Assignment 1/Ibrahim_Mohamed_A1_ML_intro.ipynb

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        "---\n",
        "# Cairo University Faculty of Engineering\n",
        "## Machine Learning \n",
        "## Assignment 1 \n",
        "\n",
        "---"
      ]
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      "source": [
        "Please write your full name here\n",
        "- **Name** : \"Ibrahim Mohamed Ibrahim\""
      ]
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      "source": [
        "## Introduction to Machine Learning\n",
        "For this assignment, you will be using the Breast Cancer Wisconsin (Diagnostic) Database to create a classifier that can help diagnose patients. First, read through the description of the dataset (below)."
      ]
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            ".. _breast_cancer_dataset:\n",
            "\n",
            "Breast cancer wisconsin (diagnostic) dataset\n",
            "--------------------------------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            "    :Number of Instances: 569\n",
            "\n",
            "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
            "\n",
            "    :Attribute Information:\n",
            "        - radius (mean of distances from center to points on the perimeter)\n",
            "        - texture (standard deviation of gray-scale values)\n",
            "        - perimeter\n",
            "        - area\n",
            "        - smoothness (local variation in radius lengths)\n",
            "        - compactness (perimeter^2 / area - 1.0)\n",
            "        - concavity (severity of concave portions of the contour)\n",
            "        - concave points (number of concave portions of the contour)\n",
            "        - symmetry\n",
            "        - fractal dimension (\"coastline approximation\" - 1)\n",
            "\n",
            "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
            "        worst/largest values) of these features were computed for each image,\n",
            "        resulting in 30 features.  For instance, field 0 is Mean Radius, field\n",
            "        10 is Radius SE, field 20 is Worst Radius.\n",
            "\n",
            "        - class:\n",
            "                - WDBC-Malignant\n",
            "                - WDBC-Benign\n",
            "\n",
            "    :Summary Statistics:\n",
            "\n",
            "    ===================================== ====== ======\n",
            "                                           Min    Max\n",
            "    ===================================== ====== ======\n",
            "    radius (mean):                        6.981  28.11\n",
            "    texture (mean):                       9.71   39.28\n",
            "    perimeter (mean):                     43.79  188.5\n",
            "    area (mean):                          143.5  2501.0\n",
            "    smoothness (mean):                    0.053  0.163\n",
            "    compactness (mean):                   0.019  0.345\n",
            "    concavity (mean):                     0.0    0.427\n",
            "    concave points (mean):                0.0    0.201\n",
            "    symmetry (mean):                      0.106  0.304\n",
            "    fractal dimension (mean):             0.05   0.097\n",
            "    radius (standard error):              0.112  2.873\n",
            "    texture (standard error):             0.36   4.885\n",
            "    perimeter (standard error):           0.757  21.98\n",
            "    area (standard error):                6.802  542.2\n",
            "    smoothness (standard error):          0.002  0.031\n",
            "    compactness (standard error):         0.002  0.135\n",
            "    concavity (standard error):           0.0    0.396\n",
            "    concave points (standard error):      0.0    0.053\n",
            "    symmetry (standard error):            0.008  0.079\n",
            "    fractal dimension (standard error):   0.001  0.03\n",
            "    radius (worst):                       7.93   36.04\n",
            "    texture (worst):                      12.02  49.54\n",
            "    perimeter (worst):                    50.41  251.2\n",
            "    area (worst):                         185.2  4254.0\n",
            "    smoothness (worst):                   0.071  0.223\n",
            "    compactness (worst):                  0.027  1.058\n",
            "    concavity (worst):                    0.0    1.252\n",
            "    concave points (worst):               0.0    0.291\n",
            "    symmetry (worst):                     0.156  0.664\n",
            "    fractal dimension (worst):            0.055  0.208\n",
            "    ===================================== ====== ======\n",
            "\n",
            "    :Missing Attribute Values: None\n",
            "\n",
            "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
            "\n",
            "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
            "\n",
            "    :Donor: Nick Street\n",
            "\n",
            "    :Date: November, 1995\n",
            "\n",
            "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
