notebook.ipynb

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      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import re\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import pickle\n",
        "\n",
        "import warnings\n",
        "warnings.simplefilter(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
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      "source": [
        "# Loading the dataset\n",
        "data_one = pd.read_csv(\"/content/data/small_vocab_en-lg.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "shuffled_df = data_one.sample(frac=1)\n",
        "shuffled_df.to_csv(\"new_file_name.csv\", index=False)\n",
        "data = pd.read_csv(\"/content/new_file_name.csv\")"
      ]
    },
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Text</th>\n",
              "      <th>Language</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Abanoonyiboobubudamu bonna baasabiddwa beewand...</td>\n",
              "      <td>Luganda</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Baayise olukungaana lw'abanoonyiboobubudamu eg...</td>\n",
              "      <td>Luganda</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Abanoonyiboobubudamu b'abadde n'obutakkaanya w...</td>\n",
              "      <td>Luganda</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Twakubirizibwa okwaniriza abanoonyiboobubudamu...</td>\n",
              "      <td>Luganda</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bwe tukolera awamu enkulaakulana enyingi efuni...</td>\n",
              "      <td>Luganda</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Disitulikiti eziriraanye ensalo si ntebenkevu.</td>\n",
              "      <td>Luganda</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Abanoonyiboobubudamu batandise okulima okusobo...</td>\n",
              "      <td>Luganda</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Kimenya mateeka okubeera n'emmundu.</td>\n",
              "      <td>Luganda</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Awa nnyo bazadde be kitiibwa.</td>\n",
              "      <td>Luganda</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Omusajja anywa nnyo omwenge.</td>\n",
              "      <td>Luganda</td>\n",
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            "text/plain": [
              "                                                Text Language\n",
              "0  Abanoonyiboobubudamu bonna baasabiddwa beewand...  Luganda\n",
              "1  Baayise olukungaana lw'abanoonyiboobubudamu eg...  Luganda\n",
              "2  Abanoonyiboobubudamu b'abadde n'obutakkaanya w...  Luganda\n",
              "3  Twakubirizibwa okwaniriza abanoonyiboobubudamu...  Luganda\n",
              "4  Bwe tukolera awamu enkulaakulana enyingi efuni...  Luganda\n",
              "5     Disitulikiti eziriraanye ensalo si ntebenkevu.  Luganda\n",
              "6  Abanoonyiboobubudamu batandise okulima okusobo...  Luganda\n",
              "7                Kimenya mateeka okubeera n'emmundu.  Luganda\n",
              "8                      Awa nnyo bazadde be kitiibwa.  Luganda\n",
              "9                       Omusajja anywa nnyo omwenge.  Luganda"
            ]
          },
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          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head(10)"
      ]
    },
    {
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          "status": "completed"
        },
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "English    3377\n",
              "Luganda    3049\n",
              "Name: Language, dtype: int64"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# value count for each language\n",
        "data[\"Language\"].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "execution": {
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        "tags": []
      },
      "outputs": [],
      "source": [
        "# separating the independent and dependant features\n",
        "X = data[\"Text\"]\n",
        "y = data[\"Language\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "execution": {
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        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "# converting categorical variables to numerical\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "y = le.fit_transform(y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "18Pr7npIjle9",
        "papermill": {
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          "start_time": "2021-02-10T17:52:01.426027",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "# Text Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KmL_cLsjlvQF",
        "outputId": "c92bdcb1-efb9-4221-bf7e-f4e2e60edbb9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text        0\n",
              "Language    0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
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        "tags": []
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      "outputs": [],
      "source": [
        "data_list = []\n",
        "for text in X:\n",
        "    text = re.sub(r'[!@#$(),\\n\"%^*?\\:;~`0-9]', ' ', text)\n",
        "    text = re.sub(r'[[]]', ' ', text)\n",
        "    text = text.lower()\n",
        "    data_list.append(text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7oQTbYlxjlfA",
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          "start_time": "2021-02-10T17:52:01.579761",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "# Bag of Words"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-10T17:52:01.630415Z",
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        },
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          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "# creating bag of words using countvectorizer\n",
        "\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "cv = CountVectorizer()\n",
        "X = cv.fit_transform(data_list).toarray()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2021-02-10T17:52:02.481951Z",
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        "papermill": {
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          "exception": false,
          "start_time": "2021-02-10T17:52:02.461233",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(6426, 9241)"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MTeioxznjlfD",
        "papermill": {
          "duration": 0.015694,
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          "start_time": "2021-02-10T17:52:02.501452",
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        },
        "tags": []
      },
      "source": [
        "# Train Test Split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-10T17:52:02.553947Z",
          "iopub.status.busy": "2021-02-10T17:52:02.553282Z",
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        },
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          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size = 0.20)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8vMvkNttjlfE",
        "papermill": {
          "duration": 0.015847,
          "end_time": "2021-02-10T17:52:04.704861",
