PCOS_Prediction.ipynb

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      "name": "PCOS Prediction.ipynb",
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        "id": "OO-M1ZDopXU3"
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      "source": [
        "import pandas as pd\n",
        "import sklearn\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "VBjcXns1qL82"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "djixD6XhrMmu"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_excel('/content/PCOS_data_without_infertility.xlsx',sheet_name = 'Full_new')"
      ],
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        "id": "SixzSyGRtvPm"
      },
      "execution_count": 4,
      "outputs": []
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              "   Sl. No  Patient File No.  PCOS (Y/N)   Age (yrs)  Weight (Kg)  Height(Cm)   \\\n",
              "0     1.0               1.0         0.0        28.0         44.6        152.0   \n",
              "1     2.0               2.0         0.0        36.0         65.0        161.5   \n",
              "2     3.0               3.0         1.0        33.0         68.8        165.0   \n",
              "3     4.0               4.0         0.0        37.0         65.0        148.0   \n",
              "4     5.0               5.0         0.0        25.0         52.0        161.0   \n",
              "\n",
              "         BMI  Blood Group  Pulse rate(bpm)   RR (breaths/min)  ...  \\\n",
              "0  19.300000         15.0              78.0              22.0  ...   \n",
              "1  24.921163         15.0              74.0              20.0  ...   \n",
              "2  25.270891         11.0              72.0              18.0  ...   \n",
              "3  29.674945         13.0              72.0              20.0  ...   \n",
              "4  20.060954         11.0              72.0              18.0  ...   \n",
              "\n",
              "   Fast food (Y/N)  Reg.Exercise(Y/N)  BP _Systolic (mmHg)  \\\n",
              "0              1.0                0.0                110.0   \n",
              "1              0.0                0.0                120.0   \n",
              "2              1.0                0.0                120.0   \n",
              "3              0.0                0.0                120.0   \n",
              "4              0.0                0.0                120.0   \n",
              "\n",
              "   BP _Diastolic (mmHg)  Follicle No. (L)  Follicle No. (R)  \\\n",
              "0                  80.0               3.0               3.0   \n",
              "1                  70.0               3.0               5.0   \n",
              "2                  80.0              13.0              15.0   \n",
              "3                  70.0               2.0               2.0   \n",
              "4                  80.0               3.0               4.0   \n",
              "\n",
              "   Avg. F size (L) (mm) Avg. F size (R) (mm)  Endometrium (mm)  Unnamed: 44  \n",
              "0                  18.0                 18.0               8.5          NaN  \n",
              "1                  15.0                 14.0               3.7          NaN  \n",
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              "Index(['Sl. No', 'Patient File No.', 'PCOS (Y/N)', ' Age (yrs)', 'Weight (Kg)',\n",
              "       'Height(Cm) ', 'BMI', 'Blood Group', 'Pulse rate(bpm) ',\n",
              "       'RR (breaths/min)', 'Hb(g/dl)', 'Cycle(R/I)', 'Cycle length(days)',\n",
              "       'Marraige Status (Yrs)', 'Pregnant(Y/N)', 'No. of aborptions',\n",
              "       '  I   beta-HCG(mIU/mL)', 'II    beta-HCG(mIU/mL)', 'FSH(mIU/mL)',\n",
              "       'LH(mIU/mL)', 'FSH/LH', 'Hip(inch)', 'Waist(inch)', 'Waist:Hip Ratio',\n",
              "       'TSH (mIU/L)', 'AMH(ng/mL)', 'PRL(ng/mL)', 'Vit D3 (ng/mL)',\n",
              "       'PRG(ng/mL)', 'RBS(mg/dl)', 'Weight gain(Y/N)', 'hair growth(Y/N)',\n",
              "       'Skin darkening (Y/N)', 'Hair loss(Y/N)', 'Pimples(Y/N)',\n",
              "       'Fast food (Y/N)', 'Reg.Exercise(Y/N)', 'BP _Systolic (mmHg)',\n",
              "       'BP _Diastolic (mmHg)', 'Follicle No. (L)', 'Follicle No. (R)',\n",
              "       'Avg. F size (L) (mm)', 'Avg. F size (R) (mm)', 'Endometrium (mm)',\n",
              "       'Unnamed: 44'],\n",
              "      dtype='object')"
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          "execution_count": 6
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        "data = data.fillna(0)"
