IMDB_Dataset_Classification.ipynb

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        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set_style('darkgrid')"
      ]
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      "source": [
        "df_review = pd.read_csv(\"IMDB Dataset.csv\")\n",
        "df_review"
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              "                                                  review sentiment\n",
              "0      One of the other reviewers has mentioned that ...  positive\n",
              "1      A wonderful little production. <br /><br />The...  positive\n",
              "2      I thought this was a wonderful way to spend ti...  positive\n",
              "3      Basically there's a family where a little boy ...  negative\n",
              "4      Petter Mattei's \"Love in the Time of Money\" is...  positive\n",
              "...                                                  ...       ...\n",
              "49995  I thought this movie did a down right good job...  positive\n",
              "49996  Bad plot, bad dialogue, bad acting, idiotic di...  negative\n",
              "49997  I am a Catholic taught in parochial elementary...  negative\n",
              "49998  I'm going to have to disagree with the previou...  negative\n",
              "49999  No one expects the Star Trek movies to be high...  negative\n",
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              "summary": "{\n  \"name\": \"df_review\",\n  \"rows\": 50000,\n  \"fields\": [\n    {\n      \"column\": \"review\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 49582,\n        \"samples\": [\n          \"\\\"Soul Plane\\\" is a horrible attempt at comedy that only should appeal people with thick skulls, bloodshot eyes and furry pawns. <br /><br />The plot is not only incoherent but also non-existent, acting is mostly sub sub-par with a gang of highly moronic and dreadful characters thrown in for bad measure, jokes are often spotted miles ahead and almost never even a bit amusing. This movie lacks any structure and is full of racial stereotypes that must have seemed old even in the fifties, the only thing it really has going for it is some pretty ladies, but really, if you want that you can rent something from the \\\"Adult\\\" section. OK?<br /><br />I can hardly see anything here to recommend since you'll probably have a lot a better and productive time chasing rats with a sledgehammer or inventing waterproof teabags or whatever.<br /><br />2/10\",\n          \"Guest from the Future tells a fascinating story of time travel, friendship, battle of good and evil -- all with a small budget, child actors, and few special effects. Something for Spielberg and Lucas to learn from. ;) A sixth-grader Kolya \\\"Nick\\\" Gerasimov finds a time machine in the basement of a decrepit building and travels 100 years into the future. He discovers a near-perfect, utopian society where robots play guitars and write poetry, everyone is kind to each other and people enjoy everything technology has to offer. Alice is the daughter of a prominent scientist who invented a device called Mielophone that allows to read minds of humans and animals. The device can be put to both good and bad use, depending on whose hands it falls into. When two evil space pirates from Saturn who want to rule the universe attempt to steal Mielophone, it falls into the hands of 20th century school boy Nick. With the pirates hot on his tracks, he travels back to his time, followed by the pirates, and Alice. Chaos, confusion and funny situations follow as the luckless pirates try to blend in with the earthlings. Alice enrolls in the same school Nick goes to and demonstrates superhuman abilities in PE class. The catch is, Alice doesn't know what Nick looks like, while the pirates do. Also, the pirates are able to change their appearance and turn literally into anyone. (Hmm, I wonder if this is where James Cameron got the idea for Terminator...) Who gets to Nick -- and Mielophone -- first? Excellent plot, non-stop adventures, and great soundtrack. I wish Hollywood made kid movies like this one...\",\n          \"\\\"National Treasure\\\" (2004) is a thoroughly misguided hodge-podge of plot entanglements that borrow from nearly every cloak and dagger government conspiracy clich\\u00e9 that has ever been written. The film stars Nicholas Cage as Benjamin Franklin Gates (how precious is that, I ask you?); a seemingly normal fellow who, for no other reason than being of a lineage of like-minded misguided fortune hunters, decides to steal a 'national treasure' that has been hidden by the United States founding fathers. After a bit of subtext and background that plays laughably (unintentionally) like Indiana Jones meets The Patriot, the film degenerates into one misguided whimsy after another \\u0096 attempting to create a 'Stanley Goodspeed' regurgitation of Nicholas Cage and launch the whole convoluted mess forward with a series of high octane, but disconnected misadventures.