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          "start_time": "2021-02-10T17:52:04.689014",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "# Model creation and Prediction\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2021-02-10T17:52:04.747118Z",
          "iopub.status.busy": "2021-02-10T17:52:04.746445Z",
          "iopub.status.idle": "2021-02-10T17:52:34.949612Z",
          "shell.execute_reply": "2021-02-10T17:52:34.950127Z"
        },
        "id": "ftb5rIDMjlfE",
        "outputId": "e621f1e7-09b0-41f9-bf7b-2db921b437e9",
        "papermill": {
          "duration": 30.229279,
          "end_time": "2021-02-10T17:52:34.950303",
          "exception": false,
          "start_time": "2021-02-10T17:52:04.721024",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "MultinomialNB()"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "\n",
        "model = MultinomialNB()\n",
        "model.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-10T17:52:34.988161Z",
          "iopub.status.busy": "2021-02-10T17:52:34.987520Z",
          "iopub.status.idle": "2021-02-10T17:52:37.404847Z",
          "shell.execute_reply": "2021-02-10T17:52:37.404230Z"
        },
        "id": "DJyE0gvTjlfF",
        "papermill": {
          "duration": 2.437964,
          "end_time": "2021-02-10T17:52:37.404984",
          "exception": false,
          "start_time": "2021-02-10T17:52:34.967020",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "# prediction \n",
        "y_pred = model.predict(x_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yC56sELejlfG",
        "papermill": {
          "duration": 0.016432,
          "end_time": "2021-02-10T17:52:37.438350",
          "exception": false,
          "start_time": "2021-02-10T17:52:37.421918",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "# prediction \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-02-10T17:52:37.476748Z",
          "iopub.status.busy": "2021-02-10T17:52:37.476015Z",
          "iopub.status.idle": "2021-02-10T17:52:37.495623Z",
          "shell.execute_reply": "2021-02-10T17:52:37.495035Z"
        },
        "id": "i0rNEI5NjlfH",
        "papermill": {
          "duration": 0.041095,
          "end_time": "2021-02-10T17:52:37.495777",
          "exception": false,
          "start_time": "2021-02-10T17:52:37.454682",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
        "\n",
        "ac = accuracy_score(y_test, y_pred)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "cr = classification_report(y_test, y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2021-02-10T17:52:37.533855Z",
          "iopub.status.busy": "2021-02-10T17:52:37.533288Z",
          "iopub.status.idle": "2021-02-10T17:52:37.536858Z",
          "shell.execute_reply": "2021-02-10T17:52:37.537389Z"
        },
        "id": "iiJZGxGKjlfH",
        "outputId": "77e38cd4-08ca-456a-8333-8e1824e1214e",
        "papermill": {
          "duration": 0.025062,
          "end_time": "2021-02-10T17:52:37.537544",
          "exception": false,
          "start_time": "2021-02-10T17:52:37.512482",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy is : 1.0\n"
          ]
        }
      ],
      "source": [
        "print(\"Accuracy is :\",ac)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2021-02-10T17:52:37.575830Z",
          "iopub.status.busy": "2021-02-10T17:52:37.575222Z",
          "iopub.status.idle": "2021-02-10T17:52:37.579168Z",
          "shell.execute_reply": "2021-02-10T17:52:37.579890Z"
        },
        "id": "938PMwRLjlfH",
        "outputId": "d82db690-29f3-492a-f23c-699661c4ba62",
        "papermill": {
          "duration": 0.025512,
          "end_time": "2021-02-10T17:52:37.580114",
          "exception": false,
          "start_time": "2021-02-10T17:52:37.554602",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00       661\n",
            "           1       1.00      1.00      1.00       625\n",
            "\n",
            "    accuracy                           1.00      1286\n",
            "   macro avg       1.00      1.00      1.00      1286\n",
            "weighted avg       1.00      1.00      1.00      1286\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# classification report\n",
        "print(cr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "execution": {
          "iopub.execute_input": "2021-02-10T17:52:37.640063Z",
          "iopub.status.busy": "2021-02-10T17:52:37.639427Z",
          "iopub.status.idle": "2021-02-10T17:52:39.369739Z",
          "shell.execute_reply": "2021-02-10T17:52:39.369077Z"
        },
        "id": "9beWtEn7jlfI",
        "outputId": "6c3196fa-5281-406f-8381-6943a30ee14f",
        "papermill": {
          "duration": 1.771423,
          "end_time": "2021-02-10T17:52:39.369879",
          "exception": false,
          "start_time": "2021-02-10T17:52:37.598456",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# visualising the confusion matrix\n",
        "plt.figure(figsize=(15,10))\n",
        "sns.heatmap(cm, annot = True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "BeazbTlKs1hN"
      },
      "outputs": [],
      "source": [
        "def predict(text):\n",
        "     x = cv.transform([text]).toarray() # converting text to bag of words model (Vector)\n",
        "     lang = model.predict(x) # predicting the language\n",
        "     lang = le.inverse_transform(lang) # finding the language corresponding the the predicted value\n",
        "     print(\"The langauge is in\",lang[0]) # printing the language"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QP15au6ejlfI",
        "outputId": "88d15338-baf6-463a-b808-3b8b32bff256",
        "papermill": {
          "duration": 0.018692,
          "end_time": "2021-02-10T17:52:39.407689",
          "exception": false,
          "start_time": "2021-02-10T17:52:39.388997",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The langauge is in Luganda\n"
          ]
        }
      ],
      "source": [
        "predict('Obumenyi bw’amateeka bufuukidde Pulezidenti n’eggwanga ekizibu.')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import pickle\n",
        "\n",
        "# Save to file in the current working directory\n",
        "pkl_filename = \"pickle_model.pkl\"\n",
        "with open(pkl_filename, 'wb') as file:\n",
        "    pickle.dump(model, file)\n",
        "\n",
        "# Load from file\n",
        "with open(pkl_filename, 'rb') as file:\n",
        "    pickle_model = pickle.load(file)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# saving both cv and model\n",
        "pickle.dump(cv, open(\"transform.pkl\", \"wb\"))\n",
        "pickle.dump(model, open(\"model.pkl\", \"wb\"))"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "language-detection-using-nlp.ipynb",
      "provenance": []
    },
    "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": "3.7.9"
    },
    "papermill": {
      "default_parameters": {},
      "duration": 45.67269,
      "end_time": "2021-02-10T17:52:40.137532",
      "environment_variables": {},
      "exception": null,
      "input_path": "__notebook__.ipynb",
      "output_path": "__notebook__.ipynb",
      "parameters": {},
      "start_time": "2021-02-10T17:51:54.464842",
      "version": "2.2.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}