_dataset_genration.ipynb

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      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "## **Creating Dummy DataSet**"
      ]
    },
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          "text": [
            "(array(['Bachelor', 'High School', 'Master', 'PhD'], dtype='<U11'), array([33, 25, 21, 21]))\n",
            "Bachelor: 33 occurrences\n",
            "High School: 25 occurrences\n",
            "Master: 21 occurrences\n",
            "PhD: 21 occurrences\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "labels = ['High School', 'Bachelor', 'Master', 'PhD']\n",
        "size = 100\n",
        "\n",
        "# Generate stratified choice with equal label distribution\n",
        "stratified_data = np.random.choice(labels, size=size, p=[0.25, 0.25, 0.25, 0.25])\n",
        "\n",
        "# Check the distribution of labels\n",
        "label_counts = np.unique(stratified_data, return_counts=True)\n",
        "\n",
        "print(label_counts)\n",
        "\n",
        "for label, count in zip(label_counts[0], label_counts[1]):\n",
        "    print(f\"{label}: {count} occurrences\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yOAF-USu6T56"
      },
      "source": [
        "**1. Simple DataSet**"
      ]
    },
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              "    age  income gender education marital_status  label\n",
              "0    69    4561      M    Master         Single      1\n",
              "1    32    7184      F    Master        Widowed      1\n",
              "2    89    4099      F    Master        Widowed      0\n",
              "3    78    7278      F  Bachelor        Married      0\n",
              "4    38    9392      M  Bachelor        Married      0\n",
              "..  ...     ...    ...       ...            ...    ...\n",
              "95   25    8777      M  Bachelor        Married      0\n",
              "96   52    1197      M    Master       Divorced      1\n",
              "97   52    8125      F  Bachelor       Divorced      0\n",
              "98   50    2930      M    Master       Divorced      1\n",
              "99   22    7287      M       PhD        Widowed      0\n",
              "\n",
              "[100 rows x 6 columns]"
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              "      <th>0</th>\n",
              "      <td>69</td>\n",
              "      <td>4561</td>\n",
              "      <td>M</td>\n",
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              "      <th>1</th>\n",
              "      <td>32</td>\n",
              "      <td>7184</td>\n",
              "      <td>F</td>\n",
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              "      <th>2</th>\n",
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              "      <td>4099</td>\n",
              "      <td>F</td>\n",
              "      <td>Master</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>78</td>\n",
              "      <td>7278</td>\n",
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              "      <th>4</th>\n",
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              "      <td>9392</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>25</td>\n",
              "      <td>8777</td>\n",
              "      <td>M</td>\n",
              "      <td>Bachelor</td>\n",
              "      <td>Married</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>52</td>\n",
              "      <td>1197</td>\n",
              "      <td>M</td>\n",
              "      <td>Master</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>1</td>\n",
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              "      <th>97</th>\n",
              "      <td>52</td>\n",
              "      <td>8125</td>\n",
              "      <td>F</td>\n",
              "      <td>Bachelor</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>50</td>\n",
              "      <td>2930</td>\n",
              "      <td>M</td>\n",
              "      <td>Master</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>22</td>\n",
              "      <td>7287</td>\n",
              "      <td>M</td>\n",
              "      <td>PhD</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>0</td>\n",
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              "<p>100 rows × 6 columns</p>\n",
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      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# setting the random seed\n",
        "np.random.seed(42)\n",
        "\n",
        "# Defining the dataset\n",
        "ages = np.random.randint(18, 100, size=100)\n",
        "income = np.random.randint(1000, 10000, size=100)\n",
        "genders = np.random.choice(['F', 'M'], size=100, p=[0.5, 0.5])\n",
        "education = np.random.choice(['High School', 'Bachelor', 'Master', 'PhD'], size=100, p=[0.25, 0.25, 0.25, 0.25])\n",
        "marital_status = np.random.choice(['Married', 'Single', 'Divorced', 'Widowed'], size=100, p=[0.25, 0.25, 0.25, 0.25])\n",