<br /><br />The relevancy and logic to having George Washington and his motley crew of patriots burying a king's ransom someplace on native soil, and then, going through the meticulous plan of leaving clues scattered throughout U.S. currency art work, is something that director Jon Turteltaub never quite gets around to explaining. Couldn't Washington found better usage for such wealth during the start up of the country? Hence, we are left with a mystery built on top of an enigma that is already on shaky ground by the time Ben appoints himself the new custodian of this untold wealth. Ben's intentions are noble \\u0096 if confusing. He's set on protecting the treasure. For who and when?\\u0085your guess is as good as mine.<br /><br />But there are a few problems with Ben's crusade. First up, his friend, Ian Holmes (Sean Bean) decides that he can't wait for Ben to make up his mind about stealing the Declaration of Independence from the National Archives (oh, yeah \\u0096 brilliant idea!). Presumably, the back of that famous document holds the secret answer to the ultimate fortune. So Ian tries to kill Ben. The assassination attempt is, of course, unsuccessful, if overly melodramatic. It also affords Ben the opportunity to pick up, and pick on, the very sultry curator of the archives, Abigail Chase (Diane Kruger). She thinks Ben is clearly a nut \\u0096 at least at the beginning. But true to action/romance form, Abby's resolve melts quicker than you can say, \\\"is that the Hope Diamond?\\\" The film moves into full X-File-ish mode, as the FBI, mistakenly believing that Ben is behind the theft, retaliate in various benign ways that lead to a multi-layering of action sequences reminiscent of Mission Impossible meets The Fugitive. Honestly, don't those guys ever get 'intelligence' information that is correct? In the final analysis, \\\"National Treasure\\\" isn't great film making, so much as it's a patchwork rehash of tired old bits from other movies, woven together from scraps, the likes of which would make IL' Betsy Ross blush.<br /><br />The Buena Vista DVD delivers a far more generous treatment than this film is deserving of. The anamorphic widescreen picture exhibits a very smooth and finely detailed image with very rich colors, natural flesh tones, solid blacks and clean whites. The stylized image is also free of blemishes and digital enhancements. The audio is 5.1 and delivers a nice sonic boom to your side and rear speakers with intensity and realism. Extras include a host of promotional junket material that is rather deep and over the top in its explanation of how and why this film was made. If only, as an audience, we had had more clarification as to why Ben and co. were chasing after an illusive treasure, this might have been one good flick. Extras conclude with the theatrical trailer, audio commentary and deleted scenes. Not for the faint-hearted \\u0096 just the thick-headed.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"negative\",\n          \"positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_positive = df_review[df_review['sentiment']=='positive'][:9000]\n",
        "df_negative = df_review[df_review['sentiment']=='negative'][:1000]\n",
        "\n",
        "df_review_imb = pd.concat([df_positive,df_negative ])"
      ],
      "metadata": {
        "id": "5PeRBwb4u4UI"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "colors = sns.color_palette('deep')\n",
        "\n",
        "plt.figure(figsize=(8,4), tight_layout=True)\n",
        "plt.bar(x=['Positive', 'Negative'],\n",
        "        height=df_review_imb.value_counts(['sentiment']),\n",
        "        color=colors[:2])\n",
        "plt.title('Sentiment')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "D_SNx1mKu6dV",
        "outputId": "f0f07bdc-0a9d-4124-ad73-d0895c748c8f"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "rus = RandomUnderSampler(random_state= 0)\n",
        "df_review_bal,df_review_bal['sentiment']=rus.fit_resample(df_review_imb[['review']],df_review_imb['sentiment'])\n",
        "df_review_bal"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "HCDw6i_au8iP",
        "outputId": "f5aed5b4-1a57-42f7-b22b-c04d6c6fba29"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                 review sentiment\n",
              "0     Basically there's a family where a little boy ...  negative\n",
              "1     This show was an amazing, fresh & innovative i...  negative\n",
              "2     Encouraged by the positive comments about this...  negative\n",
              "3     Phil the Alien is one of those quirky films wh...  negative\n",
              "4     I saw this movie when I was about 12 when it c...  negative\n",
              "...                                                 ...       ...\n",
              "1995  Knute Rockne led an extraordinary life and his...  positive\n",
              "1996  At the height of the 'Celebrity Big Brother' r...  positive\n",
              "1997  This is another of Robert Altman's underrated ...  positive\n",
              "1998  This movie won a special award at Cannes for i...  positive\n",
