Naive_Bayes_Model1_Gaussian.ipynb

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      "cell_type": "code",
      "execution_count": null,
      "id": "realistic-substitute",
      "metadata": {
        "id": "realistic-substitute"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import imblearn\n",
        "from collections import Counter\n",
        "data = pd.read_csv('new_dataset_with_label.csv',index_col=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "identified-buffalo",
      "metadata": {
        "id": "identified-buffalo",
        "outputId": "61afc37e-3824-440b-c8d2-ff48bbf6ede8"
      },
      "outputs": [
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              "                   total_time  compiled  compiled successfully  ran_tests  \\\n",
              "Student                                                                     \n",
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              "A2016\\student1066   2784368.0     338.0                  223.0      449.0   \n",
              "...                       ...       ...                    ...        ...   \n",
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              "B2016\\student9949   7787496.0    1961.0                 1199.0      588.0   \n",
              "B2016\\student9972  12352640.0    5669.0                 3568.0      973.0   \n",
              "B2016\\student9979      8496.0       NaN                    NaN        NaN   \n",
              "B2016\\student9998   6599896.0    1659.0                  993.0      323.0   \n",
              "\n",
              "                   test_results  modified  add_lines   change  remove_lines  \\\n",
              "Student                                                                       \n",
              "A2016\\student1013        1349.0   38441.0    12365.0  32597.0        2023.0   \n",
              "A2016\\student1016           NaN       1.0        NaN      NaN           8.0   \n",
              "A2016\\student1021        1605.0   58550.0    19160.0  49003.0        6105.0   \n",
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              "A2016\\student1066         810.0   13924.0     6140.0  12169.0         369.0   \n",
              "...                         ...       ...        ...      ...           ...   \n",
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              "B2016\\student9949         468.0   39601.0     9607.0  30431.0        3452.0   \n",
              "B2016\\student9972         612.0   55655.0    20719.0  47870.0        3707.0   \n",
              "B2016\\student9979           NaN      69.0       38.0     52.0          19.0   \n",
              "B2016\\student9998         331.0   33109.0    58287.0  28059.0        1000.0   \n",
              "\n",
              "                        cps  paste_lines  max_pastes  average_test_results  \\\n",
              "Student                                                                      \n",
              "A2016\\student1013  0.003436       5202.0       120.0              1.957910   \n",
              "A2016\\student1016  0.000000          NaN         NaN                   NaN   \n",
              "A2016\\student1021  0.003997       7270.0       162.0              2.039390   \n",
              "A2016\\student1029  0.005040       7235.0       165.0              0.884977   \n",
              "A2016\\student1066  0.004970       3988.0       139.0              1.804009   \n",
              "...                     ...          ...         ...                   ...   \n",
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              "\n",
              "                   avg_paste  \n",
              "Student                       \n",
              "A2016\\student1013   4.553441  \n",
              "A2016\\student1016        NaN  \n",
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              "A2016\\student1029   4.840774  \n",
              "A2016\\student1066   3.470160  \n",
              "...                      ...  \n",
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              "\n",
              "[2339 rows x 14 columns]"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.iloc[:2339,:-1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "genuine-jenny",
      "metadata": {
        "id": "genuine-jenny"
      },
      "outputs": [],
      "source": [
        "data.fillna(0, inplace=True)\n",
        "data[\"Label\"] =data[\"Label\"].astype(\"int64\")\n",
        "x = data.iloc[:2339,:-1]\n",
        "y = data.iloc[:2339,-1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "accepted-measurement",
