Assignment13-zoo.ipynb

Assignment12-zoo.ipynb

{
  "cells": [
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import pandas as pd\nimport numpy as np\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.model_selection import GridSearchCV\nimport warnings \nwarnings.filterwarnings(\"ignore\")",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "zoo = pd.read_csv('C:/Users/Prathmesh/Downloads/Zoo.csv')\nzoo.head()",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "  animal name  hair  feathers  eggs  milk  airborne  aquatic  predator  \\\n0    aardvark     1         0     0     1         0        0         1   \n1    antelope     1         0     0     1         0        0         0   \n2        bass     0         0     1     0         0        1         1   \n3        bear     1         0     0     1         0        0         1   \n4        boar     1         0     0     1         0        0         1   \n\n   toothed  backbone  breathes  venomous  fins  legs  tail  domestic  catsize  \\\n0        1         1         1         0     0     4     0         0        1   \n1        1         1         1         0     0     4     1         0        1   \n2        1         1         0         0     1     0     1         0        0   \n3        1         1         1         0     0     4     0         0        1   \n4        1         1         1         0     0     4     1         0        1   \n\n   type  \n0     1  \n1     1  \n2     4  \n3     1  \n4     1  ",
            "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>animal name</th>\n      <th>hair</th>\n      <th>feathers</th>\n      <th>eggs</th>\n      <th>milk</th>\n      <th>airborne</th>\n      <th>aquatic</th>\n      <th>predator</th>\n      <th>toothed</th>\n      <th>backbone</th>\n      <th>breathes</th>\n      <th>venomous</th>\n      <th>fins</th>\n      <th>legs</th>\n      <th>tail</th>\n      <th>domestic</th>\n      <th>catsize</th>\n      <th>type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>aardvark</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>antelope</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>bass</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>bear</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>boar</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "zoo.shape",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "(101, 18)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "array = zoo.values",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "x = zoo.iloc[:,1:17]\ny = zoo.iloc[:,17]",
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "n_neighbors = np.array(range(1,40))\nparam_grid = dict(n_neighbors=n_neighbors)",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "scrolled": false
      },
      "cell_type": "code",
      "source": "model = KNeighborsClassifier()\nmodel.fit(x, y)",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "data": {
            "text/plain": "KNeighborsClassifier()"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import matplotlib.pyplot as plt \n%matplotlib inline\n\nk_range = range(1, 41)\nk_scores = []\n\nfor k in k_range:\n    knn = KNeighborsClassifier(n_neighbors=k)\n    scores = cross_val_score(knn, x, y, cv=5)\n    k_scores.append(scores.mean())\n\nplt.plot(k_range, k_scores)\nplt.xlabel('Value of K for KNN')\nplt.ylabel('Cross-Validated Accuracy')\nplt.show()\n\nprint(\"Best accuracy is {} with K = {}\".format(np.max(k_scores),1+k_scores.index(np.max(k_scores))))",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": "Best accuracy is 0.96 with K = 1\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.8.5",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "gist": {
      "id": "",
      "data": {
        "description": "Assignment12-zoo.ipynb",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}

Assignment13-zoo.ipynb