Data_Science_Processes_Explained_Step_by_Step_FreeBirds_Crew.ipynb

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      "name": "Data Science Processes Explained Step by Step - FreeBirds Crew",
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      "name": "python3",
      "display_name": "Python 3"
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      "cell_type": "markdown",
      "metadata": {
        "id": "qnGx8WDsVGZ5",
        "colab_type": "text"
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      "source": [
        "# **FreeBirds Crew**\n",
        "---\n",
        "We Learn and Grow Together\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=19mbO0yQ6U4jU_RU5FNTqVoT0QYX0yKIi\" alt=\"drawing\" width=\"500\"/>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5gqYYJCVXcIA",
        "colab_type": "text"
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      "source": [
        "## Data Science Processes Explained Step by Step\n",
        "1. Business Understanding\n",
        "2. Analytic Approach\n",
        "3. Data Mining or Extraction of Data\n",
        "4. Data Cleaning\n",
        "5. Data Exploration\n",
        "6. Feature Engineering\n",
        "7. Predictive Modeling\n",
        "8. Data Visualization\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C1aLkVZ5cyUs",
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      "source": [
        "### **Business Understanding**\n",
        "Every Project starts with the Business Understanding that involves -\n",
        "1. Objective of Project?\n",
        "2. Sponsership\n",
        "3. Goal of the Project?\n",
        "4. What is our Success Meausres and How do we Define those.\n",
        "\n",
        "Example - Trafic Jam or House Price Prediction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z1lm_mrchWf-",
        "colab_type": "text"
      },
      "source": [
        "### **Analytical Approach**\n",
        "Describe Problem Statement in Form of Statistical and Machine Learning Algorithms - \n",
        "\n",
        "1. **Regression**:\t\n",
        "• “Predicting Sales,Revenues and Prices in Future?”\t\n",
        "2. **Classification**:\t\n",
        "• “Prediction on the Based of Catgories?”\n",
        "3. **Clustering**:\t\n",
        "• “Grouping Similar things Together on the based of Smiliarities, Behaviour and many more things?”\n",
        "4. **Recommendation/Personalization**:\t\n",
        "• “You should also buy this, because someone who buy this, buy that also?”\n",
        "5. **Outlier\tDetection**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h6_kq1qPi9Ca",
        "colab_type": "text"
      },
      "source": [
        "### **Data Mining or Extraction of Data**\n",
        "Sources to get Data - \n",
        "1. [Kaggle](https://www.kaggle.com/datasets)\n",
        "2. [Amazon Datasets](https://registry.opendata.aws/)\n",
        "3. [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php)\n",
        "4. [Google’s Datasets Search Engine](https://datasetsearch.research.google.com/)\n",
        "5. [Microsoft Datasets](https://msropendata.com/)\n",
        "6. [Awesome Public Datasets Collection](https://github.com/awesomedata/awesome-public-datasets)\n",
        "7. [Government Datasets](https://data.gov.in/)\n",
        "8. [Computer Vision Datasets](https://www.visualdata.io/)\n",
        "9. [Lionbridge AI Datasets](https://lionbridge.ai/datasets/)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NZL2jwnRWuXS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Basic Libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M6FXesrAm5Hp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Flickr_Image_Books_Dataset\n",
        "books = pd.read_csv(\"https://raw.githubusercontent.com/realpython/python-data-cleaning/master/Datasets/BL-Flickr-Images-Book.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
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        "id": "52Fby4f5nDG_",
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          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "c5cb9781-c665-46a9-a6ae-101997bc2020"
      },
      "source": [
        "books.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Identifier</th>\n",
              "      <th>Edition Statement</th>\n",
              "      <th>Place of Publication</th>\n",
              "      <th>Date of Publication</th>\n",
              "      <th>Publisher</th>\n",
              "      <th>Title</th>\n",
              "      <th>Author</th>\n",
              "      <th>Contributors</th>\n",
              "      <th>Corporate Author</th>\n",
              "      <th>Corporate Contributors</th>\n",
              "      <th>Former owner</th>\n",
              "      <th>Engraver</th>\n",
              "      <th>Issuance type</th>\n",
              "      <th>Flickr URL</th>\n",
              "      <th>Shelfmarks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>206</td>\n",
              "      <td>NaN</td>\n",
              "      <td>London</td>\n",
              "      <td>1879 [1878]</td>\n",
              "      <td>S. Tinsley &amp; Co.</td>\n",
              "      <td>Walter Forbes. [A novel.] By A. A</td>\n",
              "      <td>A. A.</td>\n",
              "      <td>FORBES, Walter.</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>monographic</td>\n",
              "      <td>http://www.flickr.com/photos/britishlibrary/ta...</td>\n",
              "      <td>British Library HMNTS 12641.b.30.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>216</td>\n",
              "      <td>NaN</td>\n",
              "      <td>London; Virtue &amp; Yorston</td>\n",
              "      <td>1868</td>\n",
              "      <td>Virtue &amp; Co.</td>\n",
              "      <td>All for Greed. [A novel. The dedication signed...</td>\n",
              "      <td>A., A. A.</td>\n",
              "      <td>BLAZE DE BURY, Marie Pauline Rose - Baroness</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>monographic</td>\n",
              "      <td>http://www.flickr.com/photos/britishlibrary/ta...</td>\n",
              "      <td>British Library HMNTS 12626.cc.2.</td>\n",
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              "      <th>2</th>\n",
              "      <td>218</td>\n",
              "      <td>NaN</td>\n",
              "      <td>London</td>\n",
              "      <td>1869</td>\n",
              "      <td>Bradbury, Evans &amp; Co.</td>\n",
              "      <td>Love the Avenger. By the author of “All for Gr...</td>\n",
              "      <td>A., A. A.</td>\n",
              "      <td>BLAZE DE BURY, Marie Pauline Rose - Baroness</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>monographic</td>\n",
              "      <td>http://www.flickr.com/photos/britishlibrary/ta...</td>\n",
