Iris_Flower_Dataset_Analysis.ipynb

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  "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",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.5"
    },
    "colab": {
      "name": "Iris Flower Dataset Analysis.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "s0-fhU3pVW_7"
      },
      "source": [
        "# apply a machine learning classifier to the infamous iris flower dataset \n",
        "# importing libraries\n",
        "import sys\n",
        "import scipy\n",
        "import numpy\n",
        "import matplotlib\n",
        "import pandas\n",
        "import sklearn"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6pLkL5G4VW__",
        "outputId": "a6db0b86-9bff-4b77-b484-82b1fd9c526b"
      },
      "source": [
        "# instead of pre-downloading the csv file you can access it online from this website:\n",
        "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
        "names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']\n",
        "dataset = pandas.read_csv(url, names = names)\n",
        "# gives number of rows and columns\n",
        "print(dataset.shape)\n",
        "# gives first 5 rows of data\n",
        "print(dataset.head(5))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(150, 5)\n",
            "   sepal-length  sepal-width  petal-length  petal-width        class\n",
            "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
            "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
            "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
            "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
            "4           5.0          3.6           1.4          0.2  Iris-setosa\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OEyFyrWXVXAC",
        "outputId": "b869d52b-204e-4645-eed2-63c1bc5e50a8"
      },
      "source": [
        "# get stats\n",
        "print(dataset.describe())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "       sepal-length  sepal-width  petal-length  petal-width\n",
            "count    150.000000   150.000000    150.000000   150.000000\n",
            "mean       5.843333     3.054000      3.758667     1.198667\n",
            "std        0.828066     0.433594      1.764420     0.763161\n",
            "min        4.300000     2.000000      1.000000     0.100000\n",
            "25%        5.100000     2.800000      1.600000     0.300000\n",
            "50%        5.800000     3.000000      4.350000     1.300000\n",
            "75%        6.400000     3.300000      5.100000     1.800000\n",
            "max        7.900000     4.400000      6.900000     2.500000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FSeiOl_KVXAD",
        "outputId": "2088c0d9-e974-4149-98f8-c81991a9a159"
      },
      "source": [
        "# flower class distribution\n",
        "dataset.groupby('class').size()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "class\n",
              "Iris-setosa        50\n",
              "Iris-versicolor    50\n",
              "Iris-virginica     50\n",
              "dtype: int64"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ImI79gYrVXAD",
        "outputId": "8372ed7d-95e4-47a5-e6c4-1eee68ab32f8"
      },
      "source": [
        "# histogram graphs\n",
        "dataset.hist()\n",
        "matplotlib.pyplot.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QhZpn0-WVXAE",
        "outputId": "634ed433-708b-4fed-c292-47a4f0680efa"
      },
      "source": [
        "# split training and test data (80% and 20% respectively)\n",
        "array = dataset.values\n",
        "# flower features\n",
        "X = array[:, 0:4]\n",
        "# flower classes\n",
        "Y = array[:, 4]\n",
        "test_size = .20\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "# X are the features, Y are the classes\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size)\n",
        "from sklearn import tree\n",
        "classifier=tree.DecisionTreeClassifier()\n",
        "# train our model\n",
        "classifier.fit(X_train,Y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
              "            max_features=None, max_leaf_nodes=None,\n",
              "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "            min_samples_leaf=1, min_samples_split=2,\n",
              "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
              "            splitter='best')"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ahGkOBwVXAF",
        "outputId": "095e7c9c-111e-4cdb-bd3c-f11c5c120faf"
      },
      "source": [
        "predictions = classifier.predict(X_test)\n",
        "from sklearn.metrics import accuracy_score\n",
        "# pre-built function computes the accuracy of our model\n",
        "print(accuracy_score(predictions,Y_test) * 100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "96.66666666666667\n"
          ]
        }
      ]
    }
  ]
}