Post_recommender_system.ipynb

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      "name": "Post_recommender_system.ipynb",
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      "cell_type": "markdown",
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        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/Neel7317/Machine_learning/blob/master/Post_recommender_system.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mzrJXx0wUufV"
      },
      "source": [
        "#importing necessary libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics.pairwise import linear_kernel\n",
        "#putting movies data on 'movies' dataframe\n"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NdD82_CPU6kK"
      },
      "source": [
        "df = pd.read_csv('posts.csv')#read data from CSV file"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>_id</th>\n",
              "      <th>title</th>\n",
              "      <th>category</th>\n",
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              "      <th>4</th>\n",
              "      <td>5d80dfbc6c53455f896e600e</td>\n",
              "      <td>How Competition law evolved?</td>\n",
              "      <td>Competition Laws</td>\n",
              "      <td>blog</td>\n",
              "    </tr>\n",
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            "text/plain": [
              "                        _id  ...  post_type\n",
              "0  5d62abaa65218653a132c956  ...       blog\n",
              "1  5d6d39567fa40e1417a4931c  ...       blog\n",
              "2  5d7d23315720533e15c3b1ee  ...       blog\n",
              "3  5d7d405e5720533e15c3b1f3  ...    artwork\n",
              "4  5d80dfbc6c53455f896e600e  ...       blog\n",
              "\n",
              "[5 rows x 4 columns]"
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          "metadata": {
            "tags": []
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          "execution_count": 25
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    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "df = df.assign(_id=(df['title'] + '_' + df['category']).astype('category').cat.codes)#make id int from hexa decimal\n",
        "df.head()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>_id</th>\n",
              "      <th>title</th>\n",
              "      <th>category</th>\n",
              "      <th>post_type</th>\n",
              "    </tr>\n",
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              "      <th>0</th>\n",
              "      <td>443</td>\n",
              "      <td>hello there</td>\n",
              "      <td>Plant Biotechnology</td>\n",
              "      <td>blog</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>248</td>\n",
              "      <td>Ml and AI</td>\n",
              "      <td>Artificial Intelligence|Machine Learning|Infor...</td>\n",
              "      <td>blog</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>418</td>\n",
              "      <td>What is an Operating System ?</td>\n",
              "      <td>Operating Systems</td>\n",
              "      <td>blog</td>\n",
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              "      <td>Lord Shiva</td>\n",
              "      <td>Drawings</td>\n",
              "      <td>artwork</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>177</td>\n",
              "      <td>How Competition law evolved?</td>\n",
              "      <td>Competition Laws</td>\n",
              "      <td>blog</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            "text/plain": [
              "   _id  ...  post_type\n",
              "0  443  ...       blog\n",
              "1  248  ...       blog\n",
              "2  418  ...       blog\n",
              "3  227  ...    artwork\n",
              "4  177  ...       blog\n",
              "\n",
              "[5 rows x 4 columns]"
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          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
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    {
      "cell_type": "code",
      "metadata": {
        "id": "FCWe-j4aVyBv",
        "outputId": "57677903-b63d-4f4b-8446-6f0c979faba8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "df['category'][0]"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Plant Biotechnology'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qNZX9kRrV-nv"
      },
      "source": [
        "tfidf = TfidfVectorizer(stop_words='english')"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kj2cPIlUWJaf"
      },
      "source": [
        "df['category'] = df['category'].fillna('')#fill NaN value with blank space string"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b6hBNgGXWpzv"
      },
      "source": [
        "#Construct the required TF-IDF matrix by applying the fit_transform method on the category feature\n",
        "category_matrix = tfidf.fit_transform(df['category'])"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k8rWkb5XW7yv",
        "outputId": "f3ceb15e-254d-4b76-9473-5b5d8f769aa9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "##Output the shape of tfidf_matrix\n",
        "category_matrix.shape"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(493, 266)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
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    {
      "cell_type": "code",
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          "base_uri": "https://localhost:8080/"
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      },
      "source": [
        "similarity_matrix = linear_kernel(category_matrix,category_matrix)\n",
        "\n",
        "similarity_matrix"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
              "        0.        ],\n",