            "https://goo.gl/U2Uwz2\n",
            "\n",
            "Features are computed from a digitized image of a fine needle\n",
            "aspirate (FNA) of a breast mass.  They describe\n",
            "characteristics of the cell nuclei present in the image.\n",
            "\n",
            "Separating plane described above was obtained using\n",
            "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
            "Construction Via Linear Programming.\" Proceedings of the 4th\n",
            "Midwest Artificial Intelligence and Cognitive Science Society,\n",
            "pp. 97-101, 1992], a classification method which uses linear\n",
            "programming to construct a decision tree.  Relevant features\n",
            "were selected using an exhaustive search in the space of 1-4\n",
            "features and 1-3 separating planes.\n",
            "\n",
            "The actual linear program used to obtain the separating plane\n",
            "in the 3-dimensional space is that described in:\n",
            "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
            "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
            "Optimization Methods and Software 1, 1992, 23-34].\n",
            "\n",
            "This database is also available through the UW CS ftp server:\n",
            "\n",
            "ftp ftp.cs.wisc.edu\n",
            "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
            "\n",
            ".. topic:: References\n",
            "\n",
            "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
            "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
            "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
            "     San Jose, CA, 1993.\n",
            "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
            "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
            "     July-August 1995.\n",
            "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
            "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
            "     163-171.\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.datasets import load_breast_cancer\n",
        "\n",
        "cancer = load_breast_cancer()\n",
        "\n",
        "print(cancer.DESCR) # Print the data set description"
      ]
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      "source": [
        "The object returned by `load_breast_cancer()` is a scikit-learn Bunch object, which is similar to a dictionary."
      ]
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          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
            ]
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          "execution_count": 6
        }
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      "source": [
        "cancer.keys()"
      ]
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      },
      "source": [
        "### Question 0\n",
        "\n",
        "How many features does the breast cancer dataset have?\n",
        "\n",
        "*This function should return an integer.*"
      ]
    },
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      "source": [
        "def answer_zero():\n",
        "    feature_count = len(cancer['feature_names'])\n",
        "    \n",
        "    return feature_count"
      ]
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      "source": [
        "### Question 1\n",
        "Pandas dataframes are much easier and more interpretable when dealing with tables. **Convert the cancer data into dataframe.**\n",
        "\n",
        "Note: Scikit-learn works with lists, numpy arrays, scipy-sparse matrices, and pandas DataFrames, so converting the dataset to a DataFrame is not necessary for training any model.\n",
        "\n",
        "*This function should return a (569, 31) DataFrame with *\n",
        "\n",
        "*columns = *\n",
        "\n",
        "    ['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
        "    'mean smoothness', 'mean compactness', 'mean concavity',\n",
        "    'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
        "    'radius error', 'texture error', 'perimeter error', 'area error',\n",
        "    'smoothness error', 'compactness error', 'concavity error',\n",
        "    'concave points error', 'symmetry error', 'fractal dimension error',\n",
        "    'worst radius', 'worst texture', 'worst perimeter', 'worst area',\n",
        "    'worst smoothness', 'worst compactness', 'worst concavity',\n",
        "    'worst concave points', 'worst symmetry', 'worst fractal dimension',\n",
        "    'target']\n",
        "    \n",
        "*and index = *\n",
        "\n",
        "RangeIndex(start=0, stop=569, step=1)"
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              "    mean radius mean texture mean perimeter mean area mean smoothness  \\\n",
              "0         17.99        10.38         122.80    1001.0         0.11840   \n",
              "1         20.57        17.77         132.90    1326.0         0.08474   \n",
              "2         19.69        21.25         130.00    1203.0         0.10960   \n",
              "3         11.42        20.38          77.58     386.1         0.14250   \n",
              "4         20.29        14.34         135.10    1297.0         0.10030   \n",
