heart.ipynb

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  "metadata": {
    "colab": {
      "name": "heart.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "L0lfcoIFTwYe"
      ],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "o1oJ4NLETFQl",
        "colab_type": "code",
        "outputId": "7e1d6cb1-6f45-4536-8e30-bc7627dfa424",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "cd /content/drive/My Drive/Dava with ML"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/Dava with ML\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nfN4l1kDT4yz",
        "colab_type": "code",
        "outputId": "0d24a265-1a64-463a-e8fd-8074bf0f63e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "!unzip heart-disease-uci.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  heart-disease-uci.zip\n",
            "replace heart.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: n\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NgXKXi54Uq94",
        "colab_type": "code",
        "outputId": "17e2cc19-269f-4d98-f183-62a09df54661",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "import sklearn.metrics as m"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sIRz4K0zf8cm",
        "colab_type": "code",
        "outputId": "1d9fbaf9-d10c-49cf-d4d3-09f25b53921d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "ls"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "breast-cancer-wisconsin-data.zip  heart.ipynb\n",
            "Cancer.ipynb                      indian_liver_patient.csv\n",
            "chronic-kidney-disease.zip        indian-liver-patient-records.zip\n",
            "data.csv                          kidney.ipynb\n",
            "diabetes.csv                      liver.ipynb\n",
            "Diabeties.ipynb                   new_model.csv\n",
            "heart.csv                         pima-indians-diabetes-database.zip\n",
            "heart-disease-uci.zip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_CWAzBMFg2NG",
        "colab_type": "code",
        "outputId": "89186ceb-56a0-41b2-fff2-290731c07924",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "dataset=pd.read_csv('heart.csv')\n",
        "dataset"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
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              "<p>303 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
              "0     63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
              "1     37    1   2       130   250    0  ...      0      3.5      0   0     2       1\n",
              "2     41    0   1       130   204    0  ...      0      1.4      2   0     2       1\n",
              "3     56    1   1       120   236    0  ...      0      0.8      2   0     2       1\n",
              "4     57    0   0       120   354    0  ...      1      0.6      2   0     2       1\n",
              "..   ...  ...  ..       ...   ...  ...  ...    ...      ...    ...  ..   ...     ...\n",
              "298   57    0   0       140   241    0  ...      1      0.2      1   0     3       0\n",
              "299   45    1   3       110   264    0  ...      0      1.2      1   0     3       0\n",
              "300   68    1   0       144   193    1  ...      0      3.4      1   2     3       0\n",
              "301   57    1   0       130   131    0  ...      1      1.2      1   1     3       0\n",
              "302   57    0   1       130   236    0  ...      0      0.0      1   1     2       0\n",
              "\n",
              "[303 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jf_8_vWbhEAr",
        "colab_type": "code",
        "outputId": "0e6d859a-9b6d-4461-cbeb-4ede0ad18d3f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "dataset.isnull().values.any()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "994KzSfQr32m",
        "colab_type": "code",
        "outputId": "562720fc-8b9e-4704-bf73-aaf557ef4242",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637
        }
      },
      "source": [
        "plt.figure(figsize=(30,15))\n",
        "sns.heatmap(dataset.corr(),annot=True)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 2160x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MbqQ--nxDovW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#dataset=dataset.drop('slope',axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sCjPVMMhNjvc",
        "colab_type": "code",
        "outputId": "2081c6e0-6f36-4c5d-fd23-86a08eeb0ce9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637
        }
      },
      "source": [
        "plt.figure(figsize=(30,15))\n",
        "sns.heatmap(dataset.corr(),annot=True)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2160x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T8E2kRT2O-jA",
        "colab_type": "code",
        "outputId": "626e211d-41b3-49bc-d867-c0b4b5f0a7d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "dataset"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</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>37</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>178</td>\n",
              "      <td>0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>354</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>163</td>\n",
              "      <td>1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>140</td>\n",
              "      <td>241</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>123</td>\n",
              "      <td>1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>110</td>\n",
              "      <td>264</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>132</td>\n",
              "      <td>0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>68</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>144</td>\n",
              "      <td>193</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>141</td>\n",
              "      <td>0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>57</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>130</td>\n",
              "      <td>131</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>115</td>\n",
              "      <td>1</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>174</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>303 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
              "0     63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
              "1     37    1   2       130   250    0  ...      0      3.5      0   0     2       1\n",
              "2     41    0   1       130   204    0  ...      0      1.4      2   0     2       1\n",
              "3     56    1   1       120   236    0  ...      0      0.8      2   0     2       1\n",
              "4     57    0   0       120   354    0  ...      1      0.6      2   0     2       1\n",
              "..   ...  ...  ..       ...   ...  ...  ...    ...      ...    ...  ..   ...     ...\n",
              "298   57    0   0       140   241    0  ...      1      0.2      1   0     3       0\n",
              "299   45    1   3       110   264    0  ...      0      1.2      1   0     3       0\n",
              "300   68    1   0       144   193    1  ...      0      3.4      1   2     3       0\n",