        "\n",
        "labels = []\n",
        "for age, gender in zip(ages, genders):\n",
        "    if age < 50 and gender == 'F':\n",
        "        labels.append(1)\n",
        "    elif age < 50 and gender == 'M':\n",
        "        labels.append(0)\n",
        "    elif age >= 50 and gender == 'F':\n",
        "        labels.append(0)\n",
        "    else:\n",
        "        labels.append(1)\n",
        "labels = np.array(labels)\n",
        "\n",
        "\n",
        "# to the pandas dataframe\n",
        "sample_data_1 = pd.DataFrame({\n",
        "    'age': ages,\n",
        "    'income': income,\n",
        "    'gender': genders,\n",
        "    'education': education,\n",
        "    'marital_status': marital_status,\n",
        "    'label': labels\n",
        "})\n",
        "\n",
        "display(sample_data_1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "whV7YJGN6T57"
      },
      "source": [
        "**2. Dataset with even more columns**\n",
        "\n",
        "> **which we can use to practice using various Scikit-learn preprocessing tools**"
      ]
    },
    {
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            "text/plain": [
              "    age gender  income    education marital_status  num_friends  \\\n",
              "0    84      M    3191  High School        Married           14   \n",
              "1    35      M    9113          PhD         Single           37   \n",
              "2    75      F    3512  High School        Widowed           96   \n",
              "3    65      F    1639  High School       Divorced           48   \n",
              "4    91      F    4054          PhD        Widowed           71   \n",
              "..  ...    ...     ...          ...            ...          ...   \n",
              "95   18      F    9281          PhD        Widowed           49   \n",
              "96   53      M    3625  High School        Married           79   \n",
              "97   47      F    6397          PhD       Divorced           67   \n",
              "98   19      F    5028     Bachelor        Married           24   \n",
              "99   86      M    7139          PhD        Widowed           32   \n",
              "\n",
              "    num_purchases  credit_score  num_children         city  label  \n",
              "0             760           697             4      Phoenix      1  \n",
              "1               0           719             1  Los Angeles      0  \n",
              "2             590           453             4      Phoenix      0  \n",
              "3             127           531             2     New York      0  \n",
              "4             144           645             1      Phoenix      0  \n",
              "..            ...           ...           ...          ...    ...  \n",
              "95             23           779             3  Los Angeles      1  \n",
              "96            987           767             3      Houston      1  \n",
              "97              4           711             9      Phoenix      1  \n",
              "98            925           599             1      Houston      1  \n",
              "99            298           673             5      Phoenix      1  \n",
              "\n",
              "[100 rows x 11 columns]"
            ],
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              "      <th>0</th>\n",
              "      <td>84</td>\n",
              "      <td>M</td>\n",
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              "      <td>High School</td>\n",
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              "      <th>1</th>\n",
              "      <td>35</td>\n",
              "      <td>M</td>\n",
              "      <td>9113</td>\n",
              "      <td>PhD</td>\n",
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              "      <th>2</th>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>65</td>\n",
              "      <td>F</td>\n",
              "      <td>1639</td>\n",
              "      <td>High School</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>48</td>\n",
              "      <td>127</td>\n",
              "      <td>531</td>\n",
              "      <td>2</td>\n",
              "      <td>New York</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>91</td>\n",
              "      <td>F</td>\n",
              "      <td>4054</td>\n",
              "      <td>PhD</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>71</td>\n",
              "      <td>144</td>\n",
              "      <td>645</td>\n",
              "      <td>1</td>\n",
              "      <td>Phoenix</td>\n",
              "      <td>0</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>18</td>\n",
              "      <td>F</td>\n",
              "      <td>9281</td>\n",
              "      <td>PhD</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>49</td>\n",
              "      <td>23</td>\n",
