Coursera_Course_Recommendation_Engine.ipynb

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    "colab": {
      "name": "Coursera Course Recommendation Engine",
      "provenance": [],
      "authorship_tag": "ABX9TyOtR8TVigUv+dAk1xbBzqrl",
      "include_colab_link": true
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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/DataMinati/Super-Notebooks-100/blob/main/Coursera_Course_Recommendation_Engine.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Importing necessary libraries"
      ],
      "metadata": {
        "id": "j4oFwlx5GaI1"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
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        },
        "id": "TBCaym-EB9hF",
        "outputId": "01412c6c-5ac7-4078-fa31-4d884a897f03"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The necessary packages are included successfully!\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from wordcloud import WordCloud\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics.pairwise import linear_kernel\n",
        "sns.set_style('darkgrid')\n",
        "plt.style.use('fivethirtyeight')\n",
        "print(\"The necessary packages are included successfully!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Importing the dataset\n"
      ],
      "metadata": {
        "id": "Xbnq_7omGdAK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "url = 'https://raw.githubusercontent.com/MainakRepositor/Datasets-/master/Coursera.csv'\n",
        "df = pd.read_csv(url)\n",
        "df.head()"
      ],
      "metadata": {
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          "height": 354
        },
        "id": "dcMxsXHsCdQZ",
        "outputId": "8222f911-dc05-4a8e-df95-a2526256781f"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\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>Course Name</th>\n",
              "      <th>University</th>\n",
              "      <th>Difficulty Level</th>\n",
              "      <th>Course Rating</th>\n",
              "      <th>Course URL</th>\n",
              "      <th>Course Description</th>\n",
              "      <th>Skills</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Write A Feature Length Screenplay For Film Or ...</td>\n",
              "      <td>Michigan State University</td>\n",
              "      <td>Beginner</td>\n",
              "      <td>4.8</td>\n",
              "      <td>https://www.coursera.org/learn/write-a-feature...</td>\n",
              "      <td>Write a Full Length Feature Film Script  In th...</td>\n",
              "      <td>Drama  Comedy  peering  screenwriting  film  D...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Business Strategy: Business Model Canvas Analy...</td>\n",
              "      <td>Coursera Project Network</td>\n",
              "      <td>Beginner</td>\n",
              "      <td>4.8</td>\n",
              "      <td>https://www.coursera.org/learn/canvas-analysis...</td>\n",
              "      <td>By the end of this guided project, you will be...</td>\n",
              "      <td>Finance  business plan  persona (user experien...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Silicon Thin Film Solar Cells</td>\n",
              "      <td>�cole Polytechnique</td>\n",
              "      <td>Advanced</td>\n",
              "      <td>4.1</td>\n",
              "      <td>https://www.coursera.org/learn/silicon-thin-fi...</td>\n",
              "      <td>This course consists of a general presentation...</td>\n",
              "      <td>chemistry  physics  Solar Energy  film  lambda...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Finance for Managers</td>\n",
              "      <td>IESE Business School</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>4.8</td>\n",
              "      <td>https://www.coursera.org/learn/operational-fin...</td>\n",
              "      <td>When it comes to numbers, there is always more...</td>\n",
              "      <td>accounts receivable  dupont analysis  analysis...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Retrieve Data using Single-Table SQL Queries</td>\n",
              "      <td>Coursera Project Network</td>\n",
              "      <td>Beginner</td>\n",
              "      <td>4.6</td>\n",
              "      <td>https://www.coursera.org/learn/single-table-sq...</td>\n",
              "      <td>In this course you�ll learn how to effectively...</td>\n",
              "      <td>Data Analysis  select (sql)  database manageme...</td>\n",
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            "text/plain": [
              "                                         Course Name  ...                                             Skills\n",
              "0  Write A Feature Length Screenplay For Film Or ...  ...  Drama  Comedy  peering  screenwriting  film  D...\n",
              "1  Business Strategy: Business Model Canvas Analy...  ...  Finance  business plan  persona (user experien...\n",
              "2                      Silicon Thin Film Solar Cells  ...  chemistry  physics  Solar Energy  film  lambda...\n",
              "3                               Finance for Managers  ...  accounts receivable  dupont analysis  analysis...\n",
              "4       Retrieve Data using Single-Table SQL Queries  ...  Data Analysis  select (sql)  database manageme...\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##3. Data Cleaning"
      ],
      "metadata": {
        "id": "llPAZPwJGi43"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# This dataframe contains some useless columns which must be dropped for a better analytics result\n",
        "uc = ['Course URL']  # uc means useless columns\n",
        "df = df.drop(columns=uc)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "2hJFgUS4CkeQ",
        "outputId": "aa4cdd56-4392-42a2-e01f-00cc22578ab6"
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      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