              "       [0.        , 1.        , 0.        , ..., 0.        , 0.15033338,\n",
              "        0.30692944],\n",
              "       [0.        , 0.        , 1.        , ..., 0.        , 0.        ,\n",
              "        0.        ],\n",
              "       ...,\n",
              "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
              "        0.        ],\n",
              "       [0.        , 0.15033338, 0.        , ..., 0.        , 1.        ,\n",
              "        0.31439084],\n",
              "       [0.        , 0.30692944, 0.        , ..., 0.        , 0.31439084,\n",
              "        1.        ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-kEkiqoCXRYv",
        "outputId": "6de41a57-af93-49fd-dbfe-98d49718228d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#posts index mapping\n",
        "mapping = pd.Series(df.index,index = df['title'])\n",
        "mapping"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "title\n",
              "hello there                                                              0\n",
              "Ml and AI                                                                1\n",
              "What is an Operating System ?                                            2\n",
              "Lord Shiva                                                               3\n",
              "How Competition law evolved?                                             4\n",
              "                                                                      ... \n",
              "CI/CD using GitHub Actions                                             488\n",
              "Configure Docker with Django; PostgreSQL; Pg-admin & Elasticsearch     489\n",
              "Recommendation Engine                                                  490\n",
              "Face Recognition using Transfer Learning.                              491\n",
              "Configure CI/CD Pipeline in GitLab and deployment to server via SSH    492\n",
              "Length: 493, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c49pD_DoYKTP"
      },
      "source": [
        "def recommend_movies_based_on_plot(title):\n",
        "  post_index= mapping[title]\n",
        "  #get similarity values with other movies\n",
        "  #similarity_score is the list of index and similarity matrix\n",
        "  similarity_score = list(enumerate(similarity_matrix[post_index]))\n",
        "  #sort in descending order the similarity score of movie inputted with all the other movies\n",
        "  similarity_score = sorted(similarity_score, key=lambda x: x[1], reverse=True)\n",
        "  # Get the scores of the 15 most similar movies. Ignore the first movie.\n",
        "  similarity_score = similarity_score[1:10]\n",
        "  #return movie names using the mapping series\n",
        "  movie_indices = [i[0] for i in similarity_score]\n",
        "  return (df['title'].iloc[movie_indices])"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jzDaDYdFZKwZ",
        "outputId": "7b548ca8-aec2-4404-9032-33bd61ba7e41",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(recommend_movies_based_on_plot('Ml and AI'))#input post title and recommend post similar to that post"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "122                              Artificial Intelligence\n",
            "170                                         Types Of AI.\n",
            "186    7 Best Python Data Science Courses & Certifica...\n",
            "224    10 Best Artificial Intelligence (AI) Courses O...\n",
            "450               Machine Learning”&“Operations\" (MlOps)\n",
            "454                                   GAN's INTRODUCTION\n",
            "455                                        GAN's Part(2)\n",
            "456                   Recommend Systems Machine Learning\n",
            "486                                          Learning...\n",
            "Name: title, dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4vDkH-yWZb6o",
        "outputId": "61a490cc-3f9c-48bd-a58d-10ce84a35a3a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(recommend_movies_based_on_plot('Artificial Intelligence'))#input post title and recommend post similar to that post"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "170                                         Types Of AI.\n",
            "169             What sports will look like in the future\n",
            "1                                              Ml and AI\n",
            "291             8 Best Machine Learning Courses for 2020\n",
            "375    6 Best + Free PLC Programming Training & Cours...\n",
            "292    10 Best UI UX Design Course & Certification [2...\n",
            "106                                Topology & it's types\n",
            "107                                 Baseband & Broadband\n",
            "108                                   Information System\n",
            "Name: title, dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sDN9SNG8cAhJ",
        "outputId": "525f2919-fbfb-41a8-8954-a0e078ef62b9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(recommend_movies_based_on_plot('Lord Shiva'))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "11          Trident\n",
            "12       Lord shiva\n",
            "15           Kid Bu\n",
            "26    Shadow Sketch\n",
            "30       The Chosen\n",
            "34          Spartan\n",
            "45     ROMAN REIGNS\n",
            "46               OM\n",
            "47        MK Swords\n",
            "Name: title, dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BsNUX5yLcmm5"
      },
      "source": [
        ""
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      "execution_count": null,
      "outputs": []
    }
  ]
}