              "..          ...          ...            ...       ...             ...   \n",
              "564       21.56        22.39         142.00    1479.0         0.11100   \n",
              "565       20.13        28.25         131.20    1261.0         0.09780   \n",
              "566       16.60        28.08         108.30     858.1         0.08455   \n",
              "567       20.60        29.33         140.10    1265.0         0.11780   \n",
              "568        7.76        24.54          47.92     181.0         0.05263   \n",
              "\n",
              "    mean compactness mean concavity mean concave points mean symmetry  \\\n",
              "0            0.27760        0.30010             0.14710        0.2419   \n",
              "1            0.07864        0.08690             0.07017        0.1812   \n",
              "2            0.15990        0.19740             0.12790        0.2069   \n",
              "3            0.28390        0.24140             0.10520        0.2597   \n",
              "4            0.13280        0.19800             0.10430        0.1809   \n",
              "..               ...            ...                 ...           ...   \n",
              "564          0.11590        0.24390             0.13890        0.1726   \n",
              "565          0.10340        0.14400             0.09791        0.1752   \n",
              "566          0.10230        0.09251             0.05302        0.1590   \n",
              "567          0.27700        0.35140             0.15200        0.2397   \n",
              "568          0.04362        0.00000             0.00000        0.1587   \n",
              "\n",
              "    mean fractal dimension  ... worst texture worst perimeter worst area  \\\n",
              "0                  0.07871  ...         17.33          184.60     2019.0   \n",
              "1                  0.05667  ...         23.41          158.80     1956.0   \n",
              "2                  0.05999  ...         25.53          152.50     1709.0   \n",
              "3                  0.09744  ...         26.50           98.87      567.7   \n",
              "4                  0.05883  ...         16.67          152.20     1575.0   \n",
              "..                     ...  ...           ...             ...        ...   \n",
              "564                0.05623  ...         26.40          166.10     2027.0   \n",
              "565                0.05533  ...         38.25          155.00     1731.0   \n",
              "566                0.05648  ...         34.12          126.70     1124.0   \n",
              "567                0.07016  ...         39.42          184.60     1821.0   \n",
              "568                0.05884  ...         30.37           59.16      268.6   \n",
              "\n",
              "    worst smoothness worst compactness worst concavity worst concave points  \\\n",
              "0            0.16220           0.66560          0.7119               0.2654   \n",
              "1            0.12380           0.18660          0.2416               0.1860   \n",
              "2            0.14440           0.42450          0.4504               0.2430   \n",
              "3            0.20980           0.86630          0.6869               0.2575   \n",
              "4            0.13740           0.20500          0.4000               0.1625   \n",
              "..               ...               ...             ...                  ...   \n",
              "564          0.14100           0.21130          0.4107               0.2216   \n",
              "565          0.11660           0.19220          0.3215               0.1628   \n",
              "566          0.11390           0.30940          0.3403               0.1418   \n",
              "567          0.16500           0.86810          0.9387               0.2650   \n",
              "568          0.08996           0.06444          0.0000               0.0000   \n",
              "\n",
              "    worst symmetry worst fractal dimension target  \n",
              "0           0.4601                 0.11890      0  \n",
              "1           0.2750                 0.08902      0  \n",
              "2           0.3613                 0.08758      0  \n",
              "3           0.6638                 0.17300      0  \n",
              "4           0.2364                 0.07678      0  \n",
              "..             ...                     ...    ...  \n",
              "564         0.2060                 0.07115      0  \n",
              "565         0.2572                 0.06637      0  \n",
              "566         0.2218                 0.07820      0  \n",
              "567         0.4087                 0.12400      0  \n",
              "568         0.2871                 0.07039      1  \n",
              "\n",
              "[569 rows x 31 columns]"
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.30010</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>...</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.16220</td>\n",
              "      <td>0.66560</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.08690</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>...</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.12380</td>\n",