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              "      <th>Course Name</th>\n",
              "      <th>University</th>\n",
              "      <th>Difficulty Level</th>\n",
              "      <th>Course Rating</th>\n",
              "      <th>Course Description</th>\n",
              "      <th>Skills</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Write A Feature Length Screenplay For Film Or ...</td>\n",
              "      <td>Michigan State University</td>\n",
              "      <td>Beginner</td>\n",
              "      <td>4.8</td>\n",
              "      <td>Write a Full Length Feature Film Script  In th...</td>\n",
              "      <td>Drama  Comedy  peering  screenwriting  film  D...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Business Strategy: Business Model Canvas Analy...</td>\n",
              "      <td>Coursera Project Network</td>\n",
              "      <td>Beginner</td>\n",
              "      <td>4.8</td>\n",
              "      <td>By the end of this guided project, you will be...</td>\n",
              "      <td>Finance  business plan  persona (user experien...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Silicon Thin Film Solar Cells</td>\n",
              "      <td>�cole Polytechnique</td>\n",
              "      <td>Advanced</td>\n",
              "      <td>4.1</td>\n",
              "      <td>This course consists of a general presentation...</td>\n",
              "      <td>chemistry  physics  Solar Energy  film  lambda...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Finance for Managers</td>\n",
              "      <td>IESE Business School</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>4.8</td>\n",
              "      <td>When it comes to numbers, there is always more...</td>\n",
              "      <td>accounts receivable  dupont analysis  analysis...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Retrieve Data using Single-Table SQL Queries</td>\n",
              "      <td>Coursera Project Network</td>\n",
              "      <td>Beginner</td>\n",
              "      <td>4.6</td>\n",
              "      <td>In this course you�ll learn how to effectively...</td>\n",
              "      <td>Data Analysis  select (sql)  database manageme...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "                                         Course Name  ...                                             Skills\n",
              "0  Write A Feature Length Screenplay For Film Or ...  ...  Drama  Comedy  peering  screenwriting  film  D...\n",
              "1  Business Strategy: Business Model Canvas Analy...  ...  Finance  business plan  persona (user experien...\n",
              "2                      Silicon Thin Film Solar Cells  ...  chemistry  physics  Solar Energy  film  lambda...\n",
              "3                               Finance for Managers  ...  accounts receivable  dupont analysis  analysis...\n",
              "4       Retrieve Data using Single-Table SQL Queries  ...  Data Analysis  select (sql)  database manageme...\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##4. Data Inferencing"
      ],
      "metadata": {
        "id": "jRa8uURzGmxF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "r,c = df.shape\n",
        "print(\"Number of customers  = \",r)\n",
        "print(\"Number of parameters = \",c)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hUr2wTIuCn0F",
        "outputId": "46f343e0-76e6-47d4-a23d-b243ad65e296"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of customers  =  3522\n",
            "Number of parameters =  6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mCW_8_QOCq08",
        "outputId": "35f8bef6-920d-4f1c-e00c-7cab6a98a7b3"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3522 entries, 0 to 3521\n",
            "Data columns (total 6 columns):\n",
            " #   Column              Non-Null Count  Dtype \n",
            "---  ------              --------------  ----- \n",
            " 0   Course Name         3522 non-null   object\n",
            " 1   University          3522 non-null   object\n",
            " 2   Difficulty Level    3522 non-null   object\n",
            " 3   Course Rating       3522 non-null   object\n",
            " 4   Course Description  3522 non-null   object\n",
            " 5   Skills              3522 non-null   object\n",
            "dtypes: object(6)\n",
            "memory usage: 165.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##5. NULL Removal / Checking"
      ],
      "metadata": {
        "id": "0bKSBNuRGp4a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Are there any missing values in the dataset ?\",df.isna().values.any())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aKnS_wnsCsM0",
        "outputId": "4d295932-384a-4170-b14f-1986c0deebec"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Are there any missing values in the dataset ? False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# complete summary of dataset\n",
        "df.describe().T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "ARsgJhEACuSl",
        "outputId": "49260a91-63ef-4bd5-d10a-f7568ff3f39a"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "    <div 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>count</th>\n",
              "      <th>unique</th>\n",
              "      <th>top</th>\n",
              "      <th>freq</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Course Name</th>\n",
              "      <td>3522</td>\n",
              "      <td>3416</td>\n",
              "      <td>Google Cloud Platform Fundamentals: Core Infra...</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>University</th>\n",
              "      <td>3522</td>\n",
              "      <td>184</td>\n",
              "      <td>Coursera Project Network</td>\n",
              "      <td>562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Difficulty Level</th>\n",