              "      <td>British Library HMNTS 12625.dd.1.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>472</td>\n",
              "      <td>NaN</td>\n",
              "      <td>London</td>\n",
              "      <td>1851</td>\n",
              "      <td>James Darling</td>\n",
              "      <td>Welsh Sketches, chiefly ecclesiastical, to the...</td>\n",
              "      <td>A., E. S.</td>\n",
              "      <td>Appleyard, Ernest Silvanus.</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>monographic</td>\n",
              "      <td>http://www.flickr.com/photos/britishlibrary/ta...</td>\n",
              "      <td>British Library HMNTS 10369.bbb.15.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>480</td>\n",
              "      <td>A new edition, revised, etc.</td>\n",
              "      <td>London</td>\n",
              "      <td>1857</td>\n",
              "      <td>Wertheim &amp; Macintosh</td>\n",
              "      <td>[The World in which I live, and my place in it...</td>\n",
              "      <td>A., E. S.</td>\n",
              "      <td>BROOME, John Henry.</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>monographic</td>\n",
              "      <td>http://www.flickr.com/photos/britishlibrary/ta...</td>\n",
              "      <td>British Library HMNTS 9007.d.28.</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "   Identifier  ...                           Shelfmarks\n",
              "0         206  ...    British Library HMNTS 12641.b.30.\n",
              "1         216  ...    British Library HMNTS 12626.cc.2.\n",
              "2         218  ...    British Library HMNTS 12625.dd.1.\n",
              "3         472  ...  British Library HMNTS 10369.bbb.15.\n",
              "4         480  ...     British Library HMNTS 9007.d.28.\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 31
        }
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      "cell_type": "code",
      "metadata": {
        "id": "kyBIzOeHyJLl",
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          "height": 297
        },
        "outputId": "d57277c8-ea82-427c-dc35-046b17c9490a"
      },
      "source": [
        "books.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Identifier</th>\n",
              "      <th>Corporate Author</th>\n",
              "      <th>Corporate Contributors</th>\n",
              "      <th>Engraver</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>8.287000e+03</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
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              "      <th>mean</th>\n",
              "      <td>2.017344e+06</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.190379e+06</td>\n",
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              "      <th>min</th>\n",
              "      <td>2.060000e+02</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>9.157875e+05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.043707e+06</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>75%</th>\n",
              "      <td>3.047430e+06</td>\n",
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              "      <th>max</th>\n",
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            "text/plain": [
              "         Identifier  Corporate Author  Corporate Contributors  Engraver\n",
              "count  8.287000e+03               0.0                     0.0       0.0\n",
              "mean   2.017344e+06               NaN                     NaN       NaN\n",
              "std    1.190379e+06               NaN                     NaN       NaN\n",
              "min    2.060000e+02               NaN                     NaN       NaN\n",
              "25%    9.157875e+05               NaN                     NaN       NaN\n",
              "50%    2.043707e+06               NaN                     NaN       NaN\n",
              "75%    3.047430e+06               NaN                     NaN       NaN\n",
              "max    4.160339e+06               NaN                     NaN       NaN"
            ]
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          "metadata": {
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          "execution_count": 32
        }
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0igwOxjF6mY5",
        "colab_type": "text"
      },
      "source": [
        "### **Data Cleaning and Data Exploration**\n",
        "• Data Cleaning is part of Data Preparation that Contains activities for construction\tand\tclean\tthe\tdata\tset -\n",
        "\n",
        "1. Data\tcleaning\n",
        "2. Handeling Missing\tor invalid row or column values\n",
        "3. Eliminating\tthe duplicates\n",
        "4. Formatting\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J0VHWHcgo1yj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "to_drop = ['Edition Statement','Corporate Author','Corporate Contributors','Former owner','Engraver','Contributors','Issuance type','Shelfmarks']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cZcUKpXnpBhr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Drop the Unwanted Columns\n",
        "books.drop(to_drop, inplace=True, axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VAfm3iL8pODK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "25d5c58e-fdbb-4420-ca7a-18023a77e8df"
      },
      "source": [
        "books.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Identifier</th>\n",
              "      <th>Place of Publication</th>\n",
              "      <th>Date of Publication</th>\n",
              "      <th>Publisher</th>\n",
              "      <th>Title</th>\n",
              "      <th>Author</th>\n",
              "      <th>Flickr URL</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>206</td>\n",
              "      <td>London</td>\n",
              "      <td>1879 [1878]</td>\n",
              "      <td>S. Tinsley &amp; Co.</td>\n",
              "      <td>Walter Forbes. [A novel.] By A. A</td>\n",
              "      <td>A. A.</td>\n",
              "      <td>http://www.flickr.com/photos/britishlibrary/ta...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>216</td>\n",
              "      <td>London; Virtue &amp; Yorston</td>\n",
              "      <td>1868</td>\n",
              "      <td>Virtue &amp; Co.</td>\n",
              "      <td>All for Greed. [A novel. The dedication signed...</td>\n",
              "      <td>A., A. A.</td>\n",
              "      <td>http://www.flickr.com/photos/britishlibrary/ta...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>218</td>\n",
              "      <td>London</td>\n",
              "      <td>1869</td>\n",
              "      <td>Bradbury, Evans &amp; Co.</td>\n",
              "      <td>Love the Avenger. By the author of “All for Gr...</td>\n",
              "      <td>A., A. A.</td>\n",