              "      <td>779</td>\n",
              "      <td>3</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>53</td>\n",
              "      <td>M</td>\n",
              "      <td>3625</td>\n",
              "      <td>High School</td>\n",
              "      <td>Married</td>\n",
              "      <td>79</td>\n",
              "      <td>987</td>\n",
              "      <td>767</td>\n",
              "      <td>3</td>\n",
              "      <td>Houston</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>47</td>\n",
              "      <td>F</td>\n",
              "      <td>6397</td>\n",
              "      <td>PhD</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>67</td>\n",
              "      <td>4</td>\n",
              "      <td>711</td>\n",
              "      <td>9</td>\n",
              "      <td>Phoenix</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>19</td>\n",
              "      <td>F</td>\n",
              "      <td>5028</td>\n",
              "      <td>Bachelor</td>\n",
              "      <td>Married</td>\n",
              "      <td>24</td>\n",
              "      <td>925</td>\n",
              "      <td>599</td>\n",
              "      <td>1</td>\n",
              "      <td>Houston</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>86</td>\n",
              "      <td>M</td>\n",
              "      <td>7139</td>\n",
              "      <td>PhD</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>32</td>\n",
              "      <td>298</td>\n",
              "      <td>673</td>\n",
              "      <td>5</td>\n",
              "      <td>Phoenix</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 11 columns</p>\n",
              "</div>\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\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",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Define the dataset\n",
        "np.random.seed(123)\n",
        "\n",
        "ages = np.random.randint(18, 100, size=100)\n",
        "genders = np.random.choice(['F', 'M'], size=100, p=[0.50, 0.50])\n",
        "income = np.random.randint(1000, 10000, size=100)\n",
        "education = np.random.choice(['High School', 'Bachelor', 'Master', 'PhD'], size=100, p=[0.25, 0.25, 0.25, 0.25])\n",
        "marital_status = np.random.choice(['Married', 'Single', 'Divorced', 'Widowed'], size=100, p=[0.25, 0.25, 0.25, 0.25])\n",
        "num_friends = np.random.randint(0, 100, size=100)\n",
        "num_purchases = np.random.randint(0, 1000, size=100)\n",
        "credit_score = np.random.randint(300, 850, size=100)\n",
        "num_children = np.random.randint(0, 10, size=100)\n",
        "city = np.random.choice(['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix'], size=100, p=[0.20, 0.20, 0.20, 0.20, 0.20])\n",
        "\n",
        "labels = []\n",
        "for age, gender in zip(ages, genders):\n",
        "    if age < 50 and gender == 'F':\n",
        "        labels.append(1)\n",
        "    elif age < 50 and gender == 'M':\n",
        "        labels.append(0)\n",
        "    elif age >= 50 and gender == 'F':\n",
        "        labels.append(0)\n",
        "    else:\n",
        "        labels.append(1)\n",
        "labels = np.array(labels)\n",
        "\n",
        "sample_data_2 = pd.DataFrame({\n",
        "    'age': ages,\n",
        "    'gender': genders,\n",
        "    'income': income,\n",
        "    'education': education,\n",
        "    'marital_status': marital_status,\n",
        "    'num_friends': num_friends,\n",
        "    'num_purchases': num_purchases,\n",
        "    'credit_score': credit_score,\n",
        "    'num_children': num_children,\n",
        "    'city': city,\n",
        "    'label': labels\n",
        "})\n",
        "\n",
        "display(sample_data_2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_keLfwYd6T57"
      },
      "source": [
        "**3. Dataset with some missing values and duplicates**\n",
        "\n",
        "we can use to practice data **cleaning** and **preprocessing**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "YaUuMDEp6T57",
        "outputId": "b22ca135-1901-4617-8953-ca258f492125"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "     age gender  income    education marital_status  num_friends  \\\n",
              "0   69.0      M  7694.0     Bachelor         Single         54.0   \n",
              "1   32.0      M  3385.0     Bachelor        Widowed         12.0   \n",
              "2   89.0      M  5736.0     Bachelor       Divorced         22.0   \n",
              "3   78.0      M  2802.0  High School         Single         88.0   \n",
              "4   38.0      F  9155.0          NaN         Single         98.0   \n",
              "..   ...    ...     ...          ...            ...          ...   \n",
              "0   69.0      M  7694.0     Bachelor         Single         54.0   \n",
              "1   32.0      M  3385.0     Bachelor        Widowed         12.0   \n",