              "301   57    1   0       130   131    0  ...      1      1.2      1   1     3       0\n",
              "302   57    0   1       130   236    0  ...      0      0.0      1   1     2       0\n",
              "\n",
              "[303 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IF3Ybag0N1R8",
        "colab_type": "text"
      },
      "source": [
        "Spliting data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wZ0PKc4xNmc9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features=dataset.iloc[:,:-1]\n",
        "labels=dataset.iloc[:,[-1]]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yo2PMzLFSLYI",
        "colab_type": "code",
        "outputId": "bb820236-32f1-4ea7-d1b6-57bddea42efc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "features"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>298</th>\n",
              "      <td>57</td>\n",
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              "      <td>140</td>\n",
              "      <td>241</td>\n",
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              "      <th>299</th>\n",
              "      <td>45</td>\n",
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              "      <td>0</td>\n",
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              "      <th>300</th>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>303 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     age  sex  cp  trestbps  chol  ...  exang  oldpeak  slope  ca  thal\n",
              "0     63    1   3       145   233  ...      0      2.3      0   0     1\n",
              "1     37    1   2       130   250  ...      0      3.5      0   0     2\n",
              "2     41    0   1       130   204  ...      0      1.4      2   0     2\n",
              "3     56    1   1       120   236  ...      0      0.8      2   0     2\n",
              "4     57    0   0       120   354  ...      1      0.6      2   0     2\n",
              "..   ...  ...  ..       ...   ...  ...    ...      ...    ...  ..   ...\n",
              "298   57    0   0       140   241  ...      1      0.2      1   0     3\n",
              "299   45    1   3       110   264  ...      0      1.2      1   0     3\n",
              "300   68    1   0       144   193  ...      0      3.4      1   2     3\n",
              "301   57    1   0       130   131  ...      1      1.2      1   1     3\n",
              "302   57    0   1       130   236  ...      0      0.0      1   1     2\n",
              "\n",
              "[303 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ysP6LX7rSMZZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "feature_train,feature_test,label_train,label_test=train_test_split(features,labels,test_size=0.2,random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0aM2FNbjSsEi",
        "colab_type": "text"
      },
      "source": [
        "Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yiu3oJUiSRcj",
        "colab_type": "code",
        "outputId": "a5ac03a4-cbc5-4b31-8786-0171f953740e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 154
        }
      },
      "source": [
        "model=LogisticRegression(max_iter=1000)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TzBOylHlSyAX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_pred=model.predict(feature_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yYJETPM9S8_A",
        "colab_type": "code",
        "outputId": "8dd87d42-2c32-4547-caab-b655bf825546",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m.accuracy_score(label_test,label_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8524590163934426"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OhwISYXdTAUU",
        "colab_type": "code",
        "outputId": "7fd49003-a3c4-4b58-898f-d9b60e03b79f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
              "       0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
              "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rlia6p8OTDp4",
        "colab_type": "code",
        "outputId": "3e82f4fb-d5cd-4085-ad08-3251ac6e4857",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "label_test"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>0</td>\n",
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              "      <th>111</th>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <th>249</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>1</td>\n",
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              "      <th>184</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>61 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     target\n",
              "179       0\n",
              "228       0\n",
              "111       1\n",
              "246       0\n",
              "60        1\n",
              "..      ...\n",
              "249       0\n",
              "104       1\n",
              "300       0\n",
              "193       0\n",
              "184       0\n",
              "\n",
              "[61 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8iqLlNr4TFCr",
        "colab_type": "code",
        "outputId": "a2276c0c-88e0-4302-80bd-7f0f819272d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.86      0.85        29\n",
            "           1       0.87      0.84      0.86        32\n",
            "\n",
            "    accuracy                           0.85        61\n",
            "   macro avg       0.85      0.85      0.85        61\n",
            "weighted avg       0.85      0.85      0.85        61\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8PUsos4NTJPt",
        "colab_type": "code",
        "outputId": "de7b4ae2-8a8b-4121-f8c1-8e159a058c22",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(m.confusion_matrix(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[25  4]\n",
            " [ 5 27]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HzLIXDdpTOKp",
        "colab_type": "text"
      },
      "source": [
        "##KNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iYZWpIwETLg2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "knn=KNeighborsClassifier()\n",
        "param={'n_neighbors':list(np.arange(1,20))}\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ajsxfcL9TStM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model=GridSearchCV(knn,param_grid=param)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P3KeyrXETU53",
        "colab_type": "code",
        "outputId": "8a9201f6-e6e1-4e34-8943-8ab2a6598da6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model.best_params_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'n_neighbors': 3}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wZiLXDuuTarr",
        "colab_type": "code",
        "outputId": "04134469-321a-4626-c3fb-6ec8f283e1f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "model=KNeighborsClassifier(n_neighbors=13)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=13, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hwesYadRTehl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_pred=model.predict(feature_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0SvbbFEMTg7P",