              "      <td>0.18660</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.19740</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>...</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.14440</td>\n",
              "      <td>0.42450</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.24140</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>...</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.20980</td>\n",
              "      <td>0.86630</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.19800</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>...</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.13740</td>\n",
              "      <td>0.20500</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</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",
              "      <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>564</th>\n",
              "      <td>21.56</td>\n",
              "      <td>22.39</td>\n",
              "      <td>142.00</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>0.11100</td>\n",
              "      <td>0.11590</td>\n",
              "      <td>0.24390</td>\n",
              "      <td>0.13890</td>\n",
              "      <td>0.1726</td>\n",
              "      <td>0.05623</td>\n",
              "      <td>...</td>\n",
              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.07115</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>20.13</td>\n",
              "      <td>28.25</td>\n",
              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
              "      <td>0.10340</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.09791</td>\n",
              "      <td>0.1752</td>\n",
              "      <td>0.05533</td>\n",
              "      <td>...</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.11660</td>\n",
              "      <td>0.19220</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>0.2572</td>\n",
              "      <td>0.06637</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>16.60</td>\n",
              "      <td>28.08</td>\n",
              "      <td>108.30</td>\n",
              "      <td>858.1</td>\n",
              "      <td>0.08455</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>0.09251</td>\n",
              "      <td>0.05302</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>0.05648</td>\n",
              "      <td>...</td>\n",
              "      <td>34.12</td>\n",
              "      <td>126.70</td>\n",
              "      <td>1124.0</td>\n",
              "      <td>0.11390</td>\n",
              "      <td>0.30940</td>\n",
              "      <td>0.3403</td>\n",
              "      <td>0.1418</td>\n",
              "      <td>0.2218</td>\n",
              "      <td>0.07820</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>20.60</td>\n",
              "      <td>29.33</td>\n",
              "      <td>140.10</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>0.11780</td>\n",
              "      <td>0.27700</td>\n",
              "      <td>0.35140</td>\n",
              "      <td>0.15200</td>\n",
              "      <td>0.2397</td>\n",
              "      <td>0.07016</td>\n",
              "      <td>...</td>\n",
              "      <td>39.42</td>\n",
              "      <td>184.60</td>\n",
              "      <td>1821.0</td>\n",
              "      <td>0.16500</td>\n",
              "      <td>0.86810</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.2650</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.12400</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>7.76</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>...</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>569 rows × 31 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-456b51ca-0769-4db8-acdd-213afd11f05b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
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              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-456b51ca-0769-4db8-acdd-213afd11f05b 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-456b51ca-0769-4db8-acdd-213afd11f05b');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "def answer_one():\n",
        "    # convert the cancer data into dataframe\n",
        "    df = pd.DataFrame(data=cancer.data, columns=[cancer.feature_names])\n",
        "    # add target column to the dataframe\n",
        "    df['target'] = cancer.target\n",
        "    \n",
        "    return df\n",
        "\n",
        "answer_one()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8e4add0a-d9d3-4052-bcf2-56b61c4bf53f",
      "metadata": {
        "id": "8e4add0a-d9d3-4052-bcf2-56b61c4bf53f"
      },
      "source": [
        "### Question 2\n",
        "What is the class distribution? (i.e. how many instances of `malignant` and how many `benign`?)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "514559d7-5afe-4e40-aca8-c257dca18a5f",
      "metadata": {
        "id": "514559d7-5afe-4e40-aca8-c257dca18a5f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e23c400-f53e-44c9-cb86-b5b51ca98130"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(target,)\n",
              "benign       357\n",
              "malignant    212\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "def answer_two():\n",