              "      <td>3522</td>\n",
              "      <td>5</td>\n",
              "      <td>Beginner</td>\n",
              "      <td>1444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Course Rating</th>\n",
              "      <td>3522</td>\n",
              "      <td>31</td>\n",
              "      <td>4.7</td>\n",
              "      <td>740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Course Description</th>\n",
              "      <td>3522</td>\n",
              "      <td>3397</td>\n",
              "      <td>This course introduces you to important concep...</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skills</th>\n",
              "      <td>3522</td>\n",
              "      <td>3424</td>\n",
              "      <td>Google Cloud Platform  Big Data  Cloud Infrast...</td>\n",
              "      <td>8</td>\n",
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              "  </tbody>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\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",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          const element = document.querySelector('#df-46781e73-e8f7-424f-845b-b58af8e6e191');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                   count  ...  freq\n",
              "Course Name         3522  ...     8\n",
              "University          3522  ...   562\n",
              "Difficulty Level    3522  ...  1444\n",
              "Course Rating       3522  ...   740\n",
              "Course Description  3522  ...     8\n",
              "Skills              3522  ...     8\n",
              "\n",
              "[6 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6. Data Visualization"
      ],
      "metadata": {
        "id": "GnwfIQEUGwAN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = df['Difficulty Level'].value_counts()\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bPZ7eIbXCwAv",
        "outputId": "ac59e691-d62d-48fa-8ec0-5dad099c6871"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Beginner          1444\n",
              "Advanced          1005\n",
              "Intermediate       837\n",
              "Conversant         186\n",
              "Not Calibrated      50\n",
              "Name: Difficulty Level, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mag = ['Beginner','Advanced','Intermediate','Conversant','Not Calibrated']\n",
        "  \n",
        "data = [1444,1005,837,186,50]\n",
        "explode = (0.1,0.0,0.0,0.0,0.0) \n",
        "  \n",
        "# Creating color parameters \n",
        "colors = (\"lightblue\",\"crimson\",\"yellow\",\"green\",\"violet\") \n",
        "  \n",
        "# Wedge properties \n",
        "wp = { 'linewidth' : 1, 'edgecolor' : \"white\" } \n",
        "  \n",
        "# Creating autocpt arguments \n",
        "def func(pct, allvalues): \n",
        "    absolute = int(pct / 100.*np.sum(allvalues)) \n",
        "    return \"{:.1f}%\\n({:d} g)\".format(pct, absolute) \n",
        "  \n",
        "# Creating plot \n",
        "fig, ax = plt.subplots(figsize =(15, 10)) \n",
        "wedges, texts, autotexts = ax.pie(data,  \n",
        "                                  autopct = lambda pct: func(pct, data), \n",
        "                                  explode = explode,  \n",
        "                                  labels = mag, \n",
        "                                  shadow = True, \n",
        "                                  colors = colors, \n",
        "                                  startangle = 90, \n",
        "                                  wedgeprops = wp, \n",
        "                                  textprops = dict(color =\"black\")) \n",
        "  \n",
        "# Adding legend \n",
        "ax.legend(wedges, mag, \n",
        "          title =\"Values\", \n",
        "          loc =\"center left\", \n",
        "          bbox_to_anchor =(1, 0, 0.5, 1)) \n",
        "  \n",
        "plt.setp(autotexts, size = 10, weight =\"bold\") \n",
        "ax.set_title(\"Payment type of course\\n\",size=19) \n",
        "  \n",
        "# show plot \n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 618
        },
        "id": "So4Ji9Y-CxXD",
        "outputId": "518adc92-177e-49d5-8703-c406a6500f11"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Course Rating'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EHyaapw4C0N3",
        "outputId": "f3538651-3ea9-4bc5-90f6-86b7bf2689e1"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.7               740\n",
              "4.6               623\n",
              "4.8               598\n",
              "4.5               389\n",
              "4.4               242\n",
              "4.9               180\n",
              "4.3               165\n",
              "4.2               121\n",
              "5                  90\n",
              "4.1                85\n",
              "Not Calibrated     82\n",
              "4                  51\n",
              "3.8                24\n",
              "3.9                20\n",
              "3.7                18\n",
              "3.6                18\n",
              "3.5                17\n",
              "3.4                13\n",
              "3                  12\n",
              "3.2                 9\n",
              "3.3                 6\n",
              "2.9                 6\n",
              "1                   2\n",
              "2.4                 2\n",
              "2.6                 2\n",
              "2.8                 2\n",
              "1.9                 1\n",
              "3.1                 1\n",
              "2.3                 1\n",
              "2.5                 1\n",
              "2                   1\n",
              "Name: Course Rating, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[df['Course Rating'] != 'Not Calibrated']\n",