              "2   89.0      M  5736.0     Bachelor       Divorced         22.0   \n",
              "3   78.0      M  2802.0  High School         Single         88.0   \n",
              "4   38.0      F  9155.0          NaN         Single         98.0   \n",
              "\n",
              "    num_purchases  credit_score  num_children         city  label  \n",
              "0           894.0         749.0             5     New York    1.0  \n",
              "1           360.0         309.0             0      Chicago    0.0  \n",
              "2           934.0         541.0             4     New York    1.0  \n",
              "3             0.0         550.0             8  Los Angeles    1.0  \n",
              "4           386.0         304.0             9      Chicago    1.0  \n",
              "..            ...           ...           ...          ...    ...  \n",
              "0           894.0         749.0             5     New York    1.0  \n",
              "1           360.0         309.0             0      Chicago    0.0  \n",
              "2           934.0         541.0             4     New York    1.0  \n",
              "3             0.0         550.0             8  Los Angeles    1.0  \n",
              "4           386.0         304.0             9      Chicago    1.0  \n",
              "\n",
              "[105 rows x 11 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-956d20f5-43af-4406-a83d-913da68711fc\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>gender</th>\n",
              "      <th>income</th>\n",
              "      <th>education</th>\n",
              "      <th>marital_status</th>\n",
              "      <th>num_friends</th>\n",
              "      <th>num_purchases</th>\n",
              "      <th>credit_score</th>\n",
              "      <th>num_children</th>\n",
              "      <th>city</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>69.0</td>\n",
              "      <td>M</td>\n",
              "      <td>7694.0</td>\n",
              "      <td>Bachelor</td>\n",
              "      <td>Single</td>\n",
              "      <td>54.0</td>\n",
              "      <td>894.0</td>\n",
              "      <td>749.0</td>\n",
              "      <td>5</td>\n",
              "      <td>New York</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>32.0</td>\n",
              "      <td>M</td>\n",
              "      <td>3385.0</td>\n",
              "      <td>Bachelor</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>12.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>309.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Chicago</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>89.0</td>\n",
              "      <td>M</td>\n",
              "      <td>5736.0</td>\n",
              "      <td>Bachelor</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>22.0</td>\n",
              "      <td>934.0</td>\n",
              "      <td>541.0</td>\n",
              "      <td>4</td>\n",
              "      <td>New York</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>78.0</td>\n",
              "      <td>M</td>\n",
              "      <td>2802.0</td>\n",
              "      <td>High School</td>\n",
              "      <td>Single</td>\n",
              "      <td>88.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>550.0</td>\n",
              "      <td>8</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>38.0</td>\n",
              "      <td>F</td>\n",
              "      <td>9155.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Single</td>\n",
              "      <td>98.0</td>\n",
              "      <td>386.0</td>\n",
              "      <td>304.0</td>\n",
              "      <td>9</td>\n",
              "      <td>Chicago</td>\n",
              "      <td>1.0</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>69.0</td>\n",
              "      <td>M</td>\n",
              "      <td>7694.0</td>\n",
              "      <td>Bachelor</td>\n",
              "      <td>Single</td>\n",
              "      <td>54.0</td>\n",
              "      <td>894.0</td>\n",
              "      <td>749.0</td>\n",
              "      <td>5</td>\n",
              "      <td>New York</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>32.0</td>\n",
              "      <td>M</td>\n",
              "      <td>3385.0</td>\n",
              "      <td>Bachelor</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>12.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>309.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Chicago</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>89.0</td>\n",
              "      <td>M</td>\n",
              "      <td>5736.0</td>\n",
              "      <td>Bachelor</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>22.0</td>\n",
              "      <td>934.0</td>\n",
              "      <td>541.0</td>\n",
              "      <td>4</td>\n",
              "      <td>New York</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>78.0</td>\n",