        "\n",
        "    # load the dataframe\n",
        "    cancer_df = answer_one()\n",
        "    # count the number of malignant and benign samples\n",
        "    count = cancer_df['target'].value_counts()\n",
        "    # rename the label from 0 and 1 to malignant and benign\n",
        "    count = count.rename(index={0:'malignant', 1:'benign'})\n",
        "\n",
        "    return count\n",
        "\n",
        "answer_two()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5c8cc5ea-3c4b-45aa-98a3-255d5b2a1b77",
      "metadata": {
        "id": "5c8cc5ea-3c4b-45aa-98a3-255d5b2a1b77"
      },
      "source": [
        "### Question 3\n",
        "Split the DataFrame into `X` (the data) and `y` (the labels)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "44a936d7-b539-4311-a137-d0304610dd09",
      "metadata": {
        "id": "44a936d7-b539-4311-a137-d0304610dd09"
      },
      "outputs": [],
      "source": [
        "def answer_three():\n",
        "  \n",
        "    # load the dataframe\n",
        "    cancer_df = answer_one()\n",
        "    # drop the target column from the dataframe to get the features only\n",
        "    X = cancer_df.drop(['target'], axis=1)\n",
        "    # get the target column\n",
        "    y = cancer_df['target']\n",
        "    \n",
        "    return X, y"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0f3336f9-9517-4a9a-91ca-fba2bb25d6dc",
      "metadata": {
        "id": "0f3336f9-9517-4a9a-91ca-fba2bb25d6dc"
      },
      "source": [
        "### Question 4\n",
        "Using `train_test_split`, split `X` and `y` into training and test sets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "180b69e2-b4cb-488f-909e-79cee866fa4f",
      "metadata": {
        "id": "180b69e2-b4cb-488f-909e-79cee866fa4f"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "def answer_four():\n",
        "  \n",
        "    X, y = answer_three()\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "    \n",
        "    return X_train, X_test, y_train, y_test"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bcb50095-8bec-4d75-9160-8338f9801dae",
      "metadata": {
        "id": "bcb50095-8bec-4d75-9160-8338f9801dae"
      },
      "source": [
        "### Question 5\n",
        "Using KNeighborsClassifier, fit a k-nearest neighbors (knn) classifier with `X_train`, `y_train`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a6e5acd3-af98-4ab7-b309-40b319f9bf33",
      "metadata": {
        "id": "a6e5acd3-af98-4ab7-b309-40b319f9bf33"
      },
      "outputs": [],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "def answer_five():\n",
        "    X_train, X_test, y_train, y_test = answer_four()\n",
        "    \n",
        "    # create a knn classifier with 5 neighbors\n",
        "    knn = KNeighborsClassifier()\n",
        "    # fit the classifier to the training data\n",
        "    knn.fit(X_train, y_train)\n",
        "    \n",
        "    return knn"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "55dbd498-cb23-4cab-9138-c4b7e7db82c1",
      "metadata": {
        "id": "55dbd498-cb23-4cab-9138-c4b7e7db82c1"
      },
      "source": [
        "### Question 6\n",
        "Using your knn classifier, predict the class label using the median value for each feature."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e6d989ab-6fd5-440a-933f-b6c6ff1072b4",
      "metadata": {
        "id": "e6d989ab-6fd5-440a-933f-b6c6ff1072b4"
      },
      "outputs": [],
      "source": [
        "def answer_six():\n",
        "    cancerdf = answer_one()\n",
        "        \n",
        "    # drop the target column from the dataframe to get the features only\n",
        "    cancer_df = cancer_df.drop(['target'], axis=1)\n",
        "    # get the median value for each feature\n",
        "    new_data = np.median(cancer_df, axis=0).reshape(1, -1)\n",
        "    # predict the class label using the knn classifier\n",
        "    knn = answer_five()\n",
        "    new_label = knn.predict(new_data)\n",
        "\n",
        "    return new_label"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "07065db3-e464-473f-ab71-dbfeeee097f6",
      "metadata": {
        "id": "07065db3-e464-473f-ab71-dbfeeee097f6"
      },
      "source": [
        "### Question 7\n",
        "Using your knn classifier, predict the class labels for the test set `X_test`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8f4b7833-53ee-4a3d-8dea-ad654cf3d47f",
      "metadata": {
        "id": "8f4b7833-53ee-4a3d-8dea-ad654cf3d47f"
      },
      "outputs": [],
      "source": [
        "def answer_seven():\n",
        "    X_train, X_test, y_train, y_test = answer_four()\n",
        "    knn = answer_five()\n",
        "\n",
        "    # predict the class labels for the test set\n",
        "    y_pred = knn.predict(X_test)\n",
        "    \n",
        "    return y_pred"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "42288a73-b635-402d-8c26-229dc49c79f8",
      "metadata": {
        "id": "42288a73-b635-402d-8c26-229dc49c79f8"
      },
      "source": [
        "### Question 8\n",
        "Find the score (accuracy) of your knn classifier using `X_test` and `y_test` & `X_train` and `y_train`. \n",
        "- Is the training accuracy higher or lower that the testing accuracy? \n",