              "      <td>http://www.flickr.com/photos/britishlibrary/ta...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>472</td>\n",
              "      <td>London</td>\n",
              "      <td>1851</td>\n",
              "      <td>James Darling</td>\n",
              "      <td>Welsh Sketches, chiefly ecclesiastical, to the...</td>\n",
              "      <td>A., E. S.</td>\n",
              "      <td>http://www.flickr.com/photos/britishlibrary/ta...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>480</td>\n",
              "      <td>London</td>\n",
              "      <td>1857</td>\n",
              "      <td>Wertheim &amp; Macintosh</td>\n",
              "      <td>[The World in which I live, and my place in it...</td>\n",
              "      <td>A., E. S.</td>\n",
              "      <td>http://www.flickr.com/photos/britishlibrary/ta...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Identifier  ...                                         Flickr URL\n",
              "0         206  ...  http://www.flickr.com/photos/britishlibrary/ta...\n",
              "1         216  ...  http://www.flickr.com/photos/britishlibrary/ta...\n",
              "2         218  ...  http://www.flickr.com/photos/britishlibrary/ta...\n",
              "3         472  ...  http://www.flickr.com/photos/britishlibrary/ta...\n",
              "4         480  ...  http://www.flickr.com/photos/britishlibrary/ta...\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2xdd5qhPq0dG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "76fb6b9b-f10a-41fe-94ee-c99c274dc778"
      },
      "source": [
        "#Check Identifier is Unique if True then Set this as Index.\n",
        "books['Identifier'].is_unique"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TmLRUQdVrEGN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "32b88999-bfe9-4269-85ba-cecea9725a66"
      },
      "source": [
        "books = books.set_index('Identifier')\n",
        "books.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Place of Publication</th>\n",
              "      <th>Date of Publication</th>\n",
              "      <th>Publisher</th>\n",
              "      <th>Title</th>\n",
              "      <th>Author</th>\n",
              "      <th>Flickr URL</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Identifier</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>206</th>\n",
              "      <td>London</td>\n",
              "      <td>1879 [1878]</td>\n",
              "      <td>S. Tinsley &amp; Co.</td>\n",
              "      <td>Walter Forbes. [A novel.] By A. A</td>\n",
              "      <td>A. A.</td>\n",
              "      <td>http://www.flickr.com/photos/britishlibrary/ta...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>216</th>\n",
              "      <td>London; Virtue &amp; Yorston</td>\n",
              "      <td>1868</td>\n",
              "      <td>Virtue &amp; Co.</td>\n",
              "      <td>All for Greed. [A novel. The dedication signed...</td>\n",
              "      <td>A., A. A.</td>\n",
              "      <td>http://www.flickr.com/photos/britishlibrary/ta...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>218</th>\n",
              "      <td>London</td>\n",
              "      <td>1869</td>\n",
              "      <td>Bradbury, Evans &amp; Co.</td>\n",
              "      <td>Love the Avenger. By the author of “All for Gr...</td>\n",
              "      <td>A., A. A.</td>\n",
              "      <td>http://www.flickr.com/photos/britishlibrary/ta...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>472</th>\n",
              "      <td>London</td>\n",
              "      <td>1851</td>\n",
              "      <td>James Darling</td>\n",
              "      <td>Welsh Sketches, chiefly ecclesiastical, to the...</td>\n",
              "      <td>A., E. S.</td>\n",
              "      <td>http://www.flickr.com/photos/britishlibrary/ta...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>480</th>\n",
              "      <td>London</td>\n",
              "      <td>1857</td>\n",
              "      <td>Wertheim &amp; Macintosh</td>\n",
              "      <td>[The World in which I live, and my place in it...</td>\n",
              "      <td>A., E. S.</td>\n",
              "      <td>http://www.flickr.com/photos/britishlibrary/ta...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Place of Publication  ...                                         Flickr URL\n",
              "Identifier                            ...                                                   \n",
              "206                           London  ...  http://www.flickr.com/photos/britishlibrary/ta...\n",
              "216         London; Virtue & Yorston  ...  http://www.flickr.com/photos/britishlibrary/ta...\n",
              "218                           London  ...  http://www.flickr.com/photos/britishlibrary/ta...\n",
              "472                           London  ...  http://www.flickr.com/photos/britishlibrary/ta...\n",
              "480                           London  ...  http://www.flickr.com/photos/britishlibrary/ta...\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mkwePw8jraUB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "ccd42566-8fa7-4d16-ea5b-af92b01635b7"
      },
      "source": [
        "books.loc[206]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Place of Publication                                               London\n",
              "Date of Publication                                           1879 [1878]\n",
              "Publisher                                                S. Tinsley & Co.\n",
              "Title                                   Walter Forbes. [A novel.] By A. A\n",
              "Author                                                              A. A.\n",
              "Flickr URL              http://www.flickr.com/photos/britishlibrary/ta...\n",
              "Name: 206, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5mRPIUaSrp73",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "381741e6-42a0-4cbf-f3e3-c16b296b5120"
      },
      "source": [
        "#Check DataTypes of the Columns\n",
        "books.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Place of Publication    object\n",
              "Date of Publication     object\n",
              "Publisher               object\n",
              "Title                   object\n",
              "Author                  object\n",
              "Flickr URL              object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ad_19ebtsrBP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "89b23726-6b67-47b4-adb1-f681ec6f1dba"
      },
      "source": [
        "#Check a Publications \n",
        "books.loc[1905:, 'Date of Publication'].head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Identifier\n",