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      "execution_count": 7,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "y = data['PCOS (Y/N)']\n",
        "X = data[[' Age (yrs)', 'Weight (Kg)','Height(Cm) ', 'BMI','Pulse rate(bpm) ', 'RR (breaths/min)','Cycle length(days)',\n",
        "          'Pregnant(Y/N)','Weight gain(Y/N)', 'hair growth(Y/N)',\n",
        "       'Skin darkening (Y/N)', 'Hair loss(Y/N)', 'Pimples(Y/N)',\n",
        "       'Fast food (Y/N)', 'Reg.Exercise(Y/N)']]"
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      "execution_count": 9,
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              "    Age (yrs)  Weight (Kg)  Height(Cm)         BMI  Pulse rate(bpm)   \\\n",
              "0        28.0         44.6        152.0  19.300000              78.0   \n",
              "1        36.0         65.0        161.5  24.921163              74.0   \n",
              "2        33.0         68.8        165.0  25.270891              72.0   \n",
              "3        37.0         65.0        148.0  29.674945              72.0   \n",
              "4        25.0         52.0        161.0  20.060954              72.0   \n",
              "\n",
              "   RR (breaths/min)  Cycle length(days)  Pregnant(Y/N)  Weight gain(Y/N)  \\\n",
              "0              22.0                 5.0            0.0               0.0   \n",
              "1              20.0                 5.0            1.0               0.0   \n",
              "2              18.0                 5.0            1.0               0.0   \n",
              "3              20.0                 5.0            0.0               0.0   \n",
              "4              18.0                 5.0            1.0               0.0   \n",
              "\n",
              "   hair growth(Y/N)  Skin darkening (Y/N)  Hair loss(Y/N)  Pimples(Y/N)  \\\n",
              "0               0.0                   0.0             0.0           0.0   \n",
              "1               0.0                   0.0             0.0           0.0   \n",
              "2               0.0                   0.0             1.0           1.0   \n",
              "3               0.0                   0.0             0.0           0.0   \n",
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              "      <th>Skin darkening (Y/N)</th>\n",
              "      <th>Hair loss(Y/N)</th>\n",
              "      <th>Pimples(Y/N)</th>\n",
              "      <th>Fast food (Y/N)</th>\n",
              "      <th>Reg.Exercise(Y/N)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>28.0</td>\n",
              "      <td>44.6</td>\n",
              "      <td>152.0</td>\n",
              "      <td>19.300000</td>\n",
              "      <td>78.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>36.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>161.5</td>\n",
              "      <td>24.921163</td>\n",
              "      <td>74.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33.0</td>\n",
              "      <td>68.8</td>\n",
              "      <td>165.0</td>\n",
              "      <td>25.270891</td>\n",
              "      <td>72.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>37.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>148.0</td>\n",
              "      <td>29.674945</td>\n",
              "      <td>72.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>25.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>161.0</td>\n",
              "      <td>20.060954</td>\n",
              "      <td>72.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-820c03c5-07b8-4dd4-8363-6e505a7f0d4f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-820c03c5-07b8-4dd4-8363-6e505a7f0d4f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-820c03c5-07b8-4dd4-8363-6e505a7f0d4f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q3la5qYF1Emz",
        "outputId": "16fe49d9-688c-4661-b473-2c11092023be"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 541 entries, 0 to 540\n",
            "Data columns (total 15 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0    Age (yrs)            541 non-null    float64\n",
            " 1   Weight (Kg)           541 non-null    float64\n",
            " 2   Height(Cm)            541 non-null    float64\n",
            " 3   BMI                   541 non-null    float64\n",
            " 4   Pulse rate(bpm)       541 non-null    float64\n",
            " 5   RR (breaths/min)      541 non-null    float64\n",
            " 6   Cycle length(days)    541 non-null    float64\n",
            " 7   Pregnant(Y/N)         541 non-null    float64\n",