        "- According to the test accuracy, would this model generalize well to new inputs?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "27fa57dd-8326-4fe7-9110-4b5ba3d921fe",
      "metadata": {
        "id": "27fa57dd-8326-4fe7-9110-4b5ba3d921fe"
      },
      "outputs": [],
      "source": [
        "def answer_eight():\n",
        "    X_train, X_test, y_train, y_test = answer_four()\n",
        "    knn = answer_five()\n",
        "    \n",
        "    # calculate the accuracy of the classifier on the training data\n",
        "    train_accuracy = knn.score(X_train, y_train)\n",
        "\n",
        "    # calculate the accuracy of the classifier on the test data\n",
        "    test_accuracy = knn.score(X_test, y_test)\n",
        "    \n",
        "    return # Return your answer"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cb4d1ae2-967b-4df0-9bf0-2872b0c53535",
      "metadata": {
        "id": "cb4d1ae2-967b-4df0-9bf0-2872b0c53535"
      },
      "source": [
        "### Question 9\n",
        "##### Accuracy Plot\n",
        "* Create `mal_train_X`, `mal_train_y`,`ben_train_X`,`ben_train_y`, `mal_test_X`,`mal_test_y`, `ben_test_X` and `ben_test_y` portions of the dataset. Where `mal_train_X` contains all training samples that belongs to malignant class while `mal_train_y` contains all labels of the samples that belongs to malignant class, and so on for other variables.\n",
        "\n",
        "* Calculate Accuracy scores for each case and save all the results in a list called `scores` using knn model from question 5.\n",
        "\n",
        "Try using the plotting function below to visualize the differet predicition scores between training and test sets, as well as malignant and benign cells."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "830729b6-9aa7-46c5-8cd1-04a3a7408f3a",
      "metadata": {
        "id": "830729b6-9aa7-46c5-8cd1-04a3a7408f3a"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "def accuracy_plot():\n",
        "\n",
        "    %matplotlib inline\n",
        "\n",
        "    X_train, X_test, y_train, y_test = answer_four()\n",
        "\n",
        "    # Find the training and testing accuracies by target value (i.e. malignant, benign)\n",
        "    \n",
        "\n",
        "    y_train = np.array(y_train)\n",
        "    y_test = np.array(y_test)\n",
        "\n",
        "    mal_train_X = X_train[y_train==[0]]\n",
        "    mal_train_y = y_train[y_train==[0]]\n",
        "    ben_train_X = X_train[y_train==[1]]\n",
        "    ben_train_y = y_train[y_train==[1]]\n",
        "\n",
        "    mal_test_X = X_test[y_test==[0]]\n",
        "    mal_test_y = y_test[y_test==[0]]\n",
        "    ben_test_X = X_test[y_test==[1]]\n",
        "    ben_test_y = y_test[y_test==[1]]\n",
        "\n",
        "    mal_train_X = X_train[y_train==0]\n",
        "    mal_train_y = y_train[y_train==0]\n",
        "    ben_train_X = X_train[y_train==1]\n",
        "    ben_train_y = y_train[y_train==1]\n",
        "\n",
        "    mal_test_X = X_test[y_test==0]\n",
        "    mal_test_y = y_test[y_test==0]\n",
        "    ben_test_X = X_test[y_test==1]\n",
        "    ben_test_y = y_test[y_test==1]\n",
        "\n",
        "    knn = answer_five()\n",
        "\n",
        "    scores = [\n",
        "        knn.score(mal_train_X, mal_train_y),\n",
        "        knn.score(ben_train_X, ben_train_y),\n",
        "        knn.score(mal_test_X, mal_test_y),\n",
        "        knn.score(ben_test_X, ben_test_y)\n",
        "    ]\n",
        "    \n",
        "\n",
        "    plt.figure()\n",
        "\n",
        "    # Plot the scores as a bar chart\n",
        "    bars = plt.bar(np.arange(4), scores, color=['#4c72b0','#4c72b0','#55a868','#55a868'])\n",
        "\n",
        "    # directly label the score onto the bars\n",
        "    for bar in bars:\n",
        "        height = bar.get_height()\n",
        "        plt.gca().text(bar.get_x() + bar.get_width()/2, height*.90, '{0:.{1}f}'.format(height, 2), \n",
        "                     ha='center', color='w', fontsize=11)\n",
        "\n",
        "    # remove all the ticks (both axes), and tick labels on the Y axis\n",
        "    plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='off', labelbottom='on')\n",
        "\n",
        "    # remove the frame of the chart\n",
        "    for spine in plt.gca().spines.values():\n",
        "        spine.set_visible(False)\n",
        "\n",
        "    plt.xticks([0,1,2,3], ['Malignant\\nTraining', 'Benign\\nTraining', 'Malignant\\nTest', 'Benign\\nTest'], alpha=0.8);\n",
        "    plt.title('Training and Test Accuracies for Malignant and Benign Cells', alpha=0.8)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "75e16042-fcea-4769-87cb-3d289ebcd479",
      "metadata": {
        "id": "75e16042-fcea-4769-87cb-3d289ebcd479",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "outputId": "8b24c68b-462e-4c23-89c6-00e2a4fe8164"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/pandas/core/generic.py:4150: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n",