              "1905           1888\n",
              "1929    1839, 38-54\n",
              "2836           1897\n",
              "2854           1865\n",
              "2956        1860-63\n",
              "2957           1873\n",
              "3017           1866\n",
              "3131           1899\n",
              "4598           1814\n",
              "4884           1820\n",
              "Name: Date of Publication, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uYQqnne1tGq5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Make \"Data of Publications\" as float Data Type\n",
        "exact_data = books['Date of Publication'].str.extract(r'^(\\d{4})', expand=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M8rt6SaatOB6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "bef55baf-47a3-4b3a-f55d-5cd6c92fff64"
      },
      "source": [
        "exact_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Identifier\n",
              "206    1879\n",
              "216    1868\n",
              "218    1869\n",
              "472    1851\n",
              "480    1857\n",
              "Name: Date of Publication, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aTXHlynetYpC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "95d5c128-2905-4244-86a1-e70cac74d392"
      },
      "source": [
        "#Add Numeric Data into the DataFrame\n",
        "books['Date of Publication'] = pd.to_numeric(exact_data)\n",
        "books.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Place of Publication     object\n",
              "Date of Publication     float64\n",
              "Publisher                object\n",
              "Title                    object\n",
              "Author                   object\n",
              "Flickr URL               object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T2YyzMkqw9Dh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637
        },
        "outputId": "258c9896-648c-436a-9333-3272d7e9ee33"
      },
      "source": [
        "#Now Clean the NaN Values of that Column\n",
        "books[books['Date of Publication'].isnull()]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Place of Publication</th>\n",
              "      <th>Date of Publication</th>\n",
              "      <th>Publisher</th>\n",
              "      <th>Title</th>\n",
              "      <th>Author</th>\n",
              "      <th>Flickr URL</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Identifier</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>667</th>\n",
              "      <td>pp. 40. G. Bryan &amp; Co: Oxford, 1898</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The Coming of Spring, and other poems. By J. A...</td>\n",
              "      <td>A., J.|A., J.</td>\n",
              "      <td>http://www.flickr.com/photos/britishlibrary/ta...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5385</th>\n",
              "      <td>London</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Bradbury, Agnew &amp; Co.</td>\n",
              "      <td>[The comic history of England ... With twenty ...</td>\n",
              "      <td>A'BECKETT, Gilbert Abbott.</td>\n",
              "      <td>http://www.flickr.com/photos/britishlibrary/ta...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5389</th>\n",
              "      <td>London</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Bradbury, Agnew &amp; Co.</td>\n",
              "      <td>[The Comic History of Rome ... Illustrated by ...</td>\n",
              "      <td>A'BECKETT, Gilbert Abbott.</td>\n",
              "      <td>http://www.flickr.com/photos/britishlibrary/ta...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11361</th>\n",
              "      <td>Leipzig</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tirol und Vorarlberg ... Mit ... einer Karte, ...</td>\n",
              "      <td>ACHLEITNER, Arthur - and UBL (E.)</td>\n",
              "      <td>http://www.flickr.com/photos/britishlibrary/ta...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11852</th>\n",
              "      <td>pp. 40. W. Cann: Plymouth, [1876?]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>A Question of Holy Writ, suggested by a lectur...</td>\n",
              "      <td>ACKLAND - Lieut., R.N</td>\n",
              "      <td>http://www.flickr.com/photos/britishlibrary/ta...</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4113816</th>\n",
              "      <td>pp. xxvi, 375. London: Jackson, Walford &amp; Hodd...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The lacemakers: sketches of Irish characters w...</td>\n",
              "      <td>MEREDITH, Susanna.|WIBERG, Carl Fredrik.</td>\n",
              "      <td>http://www.flickr.com/photos/britishlibrary/ta...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4114334</th>\n",
              "      <td>pp. viii. 64. J. Debrett: London, 1789</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The Sword of Peace; or, a voyage of love; a co...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>http://www.flickr.com/photos/britishlibrary/ta...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4114889</th>\n",
              "      <td>London</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The 5 Alls. A collection of stories, charades,...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>http://www.flickr.com/photos/britishlibrary/ta...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4115138</th>\n",
              "      <td>G. Eld: London, 1608</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The Revenger's Tagaedie. As it hath beene sund...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>http://www.flickr.com/photos/britishlibrary/ta...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4159563</th>\n",
              "      <td>London</td>\n",
              "      <td>NaN</td>\n",
              "      <td>T. Cadell and W. Davies</td>\n",
              "      <td>Magna Britannia; being a concise topographical...</td>\n",
              "      <td>LYSONS, Daniel - M.A., F.R.S., and LYSONS (Sam...</td>\n",
              "      <td>http://www.flickr.com/photos/britishlibrary/ta...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>971 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         Place of Publication  ...                                         Flickr URL\n",
              "Identifier                                                     ...                                                   \n",
              "667                       pp. 40. G. Bryan & Co: Oxford, 1898  ...  http://www.flickr.com/photos/britishlibrary/ta...\n",
              "5385                                                   London  ...  http://www.flickr.com/photos/britishlibrary/ta...\n",