        "colab_type": "code",
        "outputId": "7dd4024b-9346-4283-a070-5230b1636d73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m.accuracy_score(label_test,label_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7377049180327869"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Ub_GXPNL6GU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_pred_train=model.predict(feature_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EwCV23H5MCwe",
        "colab_type": "code",
        "outputId": "80f7debe-4cf3-42f1-a7f0-0f7e836ca6e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m.accuracy_score(label_train,label_pred_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6900826446280992"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hxchrwwhTkF7",
        "colab_type": "code",
        "outputId": "223f9daf-65b4-4e5b-ed0b-10f0c2dd4f8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.74      0.69      0.71        29\n",
            "           1       0.74      0.78      0.76        32\n",
            "\n",
            "    accuracy                           0.74        61\n",
            "   macro avg       0.74      0.74      0.74        61\n",
            "weighted avg       0.74      0.74      0.74        61\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Us6ZOCblTmbv",
        "colab_type": "code",
        "outputId": "9bbb9725-cd02-465b-e245-fdbec4fdbc38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(m.confusion_matrix(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[20  9]\n",
            " [ 7 25]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FaLTzTEXTovN",
        "colab_type": "code",
        "outputId": "fc38f7c9-5fce-45aa-fdf3-1435576bef97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
              "       0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
              "       1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p3ctVz6FTsVQ",
        "colab_type": "code",
        "outputId": "86054d8d-ebc1-4c00-e579-86287e48b180",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "label_test"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>228</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>184</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>61 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     target\n",
              "179       0\n",
              "228       0\n",
              "111       1\n",
              "246       0\n",
              "60        1\n",
              "..      ...\n",
              "249       0\n",
              "104       1\n",
              "300       0\n",
              "193       0\n",
              "184       0\n",
              "\n",
              "[61 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L0lfcoIFTwYe",
        "colab_type": "text"
      },
      "source": [
        "##Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jDB1xLdsTuFz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "dt=DecisionTreeClassifier()\n",
        "param={'max_depth':list(np.arange(1,20))}\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VpV3L8uhT3GS",
        "colab_type": "code",
        "outputId": "52ad7a16-d38d-42c1-d840-1fef21fe67cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        }
      },
      "source": [
        "model=GridSearchCV(dt,param_grid=param)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=None, error_score=nan,\n",
              "             estimator=DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None,\n",
              "                                              criterion='gini', max_depth=None,\n",
              "                                              max_features=None,\n",
              "                                              max_leaf_nodes=None,\n",
              "                                              min_impurity_decrease=0.0,\n",
              "                                              min_impurity_split=None,\n",
              "                                              min_samples_leaf=1,\n",
              "                                              min_samples_split=2,\n",
              "                                              min_weight_fraction_leaf=0.0,\n",
              "                                              presort='deprecated',\n",
              "                                              random_state=None,\n",
              "                                              splitter='best'),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'max_depth': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
              "                                       13, 14, 15, 16, 17, 18, 19]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3WlsafWFT6RY",
        "colab_type": "code",
        "outputId": "b85ccf64-618a-4c9b-93d2-b96e5f729ead",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model.best_params_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': 8}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iZC7O9_cT9j8",
        "colab_type": "code",
        "outputId": "31660177-722b-4c96-aa56-d5c1af1499a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "model=DecisionTreeClassifier(max_depth=8)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=8, 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='deprecated',\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xXc_oxSHUCm4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_pred=model.predict(feature_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kC6WruF0UGK9",
        "colab_type": "code",
        "outputId": "dac02e51-ecb5-4214-87cd-2137f018237b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m.accuracy_score(label_test,label_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8524590163934426"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fW6ItJcTUIIo",
        "colab_type": "code",
        "outputId": "0c5ac65a-c2c1-4ae0-9add-4a3c4061feff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.79      0.93      0.86        29\n",
            "           1       0.93      0.78      0.85        32\n",
            "\n",
            "    accuracy                           0.85        61\n",
            "   macro avg       0.86      0.86      0.85        61\n",
            "weighted avg       0.86      0.85      0.85        61\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "21C1M4piUKmm",
        "colab_type": "code",
        "outputId": "9c9213fa-b56a-4dfa-b781-700b99857ec7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(m.confusion_matrix(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[27  2]\n",
            " [ 7 25]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4cPW1b4CUPba",
        "colab_type": "code",
        "outputId": "cea13bdc-7cde-4ed9-b3f9-4d3a01706b16",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0,\n",
              "       1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0,\n",
              "       1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f4In9hbOURm5",
        "colab_type": "code",