        "df['Course Rating'] = df['Course Rating'].astype(float)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 763
        },
        "id": "6zHVHpUCD7g-",
        "outputId": "c024a4c7-1d78-4ffd-af3d-030c203cde04"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "  <div id=\"df-73bd06bf-ecb4-4ee2-bc57-28df6f024f41\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Course Name</th>\n",
              "      <th>University</th>\n",
              "      <th>Difficulty Level</th>\n",
              "      <th>Course Rating</th>\n",
              "      <th>Course Description</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Write A Feature Length Screenplay For Film Or ...</td>\n",
              "      <td>Michigan State University</td>\n",
              "      <td>Beginner</td>\n",
              "      <td>4.8</td>\n",
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              "      <td>Drama  Comedy  peering  screenwriting  film  D...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Business Strategy: Business Model Canvas Analy...</td>\n",
              "      <td>Coursera Project Network</td>\n",
              "      <td>Beginner</td>\n",
              "      <td>4.8</td>\n",
              "      <td>By the end of this guided project, you will be...</td>\n",
              "      <td>Finance  business plan  persona (user experien...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Silicon Thin Film Solar Cells</td>\n",
              "      <td>�cole Polytechnique</td>\n",
              "      <td>Advanced</td>\n",
              "      <td>4.1</td>\n",
              "      <td>This course consists of a general presentation...</td>\n",
              "      <td>chemistry  physics  Solar Energy  film  lambda...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Finance for Managers</td>\n",
              "      <td>IESE Business School</td>\n",
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              "      <td>4.8</td>\n",
              "      <td>When it comes to numbers, there is always more...</td>\n",
              "      <td>accounts receivable  dupont analysis  analysis...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Retrieve Data using Single-Table SQL Queries</td>\n",
              "      <td>Coursera Project Network</td>\n",
              "      <td>Beginner</td>\n",
              "      <td>4.6</td>\n",
              "      <td>In this course you�ll learn how to effectively...</td>\n",
              "      <td>Data Analysis  select (sql)  database manageme...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>3517</th>\n",
              "      <td>Capstone: Retrieving, Processing, and Visualiz...</td>\n",
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              "      <td>Beginner</td>\n",
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              "    <tr>\n",
              "      <th>3518</th>\n",
              "      <td>Patrick Henry: Forgotten Founder</td>\n",
              "      <td>University of Virginia</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>4.9</td>\n",
              "      <td>�Give me liberty, or give me death:� Rememberi...</td>\n",
              "      <td>retirement  Causality  career  history of the ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3519</th>\n",
              "      <td>Business intelligence and data analytics: Gene...</td>\n",
              "      <td>Macquarie University</td>\n",
              "      <td>Advanced</td>\n",
              "      <td>4.6</td>\n",
              "      <td>�Megatrends� heavily influence today�s organis...</td>\n",
              "      <td>analytics  tableau software  Business Intellig...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3520</th>\n",
              "      <td>Rigid Body Dynamics</td>\n",
              "      <td>Korea Advanced Institute of Science and Techno...</td>\n",
              "      <td>Beginner</td>\n",
              "      <td>4.6</td>\n",
              "      <td>This course teaches dynamics, one of the basic...</td>\n",
              "      <td>Angular  Mechanical Design  fluid mechanics  F...</td>\n",
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              "    <tr>\n",
              "      <th>3521</th>\n",
              "      <td>Architecting with Google Kubernetes Engine: Pr...</td>\n",
              "      <td>Google Cloud</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>4.7</td>\n",
              "      <td>In this course, \"Architecting with Google Kube...</td>\n",
              "      <td>bigtable  bigquery  SQL  Google Cloud Platform...</td>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            "text/plain": [
              "                                            Course Name  ...                                             Skills\n",
              "0     Write A Feature Length Screenplay For Film Or ...  ...  Drama  Comedy  peering  screenwriting  film  D...\n",
              "1     Business Strategy: Business Model Canvas Analy...  ...  Finance  business plan  persona (user experien...\n",
              "2                         Silicon Thin Film Solar Cells  ...  chemistry  physics  Solar Energy  film  lambda...\n",
              "3                                  Finance for Managers  ...  accounts receivable  dupont analysis  analysis...\n",
              "4          Retrieve Data using Single-Table SQL Queries  ...  Data Analysis  select (sql)  database manageme...\n",
              "...                                                 ...  ...                                                ...\n",
              "3517  Capstone: Retrieving, Processing, and Visualiz...  ...  Databases  syntax  analysis  web  Data Visuali...\n",
              "3518                   Patrick Henry: Forgotten Founder  ...  retirement  Causality  career  history of the ...\n",
              "3519  Business intelligence and data analytics: Gene...  ...  analytics  tableau software  Business Intellig...\n",