      "metadata": {
        "id": "accepted-measurement",
        "outputId": "f0cc3381-4adc-403e-c2d1-60813c3352b5"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\Keerthana\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[Text(0, 0, 'Not plagiarised'), Text(1, 0, 'plagiarised')]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
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          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "g = sns.countplot(data['Label'])\n",
        "g.set_xticklabels(['Not plagiarised','plagiarised'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "secure-leave",
      "metadata": {
        "id": "secure-leave",
        "outputId": "2b173331-ef3a-464f-ce23-1aef103aaf1f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2339"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "polished-lexington",
      "metadata": {
        "id": "polished-lexington"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(x,y, test_size=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "respiratory-imagination",
      "metadata": {
        "id": "respiratory-imagination"
      },
      "outputs": [],
      "source": [
        "from imblearn.over_sampling import RandomOverSampler\n",
        "oversample = RandomOverSampler(sampling_strategy='minority')\n",
        "# fit and apply the transform\n",
        "X_over, y_over = oversample.fit_resample(X_train,Y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "innovative-compilation",
      "metadata": {
        "id": "innovative-compilation",
        "outputId": "c5e8da7b-7b40-4e64-e4d4-b0f00f9d9dab"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[(0, 609), (1, 614)]\n"
          ]
        }
      ],
      "source": [
        "smote_enn = imblearn.combine.SMOTEENN(random_state=0,sampling_strategy='minority')\n",
        "x_train_resampled, y_train_resampled = smote_enn.fit_resample(X_train,Y_train)\n",
        "print(sorted(Counter(y_train_resampled).items()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "specific-mediterranean",
      "metadata": {
        "id": "specific-mediterranean",
        "outputId": "89829a21-e688-4ef8-f0b6-40348fbcb605"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<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>total_time</th>\n",
              "      <th>compiled</th>\n",
              "      <th>compiled successfully</th>\n",
              "      <th>ran_tests</th>\n",
              "      <th>test_results</th>\n",
              "      <th>modified</th>\n",
              "      <th>add_lines</th>\n",
              "      <th>change</th>\n",
              "      <th>remove_lines</th>\n",
              "      <th>cps</th>\n",
              "      <th>paste_lines</th>\n",
              "      <th>max_pastes</th>\n",
              "      <th>average_test_results</th>\n",
              "      <th>avg_paste</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.120000e+02</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.099920e+05</td>\n",
              "      <td>112.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3180.000000</td>\n",
              "      <td>851.000000</td>\n",
              "      <td>2843.000000</td>\n",
              "      <td>311.000000</td>\n",
              "      <td>0.001994</td>\n",
              "      <td>396.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.568182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.400000e+03</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.001200e+05</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>131.000000</td>\n",
              "      <td>394.000000</td>\n",
              "      <td>370.000000</td>\n",
              "      <td>263.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.004182</td>\n",
              "      <td>308.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>6.238095</td>\n",
              "      <td>2.717532</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>1218</th>\n",
              "      <td>5.673361e+06</td>\n",
              "      <td>1205.823904</td>\n",
              "      <td>916.123945</td>\n",
              "      <td>678.416716</td>\n",
              "      <td>1144.193480</td>\n",
              "      <td>29759.603600</td>\n",
              "      <td>10571.835937</td>\n",
              "      <td>25063.768498</td>\n",
              "      <td>1310.167155</td>\n",
              "      <td>0.002276</td>\n",
              "      <td>4776.599833</td>\n",
              "      <td>91.598997</td>\n",
              "      <td>1.697133</td>\n",
              "      <td>2.702448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1219</th>\n",