            " 8   Weight gain(Y/N)      541 non-null    float64\n",
            " 9   hair growth(Y/N)      541 non-null    float64\n",
            " 10  Skin darkening (Y/N)  541 non-null    float64\n",
            " 11  Hair loss(Y/N)        541 non-null    float64\n",
            " 12  Pimples(Y/N)          541 non-null    float64\n",
            " 13  Fast food (Y/N)       541 non-null    float64\n",
            " 14  Reg.Exercise(Y/N)     541 non-null    float64\n",
            "dtypes: float64(15)\n",
            "memory usage: 63.5 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test = train_test_split(X,y)"
      ],
      "metadata": {
        "id": "DviikRTlxSuk"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model1 = DecisionTreeClassifier()\n",
        "model1.fit(x_train,y_train)\n",
        "y_pred = model1.predict(x_test)\n",
        "print(\"Accuracy = \",accuracy_score(y_test,y_pred))\n",
        "print(\"Confusion matrix:\\n\",confusion_matrix(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sgIe1TO1y8gI",
        "outputId": "37708b09-0d35-497f-d0ee-e7b77bab85eb"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy =  0.7352941176470589\n",
            "Confusion matrix:\n",
            " [[77 17]\n",
            " [19 23]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model2 = RandomForestClassifier()\n",
        "model2.fit(x_train,y_train)\n",
        "y_pred = model2.predict(x_test)\n",
        "print(\"Accuracy = \",accuracy_score(y_test,y_pred))\n",
        "print(\"Confusion matrix:\\n\",confusion_matrix(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sj_Dke9QzCvT",
        "outputId": "02ab00c7-4372-40d5-f11a-030b4bc534a1"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy =  0.8455882352941176\n",
            "Confusion matrix:\n",
            " [[89  5]\n",
            " [16 26]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model3 = KNeighborsClassifier()\n",
        "model3.fit(x_train,y_train)\n",
        "y_pred = model3.predict(x_test)\n",
        "print(\"Accuracy = \",accuracy_score(y_test,y_pred))\n",
        "print(\"Confusion matrix:\\n\",confusion_matrix(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_SpCNw6PzD_C",
        "outputId": "df1ec049-8e56-4f26-c9d7-46b411f77ce1"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy =  0.6764705882352942\n",
            "Confusion matrix:\n",
            " [[79 15]\n",
            " [29 13]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestClassifier()\n",
        "model.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vU3xsYa_3iLn",
        "outputId": "751f51e2-5f38-4048-86eb-332dffddb546"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from joblib import dump, load\n",
        "dump(model, 'RandomForestModel.joblib')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zVqDFsTJ3gc_",
        "outputId": "6691eb55-1c06-4f22-bcaa-f3752f92ff7d"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['RandomForestModel.joblib']"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = data['PCOS (Y/N)']\n",
        "X = data[[' Age (yrs)', 'Weight (Kg)',\n",
        "       'Height(Cm) ', 'BMI', 'Pulse rate(bpm) ',\n",
        "       'RR (breaths/min)', 'Hb(g/dl)', 'Cycle length(days)', 'Pregnant(Y/N)',\n",
        "       '  I   beta-HCG(mIU/mL)','FSH(mIU/mL)',\n",
        "       'LH(mIU/mL)', 'FSH/LH', 'Hip(inch)', 'Waist(inch)', 'Waist:Hip Ratio',\n",
        "       'TSH (mIU/L)', 'PRL(ng/mL)', 'Vit D3 (ng/mL)',\n",
        "       'PRG(ng/mL)', 'RBS(mg/dl)', 'Weight gain(Y/N)', 'hair growth(Y/N)',\n",
        "       'Skin darkening (Y/N)', 'Hair loss(Y/N)', 'Pimples(Y/N)',\n",
        "       'Fast food (Y/N)', 'Reg.Exercise(Y/N)', 'BP _Systolic (mmHg)',\n",
        "       'BP _Diastolic (mmHg)', 'Follicle No. (L)', 'Follicle No. (R)',\n",
        "       'Avg. F size (L) (mm)', 'Avg. F size (R) (mm)', 'Endometrium (mm)']]"
      ],
      "metadata": {
        "id": "TGYcNV0unDyN"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "07gE5YIBoIPI",
        "outputId": "5823205b-2751-4c05-a669-ba4497ce19dd"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 541 entries, 0 to 540\n",
            "Data columns (total 35 columns):\n",
            " #   Column                  Non-Null Count  Dtype  \n",
            "---  ------                  --------------  -----  \n",
            " 0    Age (yrs)              541 non-null    float64\n",
            " 1   Weight (Kg)             541 non-null    float64\n",
            " 2   Height(Cm)              541 non-null    float64\n",
            " 3   BMI                     541 non-null    float64\n",
            " 4   Pulse rate(bpm)         541 non-null    float64\n",