              "3520                                Rigid Body Dynamics  ...  Angular  Mechanical Design  fluid mechanics  F...\n",
              "3521  Architecting with Google Kubernetes Engine: Pr...  ...  bigtable  bigquery  SQL  Google Cloud Platform...\n",
              "\n",
              "[3440 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(18,7))\n",
        "sns.countplot(data=df,x='Course Rating',palette='plasma')\n",
        "plt.xlabel('Course Ratings',fontsize='16',color='blue')\n",
        "plt.ylabel('Number of courses',fontsize='16',color='blue')\n",
        "plt.xticks(fontsize='14',color='green')\n",
        "plt.yticks(fontsize='14',color='red')\n",
        "plt.title(\"Count of course types\\n\",fontsize=24,fontweight='bold',color='indigo')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "HWSxEeVoD9Wi",
        "outputId": "4e71f515-0849-4189-b636-eb3fdf035660"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Count of course types\\n')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##7. Filterning required data"
      ],
      "metadata": {
        "id": "PdfppS5YG3j3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = df[df['Course Rating'] > 4.0]\n",
        "df2.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "rHXEm5z2D_h7",
        "outputId": "23fd6353-0029-4971-ce68-a195e68e2b7f"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-817c4a55-548e-4d8c-975d-a67721fb87c5\">\n",
              "    <div 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",
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              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Course Name</th>\n",
              "      <th>University</th>\n",
              "      <th>Difficulty Level</th>\n",
              "      <th>Course Rating</th>\n",
              "      <th>Course Description</th>\n",
              "      <th>Skills</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Write A Feature Length Screenplay For Film Or ...</td>\n",
              "      <td>Michigan State University</td>\n",
              "      <td>Beginner</td>\n",
              "      <td>4.8</td>\n",
              "      <td>Write a Full Length Feature Film Script  In th...</td>\n",
              "      <td>Drama  Comedy  peering  screenwriting  film  D...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Business Strategy: Business Model Canvas Analy...</td>\n",
              "      <td>Coursera Project Network</td>\n",
              "      <td>Beginner</td>\n",
              "      <td>4.8</td>\n",
              "      <td>By the end of this guided project, you will be...</td>\n",
              "      <td>Finance  business plan  persona (user experien...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Silicon Thin Film Solar Cells</td>\n",
              "      <td>�cole Polytechnique</td>\n",
              "      <td>Advanced</td>\n",
              "      <td>4.1</td>\n",
              "      <td>This course consists of a general presentation...</td>\n",
              "      <td>chemistry  physics  Solar Energy  film  lambda...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Finance for Managers</td>\n",
              "      <td>IESE Business School</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>4.8</td>\n",
              "      <td>When it comes to numbers, there is always more...</td>\n",
              "      <td>accounts receivable  dupont analysis  analysis...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Retrieve Data using Single-Table SQL Queries</td>\n",
              "      <td>Coursera Project Network</td>\n",
              "      <td>Beginner</td>\n",
              "      <td>4.6</td>\n",
              "      <td>In this course you�ll learn how to effectively...</td>\n",
              "      <td>Data Analysis  select (sql)  database manageme...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Building Test Automation Framework using Selen...</td>\n",
              "      <td>Coursera Project Network</td>\n",
              "      <td>Beginner</td>\n",
              "      <td>4.7</td>\n",
              "      <td>Selenium is one of the most widely used functi...</td>\n",
              "      <td>maintenance  test case  test automation  scree...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Programming Languages, Part A</td>\n",
              "      <td>University of Washington</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>4.9</td>\n",
              "      <td>This course is an introduction to the basic co...</td>\n",
              "      <td>inference  ml (programming language)  higher-o...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>The Roles and Responsibilities of Nonprofit Bo...</td>\n",
              "      <td>The State University of New York</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>4.3</td>\n",
              "      <td>This course provides a more in-depth look at t...</td>\n",
              "      <td>Planning  Peer Review  fundraising  strategic ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Esports Management Capstone Project</td>\n",
              "      <td>University of California, Irvine</td>\n",
              "      <td>Beginner</td>\n",
              "      <td>4.4</td>\n",
              "      <td>We will analyze the pros and cons of creating ...</td>\n",
              "      <td>Average  Planning  Leadership and Management  ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Business Statistics and Analysis Capstone</td>\n",
              "      <td>Rice University</td>\n",
              "      <td>Beginner</td>\n",
              "      <td>4.7</td>\n",
              "      <td>The Business Statistics and Analysis Capstone ...</td>\n",
              "      <td>Statistical Analysis  Microsoft Excel  busines...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      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-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",