              "1999  You'd be forgiven to think a Finnish director ...  positive\n",
              "\n",
              "[2000 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-04966df2-7fd1-4fee-af00-ec83467e5f16\" class=\"colab-df-container\">\n",
              "    <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>review</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Basically there's a family where a little boy ...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>This show was an amazing, fresh &amp; innovative i...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Encouraged by the positive comments about this...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Phil the Alien is one of those quirky films wh...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>I saw this movie when I was about 12 when it c...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995</th>\n",
              "      <td>Knute Rockne led an extraordinary life and his...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1996</th>\n",
              "      <td>At the height of the 'Celebrity Big Brother' r...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1997</th>\n",
              "      <td>This is another of Robert Altman's underrated ...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1998</th>\n",
              "      <td>This movie won a special award at Cannes for i...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999</th>\n",
              "      <td>You'd be forgiven to think a Finnish director ...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2000 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-04966df2-7fd1-4fee-af00-ec83467e5f16')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      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",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-04966df2-7fd1-4fee-af00-ec83467e5f16 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-04966df2-7fd1-4fee-af00-ec83467e5f16');\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",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-73fc14d4-b548-40c8-913b-614c6e1f8b6c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-73fc14d4-b548-40c8-913b-614c6e1f8b6c')\"\n",
              "            title=\"Suggest charts\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-73fc14d4-b548-40c8-913b-614c6e1f8b6c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_a442ec9f-64c0-4fd7-bc1f-8c2e8b2fa4b4\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        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",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_review_bal')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_a442ec9f-64c0-4fd7-bc1f-8c2e8b2fa4b4 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_review_bal');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_review_bal",
              "summary": "{\n  \"name\": \"df_review_bal\",\n  \"rows\": 2000,\n  \"fields\": [\n    {\n      \"column\": \"review\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2000,\n        \"samples\": [\n          \"Refreshing `lost' gem! Featuring effective dialog combined with excellent acting to establish the characters and involve you enough to care what happens to them. The Douglas and Widmark characters are realistic heroes. Palance is his usual evil presence. Widmark win the fisticuffs fight scene, a car chase of less than 60 seconds with a `logical' end, and a lengthy chase on foot that shames the overdone chase sequences of contemporary Hollywood. You know how it will likely end, but the suspense and interest are sustained throughout. The end of the chase is one of the most realistic you will ever see. The film seems to slow a little past the middle, but stay with it for the rewarding conclusion.\",\n          \"Never saw the original movie in the series...I only hope it was a much better movie than this or the sequel made in the 1980's as if it is not how were these two terrible sequels even justified. This movie had a really good lead in when they were advertising it to be shown on one of those old independent stations that are a thing of the past now. Anyways it looked like it would be a pretty good scary movie. It was, however, a movie that would make some Walt Disney movies look dark. Really, this movie was just a bunch of light fluff with virtually no boggy creek creature to be seen. The only real sighting is near the end when you see its shape during a very heavy rainstorm, other than that there is virtually no sign of the creature which was really disappointing as a kid. The story is basically the old evil hunters must kill anything they see and are after the boggy creek creature and kids are out to help it or just some random hairy guy in the woods that likes to pull random boats through the water. Not really worth watching I would however like to see the original, granted the maker of that would make the also bad boggy creature of the 80's, but he also made a very good slasher movie in the 70's \\\"The Town the Dreaded Sundown\\\".