            " 5   RR (breaths/min)        541 non-null    float64\n",
            " 6   Hb(g/dl)                541 non-null    float64\n",
            " 7   Cycle length(days)      541 non-null    float64\n",
            " 8   Pregnant(Y/N)           541 non-null    float64\n",
            " 9     I   beta-HCG(mIU/mL)  541 non-null    float64\n",
            " 10  FSH(mIU/mL)             541 non-null    float64\n",
            " 11  LH(mIU/mL)              541 non-null    float64\n",
            " 12  FSH/LH                  541 non-null    float64\n",
            " 13  Hip(inch)               541 non-null    float64\n",
            " 14  Waist(inch)             541 non-null    float64\n",
            " 15  Waist:Hip Ratio         541 non-null    float64\n",
            " 16  TSH (mIU/L)             541 non-null    float64\n",
            " 17  PRL(ng/mL)              541 non-null    float64\n",
            " 18  Vit D3 (ng/mL)          541 non-null    float64\n",
            " 19  PRG(ng/mL)              541 non-null    float64\n",
            " 20  RBS(mg/dl)              541 non-null    float64\n",
            " 21  Weight gain(Y/N)        541 non-null    float64\n",
            " 22  hair growth(Y/N)        541 non-null    float64\n",
            " 23  Skin darkening (Y/N)    541 non-null    float64\n",
            " 24  Hair loss(Y/N)          541 non-null    float64\n",
            " 25  Pimples(Y/N)            541 non-null    float64\n",
            " 26  Fast food (Y/N)         541 non-null    float64\n",
            " 27  Reg.Exercise(Y/N)       541 non-null    float64\n",
            " 28  BP _Systolic (mmHg)     541 non-null    float64\n",
            " 29  BP _Diastolic (mmHg)    541 non-null    float64\n",
            " 30  Follicle No. (L)        541 non-null    float64\n",
            " 31  Follicle No. (R)        541 non-null    float64\n",
            " 32  Avg. F size (L) (mm)    541 non-null    float64\n",
            " 33  Avg. F size (R) (mm)    541 non-null    float64\n",
            " 34  Endometrium (mm)        541 non-null    float64\n",
            "dtypes: float64(35)\n",
            "memory usage: 148.1 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test = train_test_split(X,y)"
      ],
      "metadata": {
        "id": "_TiTYX1cnDyO"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model1 = DecisionTreeClassifier()\n",
        "model1.fit(x_train,y_train)\n",
        "y_pred = model1.predict(x_test)\n",
        "print(\"Accuracy = \",accuracy_score(y_test,y_pred))\n",
        "print(\"Confusion matrix:\\n\",confusion_matrix(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "45631870-dd5f-4b10-dc44-4988375e5abd",
        "id": "tSGmyzPOb5iX"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy =  0.7647058823529411\n",
            "Confusion matrix:\n",
            " [[74 17]\n",
            " [15 30]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model2 = RandomForestClassifier()\n",
        "model2.fit(x_train,y_train)\n",
        "y_pred = model2.predict(x_test)\n",
        "print(\"Accuracy = \",accuracy_score(y_test,y_pred))\n",
        "print(\"Confusion matrix:\\n\",confusion_matrix(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "56a106ce-7ed1-4ed1-ddad-7d6643ea51fb",
        "id": "mAZynkmEb5iY"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy =  0.9191176470588235\n",
            "Confusion matrix:\n",
            " [[89  2]\n",
            " [ 9 36]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model3 = KNeighborsClassifier()\n",
        "model3.fit(x_train,y_train)\n",
        "y_pred = model3.predict(x_test)\n",
        "print(\"Accuracy = \",accuracy_score(y_test,y_pred))\n",
        "print(\"Confusion matrix:\\n\",confusion_matrix(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1258d027-a6c2-4813-a1de-94232b8f4d57",
        "id": "dpTRe9SRb5iZ"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy =  0.6764705882352942\n",
            "Confusion matrix:\n",
            " [[80 11]\n",
            " [33 12]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model1 = RandomForestClassifier()\n",
        "model1.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35b0131f-d552-42b7-8c93-b5b00db4c5a5",
        "id": "aXUxYUaQb5ia"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from joblib import dump, load\n",
        "dump(model1, 'RandomForestModel1.joblib')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c034015-7ad7-4f98-fbfa-2310fd5b95ee",
        "id": "1R17R2X0b5ib"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['RandomForestModel1.joblib']"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    }
  ]
}