       [6. , 2.7, 5.1, 1.6],\n",
       "       [5.4, 3. , 4.5, 1.5],\n",
       "       [6. , 3.4, 4.5, 1.6],\n",
       "       [6.7, 3.1, 4.7, 1.5],\n",
       "       [6.3, 2.3, 4.4, 1.3],\n",
       "       [5.6, 3. , 4.1, 1.3],\n",
       "       [5.5, 2.5, 4. , 1.3],\n",
       "       [5.5, 2.6, 4.4, 1.2],\n",
       "       [6.1, 3. , 4.6, 1.4],\n",
       "       [5.8, 2.6, 4. , 1.2],\n",
       "       [5. , 2.3, 3.3, 1. ],\n",
       "       [5.6, 2.7, 4.2, 1.3],\n",
       "       [5.7, 3. , 4.2, 1.2],\n",
       "       [5.7, 2.9, 4.2, 1.3],\n",
       "       [6.2, 2.9, 4.3, 1.3],\n",
       "       [5.1, 2.5, 3. , 1.1],\n",
       "       [5.7, 2.8, 4.1, 1.3],\n",
       "       [6.3, 3.3, 6. , 2.5],\n",
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       "       [5.6, 2.8, 4.9, 2. ],\n",
       "       [7.7, 2.8, 6.7, 2. ],\n",
       "       [6.3, 2.7, 4.9, 1.8],\n",
       "       [6.7, 3.3, 5.7, 2.1],\n",
       "       [7.2, 3.2, 6. , 1.8],\n",
       "       [6.2, 2.8, 4.8, 1.8],\n",
       "       [6.1, 3. , 4.9, 1.8],\n",
       "       [6.4, 2.8, 5.6, 2.1],\n",
       "       [7.2, 3. , 5.8, 1.6],\n",
       "       [7.4, 2.8, 6.1, 1.9],\n",
       "       [7.9, 3.8, 6.4, 2. ],\n",
       "       [6.4, 2.8, 5.6, 2.2],\n",
       "       [6.3, 2.8, 5.1, 1.5],\n",
       "       [6.1, 2.6, 5.6, 1.4],\n",
       "       [7.7, 3. , 6.1, 2.3],\n",
       "       [6.3, 3.4, 5.6, 2.4],\n",
       "       [6.4, 3.1, 5.5, 1.8],\n",
       "       [6. , 3. , 4.8, 1.8],\n",
       "       [6.9, 3.1, 5.4, 2.1],\n",
       "       [6.7, 3.1, 5.6, 2.4],\n",
       "       [6.9, 3.1, 5.1, 2.3],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [6.8, 3.2, 5.9, 2.3],\n",
       "       [6.7, 3.3, 5.7, 2.5],\n",
       "       [6.7, 3. , 5.2, 2.3],\n",
       "       [6.3, 2.5, 5. , 1.9],\n",
       "       [6.5, 3. , 5.2, 2. ],\n",
       "       [6.2, 3.4, 5.4, 2.3],\n",
       "       [5.9, 3. , 5.1, 1.8]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'.. _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 ...'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.DESCR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'c:\\\\users\\\\kumar\\\\appdata\\\\local\\\\programs\\\\python\\\\python36\\\\lib\\\\site-packages\\\\sklearn\\\\datasets\\\\data\\\\iris.csv'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(iris.data)\n",
    "type(iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = iris.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape and Dim Of the IRIS DATA (150, 4) \n",
      "Dim 2\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape and Dim Of the IRIS DATA\",X.shape,\"\\nDim\",X.ndim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape and Dim Of the IRIS TARGET (150,) \n",
      "Dim 1\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape and Dim Of the IRIS TARGET\",y.shape,\"\\nDim\",y.ndim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df =pd.DataFrame(X,columns = iris.feature_names)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.plotting import scatter_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001C7A92AE7F0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001C7A92FD518>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001C7A9682978>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001C7A96B7EB8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001C7AAD734A8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001C7AADA2A58>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001C7AADE2048>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001C7AAE135F8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001C7AAE13630>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001C7AAE81160>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001C7AAEB1710>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001C7AAEE2CC0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001C7AAF212B0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001C7AAF4F860>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001C7AAF81E10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001C7AAFC1400>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_matrix(df, c =y, figsize = [16, 16])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using scikit-learn to fit a classifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=6, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=6, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(iris['data'], iris['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris['data'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150,)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris['target'].shape"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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