            "  obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/utils/validation.py:1688: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['tuple']. An error will be raised in 1.2.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/utils/validation.py:1688: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['tuple']. An error will be raised in 1.2.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/utils/validation.py:1688: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['tuple']. An error will be raised in 1.2.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/utils/validation.py:1688: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['tuple']. An error will be raised in 1.2.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/utils/validation.py:1688: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['tuple']. An error will be raised in 1.2.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "accuracy_plot() "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d3dda16d-0a7f-4376-a1e6-a82ca8b8e6eb",
      "metadata": {
        "id": "d3dda16d-0a7f-4376-a1e6-a82ca8b8e6eb"
      },
      "source": [
        "### Question 10\n",
        "1. Load Iris dataset from sklearn.\n",
        "2. Split the data into training and testing (30% testing)\n",
        "3. Create scatter plot.\n",
        "4. From the scatter plot choose two features only to train a knn model with suitable \"k\" of your choice.\n",
        "5. Evaluate your model performance and comment on your results."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8a8d4298",
      "metadata": {
        "id": "8a8d4298",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "outputId": "452f292b-11b5-4148-dc3e-999454653237"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'.. _iris_dataset:\\n\\nIris plants dataset\\n--------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 150 (50 in each of three classes)\\n    :Number of Attributes: 4 numeric, predictive attributes and the class\\n    :Attribute Information:\\n        - sepal length in cm\\n        - sepal width in cm\\n        - petal length in cm\\n        - petal width in cm\\n        - class:\\n                - Iris-Setosa\\n                - Iris-Versicolour\\n                - Iris-Virginica\\n                \\n    :Summary Statistics:\\n\\n    ============== ==== ==== ======= ===== ====================\\n                    Min  Max   Mean    SD   Class Correlation\\n    ============== ==== ==== ======= ===== ====================\\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\\n    ============== ==== ==== ======= ===== ====================\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: 33.3% for each of 3 classes.\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\nfrom Fisher\\'s paper. Note that it\\'s the same as in R, but not as in the UCI\\nMachine Learning Repository, which has two wrong data points.\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\n.. topic:: References\\n\\n   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n     Mathematical Statistics\" (John Wiley, NY, 1950).\\n   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n     Structure and Classification Rule for Recognition in Partially Exposed\\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n     on Information Theory, May 1972, 431-433.\\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n     conceptual clustering system finds 3 classes in the data.\\n   - Many, many more ...'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 81
        }
      ],
      "source": [
        "from sklearn.datasets import load_iris\n",
        "\n",
        "iris = load_iris()\n",
        "iris.DESCR"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert the cancer data into dataframe\n",
        "iris_df = pd.DataFrame(data=iris.data, columns=[iris.feature_names])\n",
        "# add target column to the dataframe\n",
        "iris_df['target'] = iris.target"
      ],
      "metadata": {
        "id": "4Wz9PhgbLaAe"
      },
      "id": "4Wz9PhgbLaAe",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_iris = iris_df.drop(axis=1,columns='target')\n",
        "y_iris = iris_df['target']\n",
        "\n",
        "iris_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "YuoKirtDLqto",
        "outputId": "c7dc2ecb-cb99-407d-c96b-7bd802f04b8b"
      },
      "id": "YuoKirtDLqto",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/pandas/core/generic.py:4150: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n",
            "  obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)  \\\n",
              "0                 5.1              3.5               1.4              0.2   \n",
              "1                 4.9              3.0               1.4              0.2   \n",
              "2                 4.7              3.2               1.3              0.2   \n",
              "3                 4.6              3.1               1.5              0.2   \n",
              "4                 5.0              3.6               1.4              0.2   \n",
              "..                ...              ...               ...              ...   \n",
              "145               6.7              3.0               5.2              2.3   \n",
              "146               6.3              2.5               5.0              1.9   \n",
              "147               6.5              3.0               5.2              2.0   \n",
              "148               6.2              3.4               5.4              2.3   \n",