        "outputId": "a3604531-709f-46c8-edc7-a49e2e59f450",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "label_test"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>228</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>184</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>61 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     target\n",
              "179       0\n",
              "228       0\n",
              "111       1\n",
              "246       0\n",
              "60        1\n",
              "..      ...\n",
              "249       0\n",
              "104       1\n",
              "300       0\n",
              "193       0\n",
              "184       0\n",
              "\n",
              "[61 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gnLlk0mkUWAC",
        "colab_type": "text"
      },
      "source": [
        "##SVM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pcDhYG57US7I",
        "colab_type": "code",
        "outputId": "6044a7c3-5940-424d-8b33-68b643822bc7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        }
      },
      "source": [
        "model=SVC(kernel='linear')\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='linear',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VaOW78StUaRE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_pred=model.predict(feature_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gyfab6u_UcOE",
        "colab_type": "code",
        "outputId": "0e930f85-da58-4765-d944-31eeee08b974",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m.accuracy_score(label_test,label_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8688524590163934"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BZBqFQnrUeDK",
        "colab_type": "code",
        "outputId": "1c2d7d4e-ee3c-40b5-d795-da4cf384b08b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.86      0.86        29\n",
            "           1       0.88      0.88      0.88        32\n",
            "\n",
            "    accuracy                           0.87        61\n",
            "   macro avg       0.87      0.87      0.87        61\n",
            "weighted avg       0.87      0.87      0.87        61\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dFBxYSWPUgtq",
        "colab_type": "code",
        "outputId": "f51bd99e-4c3d-4ce2-8238-1f4e3194bf09",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(m.confusion_matrix(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[25  4]\n",
            " [ 4 28]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pjiOhBQ0UjRZ",
        "colab_type": "code",
        "outputId": "3b65c568-8e64-425a-bab9-c694c49db1d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
              "       0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
              "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fV--h4NJUmpp",
        "colab_type": "code",
        "outputId": "37176816-4804-46ae-e511-51b6a08e5f4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "label_test"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>228</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>184</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>61 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     target\n",
              "179       0\n",
              "228       0\n",
              "111       1\n",
              "246       0\n",
              "60        1\n",
              "..      ...\n",
              "249       0\n",
              "104       1\n",
              "300       0\n",
              "193       0\n",
              "184       0\n",
              "\n",
              "[61 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cr4k3XyfUqvR",
        "colab_type": "text"
      },
      "source": [
        "##Random Forest Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lDxkZ-gcUoGP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "rfc=RandomForestClassifier()\n",
        "param={'n_estimators':[10,20,30,40,50,60,70,80,90,100,110,120,130,140,150,160,170,180,190,200],'max_depth':list(np.arange(1,20))}\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BQA1Y6fGUxxm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model=GridSearchCV(rfc,param_grid=param)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i9j2n9zmUzxk",
        "colab_type": "code",
        "outputId": "0cb8b9c2-3645-4ba3-b74d-241f25b13c89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model.best_params_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': 5, 'n_estimators': 10}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mTrmxN8EWgjK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model=model.best_estimator_"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6GdfgCyxWjOI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_pred=model.predict(feature_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d1dV543zWlxi",
        "colab_type": "code",
        "outputId": "60b8ffb2-f0c8-4c5a-81a9-3c1002ae637d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m.accuracy_score(label_test,label_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8688524590163934"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4onnnj92WoEZ",
        "colab_type": "code",
        "outputId": "097bdedf-8642-4402-e4ed-d021267af6aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.90      0.87        29\n",
            "           1       0.90      0.84      0.87        32\n",
            "\n",
            "    accuracy                           0.87        61\n",
            "   macro avg       0.87      0.87      0.87        61\n",
            "weighted avg       0.87      0.87      0.87        61\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RWzNfpfKWrYr",
        "colab_type": "code",
        "outputId": "b820656f-1b29-4bde-d7b3-b0371ea1ec72",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(m.confusion_matrix(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[26  3]\n",
            " [ 5 27]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Kfnuvm0WuJf",
        "colab_type": "code",
        "outputId": "8e0c6874-1a80-48f8-cc66-df8af322e63e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0,\n",
              "       1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,\n",
              "       1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xjwj7QO5WwO5",
        "colab_type": "code",
        "outputId": "41255a4a-ea65-4021-e860-c1a3760b1f91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "label_test"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>228</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>184</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>61 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     target\n",
              "179       0\n",
              "228       0\n",
              "111       1\n",
              "246       0\n",
              "60        1\n",
              "..      ...\n",
              "249       0\n",
              "104       1\n",
              "300       0\n",
              "193       0\n",
              "184       0\n",
              "\n",
              "[61 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z0k80_-PWxjd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}