\",\n          \"Once upon a time, in Sweden, there was a poor Salvation Army sister. At death's door, she requests, \\\"Send for David Holm!\\\" But, Victor Sj\\u00f6str\\u00f6m (as David Holm) cannot be located, because he is spending New Year's Eve in a graveyard, with his drinking buddies. Dying Sister Astrid Holm (as Edit) wants to see if praying for Mr. Sj\\u00f6str\\u00f6m's soul, over the past year, has produced any results; arguably, it has not. In the graveyard, Sj\\u00f6str\\u00f6m tells the story of \\\"The Phantom Carriage\\\", which he heard from his dead friend Tore Svennberg (as Georges). According to legend, the last person to die in each year must pick up the souls of all the dead people, until being relieved next New Year's Eve...<br /><br />Director Sj\\u00f6str\\u00f6m, whose lead performance is very strong, combines with photographer Julius Jaenzon to create a visually appealing film. The great \\\"double exposure\\\" effect is used frequently, but never seems overdone; and, it doesn't make the film's other dramatic highlights any less memorable (for example, Sj\\u00f6str\\u00f6m's tearing of his sewn coat and axing of the door). A Selma Lagerl\\u00f6f story probably wasn't one you could, or would want to, tamper with in the 1920s - which may, or may not be, why the ending of this film is a letdown. And, unlike similar spiritual stories, it's difficult to suspend your disbelief, if you think too carefully about what is really happening in \\\"K\\u00f6rkarlen\\\".<br /><br />******* K\\u00f6rkarlen (1/1/21) Victor Sj\\u00f6str\\u00f6m ~ Victor Sj\\u00f6str\\u00f6m, Hilda Borgstr\\u00f6m, Tore Svennberg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"positive\",\n          \"negative\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_review_imb.value_counts('sentiment'))\n",
        "print(df_review_bal.value_counts('sentiment'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AtIOLbnRvAOs",
        "outputId": "9695fa21-8f34-4b05-9961-7779fa94bc59"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sentiment\n",
            "positive    9000\n",
            "negative    1000\n",
            "Name: count, dtype: int64\n",
            "sentiment\n",
            "negative    1000\n",
            "positive    1000\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train,test = train_test_split(df_review_bal,test_size =0.33,random_state=42)"
      ],
      "metadata": {
        "id": "AA5drslYvED7"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_x, train_y = train['review'], train['sentiment']\n",
        "test_x, test_y = test['review'], test['sentiment']"
      ],
      "metadata": {
        "id": "GNvwMR1lvFxA"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_y.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0-cJBMJHvHiz",
        "outputId": "6c4ba4d3-b832-4bdc-b137-29eaa849587a"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sentiment\n",
              "negative    675\n",
              "positive    665\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "tfidf = TfidfVectorizer(stop_words='english')\n",
        "train_x_vector = tfidf.fit_transform(train_x)\n",
        "# also fit the test_x_vector\n",
        "test_x_vector = tfidf.transform(test_x)\n",
        "sparse_df = pd.DataFrame.sparse.from_spmatrix(train_x_vector, index=train_x.index, columns=feature_names)"
      ],
      "metadata": {
        "id": "jd19F5x-vJav"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "svc = SVC(kernel='linear')\n",
        "svc.fit(train_x_vector, train_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "QdCNEKmkvM70",
        "outputId": "b1271ff3-b349-4bd5-947e-9ba7fc9d1198"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(kernel='linear')"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(svc.predict(tfidf.transform(['A good movie'])))\n",
        "print(svc.predict(tfidf.transform(['An excellent movie'])))\n",
        "print(svc.predict(tfidf.transform(['I did not like this movie at all I gave this movie away'])))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nQ2v9U2IvPpa",
        "outputId": "009a10b8-01a1-4b6a-de7f-2d6a10caca9e"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['positive']\n",
            "['positive']\n",
            "['negative']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#DECISION TREE"
      ],
      "metadata": {
        "id": "Yi6uyr2cvWRe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "dec_tree = DecisionTreeClassifier()\n",
        "dec_tree.fit(train_x_vector, train_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "w4fmdihfvRkA",
        "outputId": "a447f99a-e65b-4b3b-a6d6-5b255c301d9b"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#NAIVE BAYES"
      ],
      "metadata": {
        "id": "X-8wRcbLvaFt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "gnb = GaussianNB()\n",
        "gnb.fit(train_x_vector.toarray(), train_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "wT4J2iBovTbd",
        "outputId": "505753b3-884d-4974-9df2-8bc5294a699e"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#LOGISTIC REGRESSION"