              "      <td>M</td>\n",
              "      <td>2802.0</td>\n",
              "      <td>High School</td>\n",
              "      <td>Single</td>\n",
              "      <td>88.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>550.0</td>\n",
              "      <td>8</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>38.0</td>\n",
              "      <td>F</td>\n",
              "      <td>9155.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Single</td>\n",
              "      <td>98.0</td>\n",
              "      <td>386.0</td>\n",
              "      <td>304.0</td>\n",
              "      <td>9</td>\n",
              "      <td>Chicago</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>105 rows × 11 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-956d20f5-43af-4406-a83d-913da68711fc')\"\n",
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              "              title=\"Suggest charts.\"\n",
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              "    </g>\n",
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              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
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              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "    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",
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              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
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              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-328821e8-e5a7-40b7-ba25-ecdba1b285e6 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-956d20f5-43af-4406-a83d-913da68711fc 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-956d20f5-43af-4406-a83d-913da68711fc');\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"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Define the dataset\n",
        "np.random.seed(42)\n",
        "\n",
        "ages = np.random.randint(18, 100, size=100)\n",
        "genders = np.random.choice(['F', 'M'], size=100, p=[0.50, 0.50])\n",
        "income = np.random.randint(1000, 10000, size=100)\n",
        "education = np.random.choice(['High School', 'Bachelor', 'Master', 'PhD', np.nan], size=100)\n",
        "marital_status = np.random.choice(['Married', 'Single', 'Divorced', 'Widowed'], size=100)\n",
        "num_friends = np.random.randint(0, 100, size=100)\n",
        "num_purchases = np.random.randint(0, 1000, size=100)\n",
        "credit_score = np.random.randint(300, 850, size=100)\n",
        "num_children = np.random.randint(0, 10, size=100)\n",
        "city = np.random.choice(['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix'], size=100)\n",
        "\n",
        "labels = []\n",
        "for age, gender in zip(ages, genders):\n",
        "    if age < 50 and gender == 'F':\n",
        "        labels.append(1)\n",
        "    elif age < 50 and gender == 'M':\n",
        "        labels.append(0)\n",
        "    elif age >= 50 and gender == 'F':\n",
        "        labels.append(0)\n",
        "    else:\n",
        "        labels.append(1)\n",
        "labels = np.array(labels)\n",
        "\n",
        "# Create the dataframe\n",
        "sample_data_3 = pd.DataFrame({\n",
        "    'age': ages,\n",
        "    'gender': genders,\n",
        "    'income': income,\n",
        "    'education': education,\n",
        "    'marital_status': marital_status,\n",
        "    'num_friends': num_friends,\n",
        "    'num_purchases': num_purchases,\n",
        "    'credit_score': credit_score,\n",
        "    'num_children': num_children,\n",
        "    'city': city,\n",
        "    'label': labels\n",
        "})\n",
        "\n",
        "# Introduce missing values\n",
        "for i in range(20):\n",
        "    data_index = np.random.randint(0, 100)\n",
        "    column_index = np.random.randint(0, 11)\n",
        "    sample_data_3.iloc[data_index, column_index] = np.nan\n",
        "\n",
        "# Introduce duplicates\n",
        "sample_data_3 = pd.concat([sample_data_3, sample_data_3.iloc[0:5]])\n",
        "\n",
        "display(sample_data_3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WAjOPiwQ6T58"
      },
      "source": [
        "**4. Modifying the data[`adding more features`] `+` missing `+` adding outliers**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C1sNzojZ6T58",
        "outputId": "0910f72a-73df-4ed9-9159-6a99408b255c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.7 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.3/1.7 MB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m29.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m22.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install faker -q"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TFxTfAXN6T58"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from faker import Faker\n",
        "from sklearn.preprocessing import Binarizer\n",
        "from sklearn.impute import SimpleImputer\n",