              "      <td>3.163886e+06</td>\n",
              "      <td>775.920040</td>\n",
              "      <td>498.800044</td>\n",
              "      <td>365.938981</td>\n",
              "      <td>433.705340</td>\n",
              "      <td>16008.265061</td>\n",
              "      <td>46017.233112</td>\n",
              "      <td>13665.054448</td>\n",
              "      <td>509.237837</td>\n",
              "      <td>0.065563</td>\n",
              "      <td>42451.085450</td>\n",
              "      <td>144.029488</td>\n",
              "      <td>1.139961</td>\n",
              "      <td>3.328610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1220</th>\n",
              "      <td>3.061077e+06</td>\n",
              "      <td>570.313622</td>\n",
              "      <td>357.508838</td>\n",
              "      <td>355.895116</td>\n",
              "      <td>433.644850</td>\n",
              "      <td>19949.235536</td>\n",
              "      <td>7896.165117</td>\n",
              "      <td>16807.783843</td>\n",
              "      <td>1124.826613</td>\n",
              "      <td>0.007200</td>\n",
              "      <td>3320.173493</td>\n",
              "      <td>6352.771208</td>\n",
              "      <td>1.190634</td>\n",
              "      <td>7.622618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1221</th>\n",
              "      <td>6.889983e+06</td>\n",
              "      <td>1834.037516</td>\n",
              "      <td>1255.338233</td>\n",
              "      <td>535.940257</td>\n",
              "      <td>954.296484</td>\n",
              "      <td>36026.601318</td>\n",
              "      <td>25411.759865</td>\n",
              "      <td>30021.507586</td>\n",
              "      <td>5270.381516</td>\n",
              "      <td>0.034037</td>\n",
              "      <td>18993.743282</td>\n",
              "      <td>438.669411</td>\n",
              "      <td>1.761379</td>\n",
              "      <td>9.675806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1222</th>\n",
              "      <td>5.210709e+06</td>\n",
              "      <td>1271.123579</td>\n",
              "      <td>931.643906</td>\n",
              "      <td>371.917306</td>\n",
              "      <td>816.767023</td>\n",
              "      <td>29509.963820</td>\n",
              "      <td>7826.692922</td>\n",
              "      <td>26031.269974</td>\n",
              "      <td>762.305923</td>\n",
              "      <td>0.002220</td>\n",
              "      <td>3513.940773</td>\n",
              "      <td>159.173519</td>\n",
              "      <td>2.176069</td>\n",
              "      <td>3.297237</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1223 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        total_time     compiled  compiled successfully   ran_tests  \\\n",
              "0     1.120000e+02     0.000000               0.000000    0.000000   \n",
              "1     5.099920e+05   112.000000              48.000000    5.000000   \n",
              "2     0.000000e+00     0.000000               0.000000    0.000000   \n",
              "3     2.400000e+03     0.000000               0.000000    0.000000   \n",
              "4     2.001200e+05    46.000000              43.000000   21.000000   \n",
              "...            ...          ...                    ...         ...   \n",
              "1218  5.673361e+06  1205.823904             916.123945  678.416716   \n",
              "1219  3.163886e+06   775.920040             498.800044  365.938981   \n",
              "1220  3.061077e+06   570.313622             357.508838  355.895116   \n",
              "1221  6.889983e+06  1834.037516            1255.338233  535.940257   \n",
              "1222  5.210709e+06  1271.123579             931.643906  371.917306   \n",
              "\n",
              "      test_results      modified     add_lines        change  remove_lines  \\\n",
              "0         0.000000     93.000000      0.000000      0.000000      0.000000   \n",
              "1         0.000000   3180.000000    851.000000   2843.000000    311.000000   \n",
              "2         0.000000     16.000000      0.000000      0.000000      0.000000   \n",
              "3         0.000000      1.000000      0.000000      0.000000     22.000000   \n",
              "4       131.000000    394.000000    370.000000    263.000000     16.000000   \n",
              "...            ...           ...           ...           ...           ...   \n",