              "5389                                                   London  ...  http://www.flickr.com/photos/britishlibrary/ta...\n",
              "11361                                                 Leipzig  ...  http://www.flickr.com/photos/britishlibrary/ta...\n",
              "11852                      pp. 40. W. Cann: Plymouth, [1876?]  ...  http://www.flickr.com/photos/britishlibrary/ta...\n",
              "...                                                       ...  ...                                                ...\n",
              "4113816     pp. xxvi, 375. London: Jackson, Walford & Hodd...  ...  http://www.flickr.com/photos/britishlibrary/ta...\n",
              "4114334                pp. viii. 64. J. Debrett: London, 1789  ...  http://www.flickr.com/photos/britishlibrary/ta...\n",
              "4114889                                                London  ...  http://www.flickr.com/photos/britishlibrary/ta...\n",
              "4115138                                  G. Eld: London, 1608  ...  http://www.flickr.com/photos/britishlibrary/ta...\n",
              "4159563                                                London  ...  http://www.flickr.com/photos/britishlibrary/ta...\n",
              "\n",
              "[971 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IqffxiP1tYue",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "books = books.dropna()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4z29LgTUxCBc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "460cc71f-36ae-4b46-b06d-98836d3fa05c"
      },
      "source": [
        "books[books['Date of Publication'].isnull()]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .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>Place of Publication</th>\n",
              "      <th>Date of Publication</th>\n",
              "      <th>Publisher</th>\n",
              "      <th>Title</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>Identifier</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Place of Publication, Date of Publication, Publisher, Title, Author, Flickr URL]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wIff8NQUnIKB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "4#Use of Imputer Method to Clean the Data \n",
        "data = pd.read_csv('/content/drive/My Drive/Colab Notebooks/sample.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wUlFjuyzoJeJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
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        "outputId": "5586674b-881e-4de0-fd09-508deedaf3cd"
      },
      "source": [
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Country</th>\n",
              "      <th>Age</th>\n",
              "      <th>Salary</th>\n",
              "      <th>Purchased</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>France</td>\n",
              "      <td>44.0</td>\n",
              "      <td>72000.0</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Spain</td>\n",
              "      <td>27.0</td>\n",
              "      <td>48000.0</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Germany</td>\n",
              "      <td>30.0</td>\n",
              "      <td>54000.0</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Spain</td>\n",
              "      <td>38.0</td>\n",
              "      <td>61000.0</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Germany</td>\n",
              "      <td>40.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>France</td>\n",
              "      <td>35.0</td>\n",
              "      <td>58000.0</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Spain</td>\n",
              "      <td>NaN</td>\n",
              "      <td>52000.0</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>France</td>\n",
              "      <td>48.0</td>\n",
              "      <td>79000.0</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Germany</td>\n",
              "      <td>50.0</td>\n",
              "      <td>83000.0</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>France</td>\n",
              "      <td>37.0</td>\n",
              "      <td>67000.0</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Country   Age   Salary Purchased\n",
              "0   France  44.0  72000.0        No\n",
              "1    Spain  27.0  48000.0       Yes\n",
              "2  Germany  30.0  54000.0        No\n",
              "3    Spain  38.0  61000.0        No\n",
              "4  Germany  40.0      NaN       Yes\n",
              "5   France  35.0  58000.0       Yes\n",
              "6    Spain   NaN  52000.0        No\n",
              "7   France  48.0  79000.0       Yes\n",
              "8  Germany  50.0  83000.0        No\n",
              "9   France  37.0  67000.0       Yes"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jV1hpadboKVH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = data.iloc[:, :-1].values\n",
        "y = data.iloc[:, 3].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0znbtD2L73td",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "fa1a0656-1ba4-4af1-df8b-274c0120eaf1"
      },
      "source": [
        "data.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Country', 'Age', 'Salary', 'Purchased'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nTBuQ8KUxWKL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "imputer = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
        "imputer = imputer.fit(x[:, 1:3])\n",
        "x[:, 1:3] = imputer.transform(x[:, 1:3])\n",
        "data2 = x "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gU4jwhlw0u_x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "80dae485-4e68-4650-b77e-5ad692c64753"
      },
      "source": [
        "data2 = pd.DataFrame(data2, columns = ['Country','Age','Salary'])\n",
        "data2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Country</th>\n",
              "      <th>Age</th>\n",
              "      <th>Salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>France</td>\n",
              "      <td>44</td>\n",
              "      <td>72000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Spain</td>\n",
              "      <td>27</td>\n",
              "      <td>48000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Germany</td>\n",
              "      <td>30</td>\n",
              "      <td>54000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Spain</td>\n",
              "      <td>38</td>\n",
              "      <td>61000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Germany</td>\n",
              "      <td>40</td>\n",
              "      <td>63777.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>France</td>\n",
              "      <td>35</td>\n",
              "      <td>58000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Spain</td>\n",
              "      <td>38.7778</td>\n",