              "    [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-817c4a55-548e-4d8c-975d-a67721fb87c5 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-817c4a55-548e-4d8c-975d-a67721fb87c5');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                          Course Name  ...                                             Skills\n",
              "0   Write A Feature Length Screenplay For Film Or ...  ...  Drama  Comedy  peering  screenwriting  film  D...\n",
              "1   Business Strategy: Business Model Canvas Analy...  ...  Finance  business plan  persona (user experien...\n",
              "2                       Silicon Thin Film Solar Cells  ...  chemistry  physics  Solar Energy  film  lambda...\n",
              "3                                Finance for Managers  ...  accounts receivable  dupont analysis  analysis...\n",
              "4        Retrieve Data using Single-Table SQL Queries  ...  Data Analysis  select (sql)  database manageme...\n",
              "5   Building Test Automation Framework using Selen...  ...  maintenance  test case  test automation  scree...\n",
              "7                       Programming Languages, Part A  ...  inference  ml (programming language)  higher-o...\n",
              "8   The Roles and Responsibilities of Nonprofit Bo...  ...  Planning  Peer Review  fundraising  strategic ...\n",
              "11                Esports Management Capstone Project  ...  Average  Planning  Leadership and Management  ...\n",
              "13          Business Statistics and Analysis Capstone  ...  Statistical Analysis  Microsoft Excel  busines...\n",
              "\n",
              "[10 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##8. Making NLP Model for Recommendation Engine"
      ],
      "metadata": {
        "id": "Y2dMPCB_G6-U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cv=TfidfVectorizer()\n",
        "tfidf_matrix=cv.fit_transform(df['Course Name'])\n",
        "course_user = df.pivot_table(columns='Course Name',values='Course Rating')\n",
        "course_user.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "id": "hwx5aZ0RGKNV",
        "outputId": "a4a75944-efb6-4039-d08f-bb1dc14a5337"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-11da2af7-dcb7-4b60-ba26-ce7888a3c0f9\">\n",
              "    <div 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>Course Name</th>\n",
              "      <th>\"Making\" Progress Teach-Out</th>\n",
              "      <th>3-Axis Machining with Autodesk Fusion 360</th>\n",
              "      <th>3D Art and Audio Pipeline</th>\n",
              "      <th>3D CAD Application</th>\n",
              "      <th>3D CAD Fundamental</th>\n",
              "      <th>3D Data Visualization for Science Communication</th>\n",
              "      <th>3D Graphics in Android: Sensors and VR</th>\n",
              "      <th>3D Interaction Design in Virtual Reality</th>\n",
              "      <th>3D Interactions and Navigation</th>\n",
              "      <th>3D Model Creation with Autodesk Fusion 360</th>\n",
              "      <th>3D Models for Virtual Reality</th>\n",
              "      <th>3D Printing Applications</th>\n",
              "      <th>3D Printing Capstone</th>\n",
              "      <th>3D Printing Hardware</th>\n",
              "      <th>3D Printing Software</th>\n",
              "      <th>A Brief History of Human Spaceflight</th>\n",
              "      <th>A Business Approach to Sustainable Landscape Restoration</th>\n",
              "      <th>A Circular Economy of Metals: Towards a Sustainable Societal Metabolism</th>\n",
              "      <th>A Complete Reinforcement Learning System (Capstone)</th>\n",
              "      <th>A Crash Course in Data Science</th>\n",
              "      <th>A Geometrical Approach to Genome Analysis: Skew &amp; Z-Curve</th>\n",
              "      <th>A Guide to Healthcare Innovation: Principles and Practice</th>\n",
              "      <th>A Journey through Western Christianity: from Persecuted Faith to Global Religion (200 - 1650)</th>\n",
              "      <th>A Law Student's Toolkit</th>\n",
              "      <th>A Life of Happiness and Fulfillment</th>\n",
              "      <th>A Scientific Approach to Innovation Management</th>\n",
              "      <th>A Start Guide: Product Marketing Using G Suite</th>\n",
              "      <th>A Voice of Their Own. Women's Spirituality in the Middle Ages.</th>\n",
              "      <th>A tecnologia da produ��o musical</th>\n",
              "      <th>ADHD: Everyday Strategies for Elementary Students</th>\n",
              "      <th>AI Capstone Project with Deep Learning</th>\n",
              "      <th>AI For Everyone</th>\n",
              "      <th>AI For Medical Treatment</th>\n",
              "      <th>AI Workflow: AI in Production</th>\n",
              "      <th>AI Workflow: Business Priorities and Data Ingestion</th>\n",
              "      <th>AI Workflow: Data Analysis and Hypothesis Testing</th>\n",
              "      <th>AI Workflow: Enterprise Model Deployment</th>\n",
              "      <th>AI Workflow: Feature Engineering and Bias Detection</th>\n",
              "      <th>AI Workflow: Machine Learning, Visual Recognition and NLP</th>\n",
              "      <th>AI for Medical Diagnosis</th>\n",
              "      <th>...</th>\n",
              "      <th>When Disaster Meets Conflict</th>\n",
              "      <th>Whole genome sequencing of bacterial genomes - tools and applications</th>\n",
              "      <th>Why Iowa? A Primer on Primaries and Caucuses Teach-Out</th>\n",
              "      <th>William Shakespeare's Twelfth Night: Comedy, Conflict, and Community</th>\n",
              "      <th>Wind Energy</th>\n",
              "      <th>Wind resources for renewable energies</th>\n",
              "      <th>Windows Server Management and Security</th>\n",
              "      <th>Wine Tasting: Sensory Techniques for Wine Analysis</th>\n",