              "1218   1144.193480  29759.603600  10571.835937  25063.768498   1310.167155   \n",
              "1219    433.705340  16008.265061  46017.233112  13665.054448    509.237837   \n",
              "1220    433.644850  19949.235536   7896.165117  16807.783843   1124.826613   \n",
              "1221    954.296484  36026.601318  25411.759865  30021.507586   5270.381516   \n",
              "1222    816.767023  29509.963820   7826.692922  26031.269974    762.305923   \n",
              "\n",
              "           cps   paste_lines   max_pastes  average_test_results  avg_paste  \n",
              "0     0.000000      0.000000     0.000000              0.000000   0.000000  \n",
              "1     0.001994    396.000000     3.000000              0.000000   2.568182  \n",
              "2     0.000000      0.000000     0.000000              0.000000   0.000000  \n",
              "3     0.000000      0.000000     0.000000              0.000000   0.000000  \n",
              "4     0.004182    308.000000    59.000000              6.238095   2.717532  \n",
              "...        ...           ...          ...                   ...        ...  \n",
              "1218  0.002276   4776.599833    91.598997              1.697133   2.702448  \n",
              "1219  0.065563  42451.085450   144.029488              1.139961   3.328610  \n",
              "1220  0.007200   3320.173493  6352.771208              1.190634   7.622618  \n",
              "1221  0.034037  18993.743282   438.669411              1.761379   9.675806  \n",
              "1222  0.002220   3513.940773   159.173519              2.176069   3.297237  \n",
              "\n",
              "[1223 rows x 14 columns]"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_train_resampled"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "grave-maker",
      "metadata": {
        "id": "grave-maker",
        "outputId": "8a9d2aea-a98d-44a4-f097-fcd1cde3f16f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0       0\n",
              "1       0\n",
              "2       0\n",
              "3       0\n",
              "4       0\n",
              "       ..\n",
              "1218    1\n",
              "1219    1\n",
              "1220    1\n",
              "1221    1\n",
              "1222    1\n",
              "Name: Label, Length: 1223, dtype: int64"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_train_resampled\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ready-daisy",
      "metadata": {
        "id": "ready-daisy",
        "outputId": "28313b77-83c1-4aea-8b0e-14457e469326"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.6474358974358975\n"
          ]
        }
      ],
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "\n",
        "gnb = GaussianNB(var_smoothing=1e-06)\n",
        "gnb.fit(x_train_resampled,y_train_resampled)\n",
        "print(gnb.score(X_test, Y_test))\n",
        "y_pred = gnb.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "tight-times",
      "metadata": {
        "id": "tight-times",
        "outputId": "503f7796-ba30-4fb6-df7c-467ed614dad3"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\n",
        "conf_matrix = confusion_matrix(y_true=Y_test, y_pred=gnb.predict(X_test))\n",
        "#\n",
        "# Print the confusion matrix using Matplotlib\n",
        "#\n",
        "fig, ax = plt.subplots(figsize=(5, 5))\n",
        "ax.matshow(conf_matrix, cmap=plt.cm.Oranges, alpha=0.3)\n",
        "for i in range(conf_matrix.shape[0]):\n",
        "    for j in range(conf_matrix.shape[1]):\n",
        "        ax.text(x=j, y=i,s=conf_matrix[i, j], va='center', ha='center', size='xx-large')\n",
        " \n",
        "plt.xlabel('Predictions', fontsize=18)\n",
        "plt.ylabel('Actuals', fontsize=18)\n",
        "plt.title('Confusion Matrix', fontsize=18)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "taken-dinner",
      "metadata": {
        "id": "taken-dinner",
        "outputId": "c0d4cdb0-8d64-4fde-ef3c-052ee6f8bf26"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Precision: 0.425\n"
          ]
        }
      ],
      "source": [
        "print('Precision: %.3f' % precision_score(Y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "happy-killer",
      "metadata": {
        "scrolled": true,
        "id": "happy-killer",
        "outputId": "a9898774-3f35-458e-d41b-581607549b95"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Recall: 0.624\n"
          ]
        }
      ],
      "source": [
        "print('Recall: %.3f' % recall_score(Y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "rapid-stewart",
      "metadata": {
        "id": "rapid-stewart",