        "\n",
        "np.random.seed(42)\n",
        "\n",
        "# creating the fakers object\n",
        "faker = Faker()\n",
        "\n",
        "# Define the dataset\n",
        "ages = np.random.randint(18, 100, size=100)\n",
        "genders = np.random.choice(['F', 'M'], size=100, p=[0.50, 0.50])\n",
        "income = np.random.randint(1000, 10000, size=100)\n",
        "education = np.random.choice(['High School', 'Bachelor', 'Master', 'PhD', np.nan], size=100)\n",
        "marital_status = np.random.choice(['Married', 'Single', 'Divorced', 'Widowed'], size=100)\n",
        "num_friends = np.random.randint(0, 100, size=100)\n",
        "num_purchases = np.random.randint(0, 1000, size=100)\n",
        "credit_score = np.random.randint(300, 850, size=100)\n",
        "num_children = np.random.randint(0, 10, size=100)\n",
        "city = np.random.choice(['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix'], size=100, p=[0.20, 0.20, 0.20, 0.20, 0.20])\n",
        "\n",
        "labels = []\n",
        "for age, gender in zip(ages, genders):\n",
        "    if age < 50 and gender == 'F':\n",
        "        labels.append(1)\n",
        "    elif age < 50 and gender == 'M':\n",
        "        labels.append(0)\n",
        "    elif age >= 50 and gender == 'F':\n",
        "        labels.append(0)\n",
        "    else:\n",
        "        labels.append(1)\n",
        "labels = np.array(labels)\n",
        "\n",
        "# Create the dataframe\n",
        "sample_data_4 = pd.DataFrame({\n",
        "    'age': ages,\n",
        "    'gender': genders,\n",
        "    'income': income,\n",
        "    'education': education,\n",
        "    'marital_status': marital_status,\n",
        "    'num_friends': num_friends,\n",
        "    'num_purchases': num_purchases,\n",
        "    'credit_score': credit_score,\n",
        "    'num_children': num_children,\n",
        "    'city': city,\n",
        "    'label': labels\n",
        "})\n",
        "\n",
        "# Add new features\n",
        "sample_data_4['occupation'] = np.random.choice(['Teacher', 'Engineer', 'Doctor', 'Lawyer', 'Accountant',\n",
        "                                                'Artist', 'Writer', 'Salesperson', 'Manager', 'Student'], size=100)\n",
        "sample_data_4['height'] = np.random.randint(150, 200, size=100)\n",
        "sample_data_4['bio'] = [faker.sentence() for _ in range(100)]\n",
        "sample_data_4['birthdate'] = pd.to_datetime(np.random.choice(\n",
        "    pd.date_range('1950-01-01', '2005-12-31'), size=100))\n",
        "\n",
        "# Introduce missing values\n",
        "for i in range(20):\n",
        "    data_index = np.random.randint(0, 100)\n",
        "    column_index = np.random.randint(0, 11)\n",
        "    sample_data_4.iloc[data_index, column_index] = np.nan\n",
        "\n",
        "# Introduce outliers\n",
        "for i in range(10):\n",
        "    data_index = np.random.randint(0, 100)\n",
        "    column_index = np.random.choice([2, 6, 8])\n",
        "    sample_data_4.iloc[data_index, column_index] = np.random.choice(\n",
        "        [500000, -500000, -1])\n",
        "\n",
        "# Create new feature from existing one\n",
        "binarizer = Binarizer(threshold=50)\n",
        "\n",
        "# Impute missing values with mean\n",
        "imputer = SimpleImputer(strategy='mean')\n",
        "sample_data_4['age'] = imputer.fit_transform(sample_data_4[['age']])\n",
        "\n",
        "# Apply binarizer\n",
        "sample_data_4['age_group'] = binarizer.fit_transform(\n",
        "    sample_data_4[['age']])\n",
        "\n",
        "display(sample_data_4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xw2LTxQS5ign"
      },
      "outputs": [],
      "source": [
        "sample_data_4.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Creating the Dataset using `sklearn.dataset`**"
      ],
      "metadata": {
        "id": "wL8Apyu0uhQO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.datasets import make_regression\n",
        "\n",
        "# Generate regression dataset\n",
        "X, y = make_regression(n_samples=100, n_features=1, random_state=42)\n",
        "\n",
        "# Plot the data\n",
        "plt.scatter(X, y)\n",
        "plt.xlabel('X')\n",
        "plt.ylabel('y')\n",
        "plt.title('Regression Data')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "pCdJ2fwEuuBA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.datasets import make_classification\n",
        "\n",
        "# Generate classification dataset\n",
        "X, y = make_classification(n_samples=100, n_features=2, n_informative=2, n_redundant=0, n_classes=2, random_state=42)\n",
        "\n",
        "# Plot the data\n",
        "plt.scatter(X[:, 0], X[:, 1], c=y, cmap='bwr')\n",
        "plt.xlabel('Feature 1')\n",
        "plt.ylabel('Feature 2')\n",
        "plt.title('Classification Data')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "dVt_9sPIuxdr"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}