              "      <td>52000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>France</td>\n",
              "      <td>48</td>\n",
              "      <td>79000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Germany</td>\n",
              "      <td>50</td>\n",
              "      <td>83000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>France</td>\n",
              "      <td>37</td>\n",
              "      <td>67000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Country      Age   Salary\n",
              "0   France       44    72000\n",
              "1    Spain       27    48000\n",
              "2  Germany       30    54000\n",
              "3    Spain       38    61000\n",
              "4  Germany       40  63777.8\n",
              "5   France       35    58000\n",
              "6    Spain  38.7778    52000\n",
              "7   France       48    79000\n",
              "8  Germany       50    83000\n",
              "9   France       37    67000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QpY5N-dN6XjF",
        "colab_type": "text"
      },
      "source": [
        "### **Feature Engineering**\n",
        "\n",
        "Feature engineering is a process of transforming the given data into a form which is easier to interpret. Here, we are interested in making it more transparent for a machine learning model. [Source](https://www.kdnuggets.com/2018/12/feature-engineering-explained.html)\n",
        "\n",
        "**List of Techniques** - \n",
        "\n",
        "1.Imputation\n",
        "\n",
        "2.Handling Outliers\n",
        "\n",
        "3.Binning\n",
        "\n",
        "4.Log Transform\n",
        "\n",
        "5.One-Hot Encoding\n",
        "\n",
        "6.Grouping Operations\n",
        "\n",
        "7.Feature Split\n",
        "\n",
        "8.Scaling\n",
        "\n",
        "9.Extracting Date\n",
        "\n",
        "![Source: https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/](https://drive.google.com/uc?export=view&id=1OXqe3g-AQeCk2HXc_0wtK3D_NejF9utr)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TfXWxAm-06EP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Encoding \n",
        "# Encoding, Independent Variable\n",
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "labelencoder_x = LabelEncoder()\n",
        "x[:, 0] = labelencoder_x.fit_transform(x[:, 0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yLGMKsrp1vjJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "8a1f3cdf-1f2b-498d-96c8-2d87f28388fc"
      },
      "source": [
        "x"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 44.0, 72000.0],\n",
              "       [2, 27.0, 48000.0],\n",
              "       [1, 30.0, 54000.0],\n",
              "       [2, 38.0, 61000.0],\n",
              "       [1, 40.0, 63777.77777777778],\n",
              "       [0, 35.0, 58000.0],\n",
              "       [2, 38.77777777777778, 52000.0],\n",
              "       [0, 48.0, 79000.0],\n",
              "       [1, 50.0, 83000.0],\n",
              "       [0, 37.0, 67000.0]], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OinNayCd5Ghe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "d34bacd7-4d6d-4ed1-cec4-abec7618fb40"
      },
      "source": [
        "#OneHotEncoder\n",
        "onehotencoder = ColumnTransformer([(\"Any\", OneHotEncoder(), [0])], remainder='passthrough')   \n",
        "x = onehotencoder.fit_transform(x).tolist()\n",
        "print(x)\n",
        "# Encoding the Dependent Variable\n",
        "labelencoder_y = LabelEncoder()\n",
        "y = labelencoder_y.fit_transform(y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1.0, 0.0, 0.0, 44.0, 72000.0], [0.0, 0.0, 1.0, 27.0, 48000.0], [0.0, 1.0, 0.0, 30.0, 54000.0], [0.0, 0.0, 1.0, 38.0, 61000.0], [0.0, 1.0, 0.0, 40.0, 63777.77777777778], [1.0, 0.0, 0.0, 35.0, 58000.0], [0.0, 0.0, 1.0, 38.77777777777778, 52000.0], [1.0, 0.0, 0.0, 48.0, 79000.0], [0.0, 1.0, 0.0, 50.0, 83000.0], [1.0, 0.0, 0.0, 37.0, 67000.0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T6ORviDd6LMo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "78642fc2-476a-4e5d-f85b-d9113a8bf40f"
      },
      "source": [
        "x,y"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([[1.0, 0.0, 0.0, 44.0, 72000.0],\n",
              "  [0.0, 0.0, 1.0, 27.0, 48000.0],\n",
              "  [0.0, 1.0, 0.0, 30.0, 54000.0],\n",
              "  [0.0, 0.0, 1.0, 38.0, 61000.0],\n",
              "  [0.0, 1.0, 0.0, 40.0, 63777.77777777778],\n",
              "  [1.0, 0.0, 0.0, 35.0, 58000.0],\n",
              "  [0.0, 0.0, 1.0, 38.77777777777778, 52000.0],\n",
              "  [1.0, 0.0, 0.0, 48.0, 79000.0],\n",
              "  [0.0, 1.0, 0.0, 50.0, 83000.0],\n",
              "  [1.0, 0.0, 0.0, 37.0, 67000.0]],\n",
              " array([0, 1, 0, 0, 1, 1, 0, 1, 0, 1]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ani3EZGru_mS",
        "colab_type": "text"
      },
      "source": [
        "### **Predictive Modeling**\n",
        "Predictive modeling, also called predictive analytics, is a mathematical process that seeks to predict future events or outcomes by analyzing patterns that are likely to forecast future results. [Source](https://searchenterpriseai.techtarget.com/definition/predictive-modeling)\n",
        "\n",
        "Steps -\n",
        "\n",
        "1. Split the dataset into two pieces: a training set and a testing set.\n",
        "2. Train the model on the training set.\n",
        "3. Test the model on the testing set, and evaluate how well our model did.\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=1JZ4ENlklp16kQQl4YwIQKRmMGeE2SUJE\" alt=\"drawing\" width=\"500\"/>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r2AU7RIg2NW0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Splitting the dataset into the Training set and Test set\n",
        "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=1 / 3, random_state=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lzPytJJm6SNx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "49a6bb48-d3af-4353-e1e8-65a6d1569fde"
      },
      "source": [
        "# Fitting Simple Linear Regression to the Training set\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(x_train, y_train)\n",
        "\n",
        "# Predicting the Test set results\n",
        "y_pred = regressor.predict(x_test)\n",
        "\n",
        "y_pred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.01846729, 0.15777147, 0.42991432, 1.06593028])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rahuc8CE8BtO",
        "colab_type": "text"
      },
      "source": [
        "### **Data Visualization**\n",
        "Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. [Source](https://www.tableau.com/learn/articles/data-visualization)\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=1YQB839N4KLVDHTGVZMKjKrGpF9O5wNtv\" alt=\"drawing\" width=\"700\"/>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FxH5KEUJ9Wcg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "aeb03c37-0b71-418a-cbff-a78435f33e95"