              "      <th>Wireless Communications for Everybody</th>\n",
              "      <th>Wireshark for Basic Network Security Analysis</th>\n",
              "      <th>Women in Leadership: Inspiring Positive Change</th>\n",
              "      <th>Women in environmental biology</th>\n",
              "      <th>Wood Science: Beyond Building</th>\n",
              "      <th>Words Spun Out of Images: Visual and Literary Culture in Nineteenth Century Japan</th>\n",
              "      <th>Work Smarter, Not Harder: Time Management for Personal &amp; Professional Productivity</th>\n",
              "      <th>World Design for Video Games</th>\n",
              "      <th>Write A Feature Length Screenplay For Film Or Television</th>\n",
              "      <th>Write Like Mozart: An Introduction to Classical Music Composition</th>\n",
              "      <th>Write Professional Emails in English</th>\n",
              "      <th>Write Your First Novel</th>\n",
              "      <th>Writing Professional Email and Memos (Project-Centered Course)</th>\n",
              "      <th>Writing Skills for Engineering Leaders</th>\n",
              "      <th>Writing Stories About Ourselves</th>\n",
              "      <th>Writing Winning Resumes and Cover Letters</th>\n",
              "      <th>Writing a Personal Essay</th>\n",
              "      <th>Writing and Editing: Drafting</th>\n",
              "      <th>Writing and Editing: Revising</th>\n",
              "      <th>Writing and Editing: Structure and Organization</th>\n",
              "      <th>Writing and Editing: Word Choice and Word Order</th>\n",
              "      <th>Writing for Young Readers: Opening the Treasure Chest</th>\n",
              "      <th>Writing in English at University</th>\n",
              "      <th>Writing in First Person Point of View</th>\n",
              "      <th>Writing your World: Finding yourself in the academic space</th>\n",
              "      <th>e-Learning Ecologies: Innovative Approaches to Teaching and Learning for the Digital Age</th>\n",
              "      <th>eHealth: More than just an electronic record</th>\n",
              "      <th>iOS App Development Basics</th>\n",
              "      <th>iOS App Store &amp; In-App Purchases</th>\n",
              "      <th>iOS Project: Transreality Game</th>\n",
              "      <th>iPad Music Player in XD</th>\n",
              "      <th>idea 2 IMPACT: An Introduction to Translating Assistive Health Technologies and Other Products</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Course Rating</th>\n",
              "      <td>4.2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>4.3</td>\n",
              "      <td>4.9</td>\n",
              "      <td>4.6</td>\n",
              "      <td>4.3</td>\n",
              "      <td>4.6</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4.7</td>\n",
              "      <td>4.6</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4.2</td>\n",
              "      <td>3.9</td>\n",
              "      <td>4.6</td>\n",
              "      <td>4.2</td>\n",
              "      <td>4.7</td>\n",
              "      <td>4.7</td>\n",
              "      <td>4.6</td>\n",
              "      <td>4.4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.6</td>\n",
              "      <td>4.7</td>\n",
              "      <td>4.6</td>\n",
              "      <td>4.7</td>\n",
              "      <td>4.4</td>\n",
              "      <td>4.7</td>\n",
              "      <td>4.4</td>\n",
              "      <td>4.8</td>\n",
              "      <td>4.7</td>\n",
              "      <td>4.2</td>\n",
              "      <td>4.8</td>\n",
              "      <td>4.6</td>\n",
              "      <td>4.2</td>\n",
              "      <td>4.1</td>\n",
              "      <td>4.2</td>\n",
              "      <td>4.2</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4.6</td>\n",
              "      <td>4.5</td>\n",
              "      <td>...</td>\n",
              "      <td>4.8</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4.4</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3.7</td>\n",
              "      <td>4.6</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4.7</td>\n",
              "      <td>4.4</td>\n",
              "      <td>4.7</td>\n",
              "      <td>4.6</td>\n",
              "      <td>4.6</td>\n",
              "      <td>4.8</td>\n",
              "      <td>4.4</td>\n",
              "      <td>4.3</td>\n",
              "      <td>4.8</td>\n",
              "      <td>4.9</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3.5</td>\n",
              "      <td>4.2</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3.7</td>\n",
              "      <td>4.7</td>\n",
              "      <td>4.4</td>\n",
              "      <td>4.8</td>\n",
              "      <td>4.8</td>\n",
              "      <td>4.6</td>\n",
              "      <td>4.6</td>\n",
              "      <td>4.6</td>\n",
              "      <td>4.7</td>\n",
              "      <td>4.6</td>\n",
              "      <td>4.7</td>\n",
              "      <td>4.2</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4.2</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4.9</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 3334 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-11da2af7-dcb7-4b60-ba26-ce7888a3c0f9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      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-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",
              "    [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-11da2af7-dcb7-4b60-ba26-ce7888a3c0f9 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-11da2af7-dcb7-4b60-ba26-ce7888a3c0f9');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "Course Name    \"Making\" Progress Teach-Out  ...  idea 2 IMPACT: An Introduction to Translating Assistive Health Technologies and Other Products\n",