              "149               5.9              3.0               5.1              1.8   \n",
              "\n",
              "    target  \n",
              "0        0  \n",
              "1        0  \n",
              "2        0  \n",
              "3        0  \n",
              "4        0  \n",
              "..     ...  \n",
              "145      2  \n",
              "146      2  \n",
              "147      2  \n",
              "148      2  \n",
              "149      2  \n",
              "\n",
              "[150 rows x 5 columns]"
            ],
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              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e32b7e78-dd0c-4176-81cf-a47655980e80')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e32b7e78-dd0c-4176-81cf-a47655980e80 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-e32b7e78-dd0c-4176-81cf-a47655980e80');\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": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_iris, X_test_iris, y_train_iris, y_test_iris = train_test_split(X_iris,y_iris,test_size=0.3)"
      ],
      "metadata": {
        "id": "f72TPs1qLvlo"
      },
      "id": "f72TPs1qLvlo",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scatter_plt = pd.plotting.scatter_matrix(X_train_iris,c=y_train_iris,marker='o',s=40,hist_kwds={'bins':15},figsize=(10,10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "ljmzP_jOL0Hv",
        "outputId": "d4d6c061-6b7e-4cab-cc78-b154fba4f188"
      },
      "id": "ljmzP_jOL0Hv",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris_df = iris_df.drop(axis=1,columns='sepal length (cm)')\n",
        "iris_df = iris_df.drop(axis=1,columns='sepal width (cm)')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MNl_wS1ZL5eG",
        "outputId": "61267c9e-2daf-4986-a603-b3edf7fa1502"
      },
      "id": "MNl_wS1ZL5eG",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/pandas/core/generic.py:4150: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n",
            "  obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_iris = iris_df.drop(axis=1,columns='target')\n",
        "y_iris = iris_df['target']\n",
        "X_train_iris, X_test_iris, y_train_iris, y_test_iris = train_test_split(X_iris,y_iris,test_size=0.3)"
      ],
      "metadata": {
        "id": "SMJeRlVVL-AO"
      },
      "id": "SMJeRlVVL-AO",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "iris_model = KNeighborsClassifier(n_neighbors=5)\n",
        "iris_model.fit(X_train_iris,y_train_iris)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OvWFEa0LL94k",
        "outputId": "01baaf17-c2e0-4197-fd4d-7f95c794f7b1"
      },
      "id": "OvWFEa0LL94k",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/utils/validation.py:1688: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['tuple']. An error will be raised in 1.2.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_prediction = iris_model.predict(X_train_iris)\n",
        "y_test_prediction = iris_model.predict(X_test_iris)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "56F3eblkMMio",
        "outputId": "f6abfe50-2060-4299-f9cc-2dc786d563e7"
      },
      "id": "56F3eblkMMio",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/utils/validation.py:1688: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['tuple']. An error will be raised in 1.2.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/utils/validation.py:1688: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['tuple']. An error will be raised in 1.2.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'train accuarcy:{accuracy_score(y_train_iris,y_train_prediction)}')\n",
        "print(f'test accuarcy:{accuracy_score(y_test_iris,y_test_prediction)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RiI-yJJoMYRq",
        "outputId": "42d8d659-2d83-41f9-925c-787ef2c8311a"
      },
      "id": "RiI-yJJoMYRq",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train accuarcy:0.9619047619047619\n",
            "test accuarcy:0.9777777777777777\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Comments"
      ],
      "metadata": {
        "id": "PC3H0h4UMxRx"
      },
      "id": "PC3H0h4UMxRx"
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "#### Choosing The 2 Features\n",
        "I choose these two features because in they have better correlation shown in the scatter plot and also shown in the description of the data set\n",
        "\n",
        "\n",
        "#### Acuraccy scores\n",
        "The Training Acurracy is 96.1% while the testing accuracy is 97.7. there is no big differnce in both accuracies which means our model has no overfitting and general for most of inputs and also our data is repesting the population well."
      ],
      "metadata": {
        "id": "gJiQr-U3M4pU"
      },
      "id": "gJiQr-U3M4pU"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MtCcGZwWOxfO"
      },
      "id": "MtCcGZwWOxfO",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "0f3336f9-9517-4a9a-91ca-fba2bb25d6dc",
        "bcb50095-8bec-4d75-9160-8338f9801dae",
        "55dbd498-cb23-4cab-9138-c4b7e7db82c1",
        "07065db3-e464-473f-ab71-dbfeeee097f6",
        "42288a73-b635-402d-8c26-229dc49c79f8"
      ]
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.10"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}