        "outputId": "1eeed95b-2d7e-4081-d15a-10e4e152fd2a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.632\n"
          ]
        }
      ],
      "source": [
        "print('Accuracy: %.3f' % accuracy_score(Y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "documented-handbook",
      "metadata": {
        "id": "documented-handbook",
        "outputId": "590d7b34-5d24-4415-b2e3-3f7a161bd67c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "F1 Score: 0.506\n"
          ]
        }
      ],
      "source": [
        "print('F1 Score: %.3f' % f1_score(Y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "understanding-folder",
      "metadata": {
        "id": "understanding-folder",
        "outputId": "a815fa2b-4fe5-4dd9-d1d0-e32cc2b89bd0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.6474358974358975\n"
          ]
        }
      ],
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "\n",
        "gnb1 =GaussianNB(var_smoothing=1e-06)\n",
        "gnb1.fit(x_train_resampled, y_train_resampled)\n",
        "print(gnb1.score(X_test, Y_test))\n",
        "y_pred = gnb1.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "narrative-warrant",
      "metadata": {
        "id": "narrative-warrant",
        "outputId": "993096c0-499a-47b5-c5f8-4a0e1539c3a4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\n",
        "conf_matrix = confusion_matrix(y_true=Y_test, y_pred=gnb1.predict(X_test))\n",
        "#\n",
        "# Print the confusion matrix using Matplotlib\n",
        "#\n",
        "fig, ax = plt.subplots(figsize=(5, 5))\n",
        "ax.matshow(conf_matrix, cmap=plt.cm.Oranges, alpha=0.3)\n",
        "for i in range(conf_matrix.shape[0]):\n",
        "    for j in range(conf_matrix.shape[1]):\n",
        "        ax.text(x=j, y=i,s=conf_matrix[i, j], va='center', ha='center', size='xx-large')\n",
        " \n",
        "plt.xlabel('Predictions', fontsize=18)\n",
        "plt.ylabel('Actuals', fontsize=18)\n",
        "plt.title('Confusion Matrix', fontsize=18)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "soviet-customs",
      "metadata": {
        "id": "soviet-customs",
        "outputId": "bcaaca0c-d3e9-47cf-8080-7283811db165"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Precision: 0.435\n"
          ]
        }
      ],
      "source": [
        "print('Precision: %.3f' % precision_score(Y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "representative-hardware",
      "metadata": {
        "id": "representative-hardware",
        "outputId": "4e58c23b-e162-4b8a-b65d-d108cf3e80a3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Recall: 0.567\n"
          ]
        }
      ],
      "source": [
        "print('Recall: %.3f' % recall_score(Y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "consolidated-bathroom",
      "metadata": {
        "id": "consolidated-bathroom",
        "outputId": "95eb3833-2833-4638-a59b-90f0dff63256"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.647\n"
          ]
        }
      ],
      "source": [
        "print('Accuracy: %.3f' % accuracy_score(Y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "lesser-repository",
      "metadata": {
        "id": "lesser-repository",
        "outputId": "048858e6-907d-45eb-a932-d5c9e252de86"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Pipeline(steps=[('pca', PCA()), ('estimator', GaussianNB(var_smoothing=1e-06))])\n",
            "best score:\n",
            "0.7204687677779043\n",
            "{'estimator__var_smoothing': 1e-06}\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.decomposition import PCA\n",
        "pipe = Pipeline(steps=[\n",
        "                    ('pca', PCA()),\n",
        "                    ('estimator', GaussianNB()),\n",
        "                    ])\n",
        "    \n",
        "parameters = {'estimator__var_smoothing': [1e-11, 1e-10, 1e-9,1e-8,1e-7,1e-6,1e-5,1e-4]}\n",
        "Bayes = GridSearchCV(pipe, parameters, scoring='accuracy', cv=10).fit(X_train, Y_train)\n",
        "print(Bayes.best_estimator_)\n",
        "print('best score:')\n",
        "print(Bayes.best_score_)\n",
        "predictions = Bayes.best_estimator_.predict(X_test)\n",
        "print(Bayes.best_params_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "divided-honduras",
      "metadata": {
        "id": "divided-honduras"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "southern-chess",
      "metadata": {
        "id": "southern-chess"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.1"
    },
    "colab": {
      "name": "Naive_Bayes_Model1-Gaussian.ipynb",
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}