              "Course Rating                          4.2  ...                                                1.0                                             \n",
              "\n",
              "[1 rows x 3334 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.rename(columns={'Course Name':'course_title'})"
      ],
      "metadata": {
        "id": "_D3zvPiOGMkM"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##9. Running Recommendation Engine on variety of course genres"
      ],
      "metadata": {
        "id": "hQ2qr5fNG_XT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)\n",
        "indices=pd.Series(df.index,index=df['course_title'])\n",
        "titles=df['course_title']\n",
        "def recommendations(course_title):\n",
        "    idx = indices[course_title]\n",
        "    sim_scores = list(enumerate(cosine_sim[idx]))\n",
        "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
        "    sim_scores = sim_scores[1:11]\n",
        "    course_indices = [i[0] for i in sim_scores]\n",
        "    return titles.iloc[course_indices]\n",
        "print(\"--------------- Similar courses to your search --------------:\\n\")\n",
        "recommendations('Write A Feature Length Screenplay For Film Or Television')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aNpWsguCGOvQ",
        "outputId": "e443c5e2-bbcf-4720-c63a-f8c04ded0899"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------- Similar courses to your search --------------:\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "149                      Scandinavian Film and Television\n",
              "2485                                  Feature Engineering\n",
              "1481    Script Writing: Write a Pilot Episode for a TV...\n",
              "2858         Machine Learning Feature Selection in Python\n",
              "3070            Perform Feature Analysis with Yellowbrick\n",
              "1854    Data Processing and Feature Engineering with M...\n",
              "1629                               Write Your First Novel\n",
              "650                  Write Professional Emails in English\n",
              "2385                     Getting Your Film off the Ground\n",
              "2472    Python: Imputations, Feature Creation & Statis...\n",
              "Name: course_title, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)\n",
        "indices=pd.Series(df.index,index=df['course_title'])\n",
        "titles=df['course_title']\n",
        "def recommendations(course_title):\n",
        "    idx = indices[course_title]\n",
        "    sim_scores = list(enumerate(cosine_sim[idx]))\n",
        "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
        "    sim_scores = sim_scores[1:11]\n",
        "    course_indices = [i[0] for i in sim_scores]\n",
        "    return titles.iloc[course_indices]\n",
        "print(\"--------------- Similar courses to your search --------------:\\n\")\n",
        "recommendations('Retrieve Data using Single-Table SQL Queries')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_XAZgsKwGQvq",
        "outputId": "14829fcd-c0d1-4af2-a0d5-3d368e9dfeb5"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------- Similar courses to your search --------------:\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3272        Retrieve Data with Multiple-Table SQL Queries\n",
              "1162       Advanced SQL Retrieval Queries in SQLiteStudio\n",
              "1580                                 SQL for Data Science\n",
              "3482                SQL for Data Science Capstone Project\n",
              "2892                           Manipulating Data with SQL\n",
              "2611                          Analyzing Big Data with SQL\n",
              "2934    How to Design a Space-Saving Table Using SketchUp\n",
              "1033                   Databases and SQL for Data Science\n",
              "1379                   Databases and SQL for Data Science\n",
              "1380                   Databases and SQL for Data Science\n",
              "Name: course_title, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)\n",
        "indices=pd.Series(df.index,index=df['course_title'])\n",
        "titles=df['course_title']\n",
        "def recommendations(course_title):\n",
        "    idx = indices[course_title]\n",
        "    sim_scores = list(enumerate(cosine_sim[idx]))\n",
        "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
        "    sim_scores = sim_scores[1:11]\n",
        "    course_indices = [i[0] for i in sim_scores]\n",
        "    return titles.iloc[course_indices]\n",
        "print(\"--------------- Similar courses to your search --------------:\\n\")\n",
        "recommendations('Finance for Managers')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C9sG8YN0GTEO",
        "outputId": "a7caf6a5-9e1d-41a8-be8e-815b4715e05d"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------- Similar courses to your search --------------:\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "419                    Finance for Non-Financial Managers\n",
              "2082                Finance for Non-Finance Professionals\n",
              "3448                                 Finance for Startups\n",
              "3094    Coding for Designers, Managers, & Entrepreneurs I\n",
              "653     Coding for Designers, Managers, & Entrepreneur...\n",
              "1659    Coding for Designers, Managers, & Entrepreneur...\n",
              "2851                        Finance for Everyone: Markets\n",
              "3110                          Finance For Everyone: Value\n",
              "959                 Finance for Everyone Capstone Project\n",
              "2188    Human Resources Management Capstone: HR for Pe...\n",
              "Name: course_title, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "nuy3uvwNGVJ9"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}