      ],
      "metadata": {
        "id": "M2INuSo0vcp2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "log_reg = LogisticRegression()\n",
        "log_reg.fit(train_x_vector,train_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "KirrxzXDvfVS",
        "outputId": "5d42b7c1-ea85-4387-f9b2-4abe6278ab6d"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#MEAN ACCURACY"
      ],
      "metadata": {
        "id": "CzSaCc90vjWq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(svc.score(test_x_vector, test_y))\n",
        "print(dec_tree.score(test_x_vector, test_y))\n",
        "print(gnb.score(test_x_vector.toarray(), test_y))\n",
        "print(log_reg.score(test_x_vector, test_y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ONNIhcsAvlvO",
        "outputId": "5a3bf271-d534-4657-eb39-7833d24f1429"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8409090909090909\n",
            "0.6545454545454545\n",
            "0.6348484848484849\n",
            "0.8303030303030303\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#F1 SCORE"
      ],
      "metadata": {
        "id": "ln0sjnDqv7iE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import f1_score\n",
        "f1_score(test_y,svc.predict(test_x_vector),\n",
        "          labels = ['positive','negative'],average=None)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d4Bf6N_VvoJM",
        "outputId": "82b380aa-8b4d-4fd5-a253-5e4045b8ff33"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.84671533, 0.83464567])"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#CLASSIFICATION REPORT"
      ],
      "metadata": {
        "id": "l0nqHVivwAWB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(test_y,\n",
        "                            svc.predict(test_x_vector),\n",
        "                            labels = ['positive','negative']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tx6DO7aLwCiY",
        "outputId": "3aad13df-6f98-46cf-c45f-ad068051b3e6"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "    positive       0.83      0.87      0.85       335\n",
            "    negative       0.85      0.82      0.83       325\n",
            "\n",
            "    accuracy                           0.84       660\n",
            "   macro avg       0.84      0.84      0.84       660\n",
            "weighted avg       0.84      0.84      0.84       660\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#CONFUSION MATRIX"
      ],
      "metadata": {
        "id": "2AQNnIKKwF8N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "conf_mat = confusion_matrix(test_y,\n",
        "                           svc.predict(test_x_vector),\n",
        "                           labels = ['positive', 'negative'])\n",
        "conf_mat"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "brS-2CrEwEnl",
        "outputId": "da0a793b-ebef-4bcd-dab5-5ea7139d5184"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[290,  45],\n",
              "       [ 60, 265]])"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#TUNING THE MODEL"
      ],
      "metadata": {
        "id": "lOLIfTP6wKnp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "train_y_encoded = le.fit_transform(train_y)\n",
        "test_y_encoded = le.fit_transform(test_y)"
      ],
      "metadata": {
        "id": "uqa_M_bAwNpC"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import xgboost as xgb\n",
        "from sklearn.metrics import accuracy_score, f1_score, classification_report, confusion_matrix\n",
        "\n",
        "xgb_clf = xgb.XGBClassifier(use_label_encoder=False, eval_metric='mlogloss')\n",
        "xgb_clf.fit(train_x_vector, train_y_encoded)\n",
        "\n",
        "xgb_pred = xgb_clf.predict(test_x_vector)\n",
        "\n",
        "xgb_accuracy = accuracy_score(test_y_encoded, xgb_pred)*100\n",
        "print(f\"XGBoost Accuracy: {xgb_accuracy}\")\n",
        "\n",
        "xgb_f1_score = f1_score(test_y_encoded, xgb_pred, labels=['1', '0'], average=None)\n",
        "print(f\"XGBoost F1 Score: {xgb_f1_score}\")\n",
        "\n",
        "xgb_classification_report = classification_report(test_y_encoded, xgb_pred, labels=['1', '0'])\n",
        "print(f\"XGBoost Classification Report:\\n{xgb_classification_report}\")"
      ],
      "metadata": {
        "id": "Je1gVMvjx5m7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a34a6cb4-9bde-4212-92bd-cc2a5106421b"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGBoost Accuracy: 76.81818181818181\n",
            "XGBoost F1 Score: [0.76712329 0.76923077]\n",
            "XGBoost Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.78      0.75      0.77       335\n",
            "           0       0.75      0.78      0.77       325\n",
            "\n",
            "   micro avg       0.77      0.77      0.77       660\n",
            "   macro avg       0.77      0.77      0.77       660\n",
            "weighted avg       0.77      0.77      0.77       660\n",
            "\n"
          ]
        }
      ]
    }
  ]
}