      },
      "source": [
        "data2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Country</th>\n",
              "      <th>Age</th>\n",
              "      <th>Salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>France</td>\n",
              "      <td>44</td>\n",
              "      <td>72000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Spain</td>\n",
              "      <td>27</td>\n",
              "      <td>48000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Germany</td>\n",
              "      <td>30</td>\n",
              "      <td>54000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Spain</td>\n",
              "      <td>38</td>\n",
              "      <td>61000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Germany</td>\n",
              "      <td>40</td>\n",
              "      <td>63777.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>France</td>\n",
              "      <td>35</td>\n",
              "      <td>58000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Spain</td>\n",
              "      <td>38.7778</td>\n",
              "      <td>52000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>France</td>\n",
              "      <td>48</td>\n",
              "      <td>79000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Germany</td>\n",
              "      <td>50</td>\n",
              "      <td>83000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>France</td>\n",
              "      <td>37</td>\n",
              "      <td>67000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Country      Age   Salary\n",
              "0   France       44    72000\n",
              "1    Spain       27    48000\n",
              "2  Germany       30    54000\n",
              "3    Spain       38    61000\n",
              "4  Germany       40  63777.8\n",
              "5   France       35    58000\n",
              "6    Spain  38.7778    52000\n",
              "7   France       48    79000\n",
              "8  Germany       50    83000\n",
              "9   France       37    67000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w9w-wz369ZT3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "25886afb-072f-47f5-cfb7-fbcc7e5e028e"
      },
      "source": [
        "plt.bar(data2['Country'],data2['Age'], color=[\"green\"])\n",
        "plt.xlabel(\"Country\")\n",
        "plt.ylabel(\"Age\")\n",
        "plt.legend(['Countries'])\n",
        "plt.title(\"Countries Represents the Age Values\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Countries Represents the Age Values')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wadBoWhZAEex",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "c7c12cd9-e05b-4d65-bb9a-5513fe270512"
      },
      "source": [
        "plt.plot(data2['Salary'])\n",
        "plt.plot(data2['Age']*1000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fce195b31d0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ig6TIB4I7an0",
        "colab_type": "text"
      },
      "source": [
        "## Let's Do all these Steps on Real Data along with Data Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iuft8iOu3iWU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "aae95d6c-a5fa-4ce5-f960-ad56b3fceb52"
      },
      "source": [
        "# Python version\n",
        "import sys\n",
        "print('Python: {}'.format(sys.version))\n",
        "# scipy\n",
        "import scipy\n",
        "print('scipy: {}'.format(scipy.__version__))\n",
        "# numpy\n",
        "import numpy\n",
        "print('numpy: {}'.format(numpy.__version__))\n",
        "# matplotlib\n",
        "import matplotlib\n",
        "print('matplotlib: {}'.format(matplotlib.__version__))\n",
        "# pandas\n",
        "import pandas\n",
        "print('pandas: {}'.format(pandas.__version__))\n",
        "# scikit-learn\n",
        "import sklearn\n",
        "print('sklearn: {}'.format(sklearn.__version__))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Python: 3.6.9 (default, Jul 17 2020, 12:50:27) \n",
            "[GCC 8.4.0]\n",
            "scipy: 1.4.1\n",
            "numpy: 1.18.5\n",
            "matplotlib: 3.2.2\n",
            "pandas: 1.0.5\n",
            "sklearn: 0.22.2.post1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Aje1cf1fB8NW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load libraries\n",
        "from pandas import read_csv\n",
        "from pandas.plotting import scatter_matrix\n",
        "from matplotlib import pyplot\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.svm import SVC"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0aRUp14yDxCR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load dataset\n",
        "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv\"\n",
        "names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']\n",
        "dataset = read_csv(url, names=names)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gEh_1hEND4N5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0d0908f4-78b3-4d79-8720-cd82f68850d7"
      },
      "source": [
        "# shape\n",
        "print(dataset.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(150, 5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "spt6FhBmD8NP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "outputId": "035c5734-a254-4038-e597-a7b07e9a3c52"
      },
      "source": [
        "# head\n",
        "print(dataset.head(20))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "    sepal-length  sepal-width  petal-length  petal-width        class\n",
            "0            5.1          3.5           1.4          0.2  Iris-setosa\n",
            "1            4.9          3.0           1.4          0.2  Iris-setosa\n",
            "2            4.7          3.2           1.3          0.2  Iris-setosa\n",
            "3            4.6          3.1           1.5          0.2  Iris-setosa\n",
            "4            5.0          3.6           1.4          0.2  Iris-setosa\n",
            "5            5.4          3.9           1.7          0.4  Iris-setosa\n",
            "6            4.6          3.4           1.4          0.3  Iris-setosa\n",
            "7            5.0          3.4           1.5          0.2  Iris-setosa\n",
            "8            4.4          2.9           1.4          0.2  Iris-setosa\n",
            "9            4.9          3.1           1.5          0.1  Iris-setosa\n",
            "10           5.4          3.7           1.5          0.2  Iris-setosa\n",
            "11           4.8          3.4           1.6          0.2  Iris-setosa\n",
            "12           4.8          3.0           1.4          0.1  Iris-setosa\n",
            "13           4.3          3.0           1.1          0.1  Iris-setosa\n",
            "14           5.8          4.0           1.2          0.2  Iris-setosa\n",
            "15           5.7          4.4           1.5          0.4  Iris-setosa\n",
            "16           5.4          3.9           1.3          0.4  Iris-setosa\n",
            "17           5.1          3.5           1.4          0.3  Iris-setosa\n",
            "18           5.7          3.8           1.7          0.3  Iris-setosa\n",
            "19           5.1          3.8           1.5          0.3  Iris-setosa\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VPG4ZlAbD-hR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 179
        },
        "outputId": "8b14926e-c1d6-4ec3-d3e4-ed8b98617879"
      },
      "source": [
        "# descriptions\n",
        "print(dataset.describe())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "       sepal-length  sepal-width  petal-length  petal-width\n",
            "count    150.000000   150.000000    150.000000   150.000000\n",
            "mean       5.843333     3.054000      3.758667     1.198667\n",
            "std        0.828066     0.433594      1.764420     0.763161\n",
            "min        4.300000     2.000000      1.000000     0.100000\n",
            "25%        5.100000     2.800000      1.600000     0.300000\n",
            "50%        5.800000     3.000000      4.350000     1.300000\n",
            "75%        6.400000     3.300000      5.100000     1.800000\n",
            "max        7.900000     4.400000      6.900000     2.500000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MF4lAzy5EDLf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "outputId": "02a93a14-ee32-4813-e451-b143c0a4e372"
      },
      "source": [
        "# class distribution\n",
        "print(dataset.groupby('class').size())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "class\n",
            "Iris-setosa        50\n",
            "Iris-versicolor    50\n",
            "Iris-virginica     50\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K2CGrmwYEUdU",
        "colab_type": "text"
      },
      "source": [
        "### We are going to look at two types of plots:\n",
        "\n",
        "Univariate plots to better understand each attribute.\n",
        "\n",
        "Multivariate plots to better understand the relationships between attributes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5X5It0ICEHA2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "outputId": "725fba98-c800-42d3-8782-76c9994df8b1"
      },
      "source": [
        "# Data Visualization - Univariate Plot\n",
        "# Box and whisker plots\n",
        "dataset.plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False)\n",
        "pyplot.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dTxzIMAyEOzb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "81a53093-9900-42aa-f28f-8a90ac73125f"
      },
      "source": [
        "# histograms\n",
        "dataset.hist()\n",
        "pyplot.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "btFZiuMjEfuA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "f68447d4-affd-4d27-a137-7826d1db4c6e"
      },
      "source": [
        "# Multivariate Plots\n",
        "# scatter plot matrix\n",
        "scatter_matrix(dataset)\n",
        "pyplot.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nNvPWRf_EnF5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Split-out validation dataset\n",
        "array = dataset.values\n",
        "X = array[:,0:4]\n",
        "y = array[:,4]\n",
        "X_train, X_validation, Y_train, Y_validation = train_test_split(X, y, test_size=0.20, random_state=47)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XyGloN2wEtQ1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "outputId": "9ee6fdfa-f687-4c41-80ea-d1ff42e6a032"
      },
      "source": [
        "# Spot Check Algorithms\n",
        "models = []\n",
        "models.append(('LR', LogisticRegression(solver='liblinear', multi_class='ovr')))\n",
        "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
        "models.append(('KNN', KNeighborsClassifier()))\n",
        "models.append(('CART', DecisionTreeClassifier()))\n",
        "models.append(('NB', GaussianNB()))\n",
        "models.append(('SVM', SVC(gamma='auto')))\n",
        "# evaluate each model in turn\n",
        "results = []\n",
        "names = []\n",
        "for name, model in models:\n",
        "\tkfold = StratifiedKFold(n_splits=10, random_state=1, shuffle=True)\n",
        "\tcv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy')\n",
        "\tresults.append(cv_results)\n",
        "\tnames.append(name)\n",
        "\tprint('%s: %f (%f)' % (name, cv_results.mean(), cv_results.std()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LR: 0.950000 (0.066667)\n",
            "LDA: 0.975000 (0.038188)\n",
            "KNN: 0.958333 (0.041667)\n",
            "CART: 0.941667 (0.083749)\n",
            "NB: 0.941667 (0.075000)\n",
            "SVM: 0.958333 (0.055902)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hqD6ZpIXE0Bu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "b686714e-c24a-42a6-ebe3-e6c276cd68ff"
      },
      "source": [
        "# Compare Algorithms\n",
        "pyplot.boxplot(results, labels=names)\n",
        "pyplot.title('Algorithm Comparison')\n",
        "pyplot.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kAbc93cdFFeB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Make predictions on validation dataset\n",
        "model = SVC(gamma='auto')\n",
        "model.fit(X_train, Y_train)\n",
        "predictions = model.predict(X_validation)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZTN_EGpoFPhF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "0ec5d2e7-998c-47a5-d1b3-1ebcf205647d"
      },
      "source": [
        "# Evaluate predictions\n",
        "print(accuracy_score(Y_validation, predictions))\n",
        "print(confusion_matrix(Y_validation, predictions))\n",
        "print(classification_report(Y_validation, predictions))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.0\n",
            "[[11  0  0]\n",
            " [ 0  7  0]\n",
            " [ 0  0 12]]\n",
            "                 precision    recall  f1-score   support\n",
            "\n",
            "    Iris-setosa       1.00      1.00      1.00        11\n",
            "Iris-versicolor       1.00      1.00      1.00         7\n",
            " Iris-virginica       1.00      1.00      1.00        12\n",
            "\n",
            "       accuracy                           1.00        30\n",
            "      macro avg       1.00      1.00      1.00        30\n",
            "   weighted avg       1.00      1.00      1.00        30\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YMzE7u78FSJZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}