MLLearningWithPython.ipynb

{
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   "source": [
    "**Book Reading:**\n",
    "\n",
    "---\n",
    "\n",
    "Serialization or self study of the Book Introduction to Machine Learning with Python,\n",
    "A Guid for Data Sciencetists. Coding excercses are those found in the book chapters.\n",
    "by Andreas C. Müller & Sarah Guido. \n",
    "\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32218655-4a09-4f49-864c-5c3743e7d3fe",
   "metadata": {},
   "source": [
    "**NumPy**\n",
    "\n",
    "NumPy is one of the fundamental packages for scientific computing in Python. It\n",
    "contains functionality for multidimensional arrays, high-level mathematical func‐\n",
    "tions such as linear algebra operations and the Fourier transform, and pseudorandom\n",
    "number generators.\n",
    "\n",
    "In scikit-learn, the NumPy array is the fundamental data structure. scikit-learn\n",
    "takes in data in the form of NumPy arrays. Any data you’re using will have to be con‐\n",
    "verted to a NumPy array. The core functionality of NumPy is the ndarray class, a\n",
    "multidimensional (n-dimensional) array. All elements of the array must be of the\n",
    "same type. A NumPy array looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "deeaa214-a4f2-4143-8420-a23e00453298",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x:[[4 5 6]\n",
      " [7 8 9]]\n"
     ]
    }
   ],
   "source": [
    "# Chap 1 Introduction to Libraries\n",
    "\n",
    "# to use numpy\n",
    "\n",
    "import numpy as np\n",
    "# create 2 arrays\n",
    "x = np.array([[4,5,6],[7,8,9]])\n",
    "print(\"x:{}\".format(x))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "215b56a6-7748-46d5-aefb-692d33631ce8",
   "metadata": {},
   "source": [
    "\n",
    "---\n",
    "\n",
    "**SciPy**\n",
    "\n",
    "SciPy is a collection of functions for scientific computing in Python. It provides,\n",
    "among other functionality, advanced linear algebra routines, mathematical function\n",
    "optimization, signal processing, special mathematical functions, and statistical distri‐\n",
    "butions. scikit-learn draws from SciPy’s collection of functions for implementing\n",
    "its algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "acf207b9-953c-4984-adbd-12e1653a231e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numpy array:\n",
      "[[1. 0. 0. 0.]\n",
      " [0. 1. 0. 0.]\n",
      " [0. 0. 1. 0.]\n",
      " [0. 0. 0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "from scipy import sparse  # scipy uses sparse matrices\n",
    "import numpy as np\n",
    "\n",
    "eye = np.eye(4) # Create a 2D NumPy array with a diagonal of ones, and zeros everywhere else\n",
    "print(\"Numpy array:\\n{}\".format(eye))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "09cc5440-4152-4ff2-b49c-7dcee0b1d3fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SciPy sparse CSR matrix:\n",
      "  (0, 0)\t1.0\n",
      "  (1, 1)\t1.0\n",
      "  (2, 2)\t1.0\n",
      "  (3, 3)\t1.0\n"
     ]
    }
   ],
   "source": [
    "# Convert the NumPy array to a SciPy sparse matrix in CSR format # Only the nonzero entries are \n",
    "# stored \n",
    "sparse_matrix = sparse.csc_matrix(eye)\n",
    "print(\"\\nSciPy sparse CSR matrix:\\n{}\".format(sparse_matrix))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fea25b7b-30d2-47a1-92c4-d96cfff96636",
   "metadata": {},
   "source": [
    "Here is a  way to create the same sparse matrix as before, using the COO formatHere is a  way to create the same sparse matrix as before, using the COO format.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6c139975-76b5-40d4-8822-e23ab07445a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "COO representation:\n",
      "  (0, 0)\t1.0\n",
      "  (1, 1)\t1.0\n",
      "  (2, 2)\t1.0\n",
      "  (3, 3)\t1.0\n"
     ]
    }
   ],
   "source": [
    "dt = np.ones(4)\n",
    "row_indices = np.arange(4) \n",
    "col_indices = np.arange(4) \n",
    "eye_coo = sparse.coo_matrix((dt, (row_indices, col_indices))) \n",
    "print(\"COO representation:\\n{}\".format(eye_coo))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08b45afb-b922-4993-aa10-71c763dea5dc",
   "metadata": {},
   "source": [
    "\n",
    "---\n",
    "\n",
    "**Matplotlib**  \n",
    "matplotlib is the primary scientific plotting library in Python. It provides functions  for making publication-quality visualizations such as line charts, histograms and scatter  plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8a294502-6032-4573-beb6-264756289d0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb3adfb25d0>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# generate data -10 to 10 with a space of 100 steps\n",
    "x = np.linspace(-10,10,100)\n",
    "\n",
    "# create a second array with cos <cosine>\n",
    "y = np.cos(x)\n",
    "\n",
    "# plot the function\n",
    "plt.plot(x,y, marker=\"x\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "577863fc-d975-4942-b6dc-f911351c076b",
   "metadata": {},
   "source": [
    "\n",
    "---\n",
    "\n",
    "**Pandas**\n",
    "\n",
    "Pandas allows data wrangling and analysis. It is built around a data function called DataFrame. pandas provides a great  range of methods to modify and operate on this table; in particular, it allows SQL-like  queries and joins of tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7a6ad96e-cd4c-4805-99cf-7b8da231850a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andrew</td>\n",
       "      <td>New york</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ann</td>\n",
       "      <td>Accra</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Roger</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Peter</td>\n",
       "      <td>Osaka</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kelly</td>\n",
       "      <td>Dubai</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Anita</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Location  Age\n",
       "0  Andrew  New york   21\n",
       "1     Ann     Accra   56\n",
       "2   Roger    Moscow   33\n",
       "3   Peter     Osaka   45\n",
       "4   Kelly     Dubai   41\n",
       "5   Anita     Delhi   26"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# create a sample dataset of people, cities and age\n",
    "\n",
    "\n",
    "dt = { 'Name':['Andrew','Ann','Roger','Peter','Kelly','Anita'],\n",
    "      'Location': ['New york','Accra','Moscow','Osaka','Dubai','Delhi'],\n",
    "      'Age': [21,56,33,45,41,26]\n",
    "\n",
    "    \n",
    "}\n",
    "# create a dataframe and display a presentable table\n",
    "dt_pandas = pd.DataFrame(dt)\n",
    "display(dt_pandas)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fe80fe2-4dd6-4944-accb-5654553f262f",
   "metadata": {},
   "source": [
    "There are various way one can query this table:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dc9134d9-d8ba-4599-99f4-974f3222434b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andrew</td>\n",
       "      <td>New york</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Roger</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Anita</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Location  Age\n",
       "0  Andrew  New york   21\n",
       "2   Roger    Moscow   33\n",
       "5   Anita     Delhi   26"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(dt_pandas[dt_pandas.Age < 35]) # where age is less than 30 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "232c7cda-98ad-4130-a368-d937b0bdec5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ann</td>\n",
       "      <td>Accra</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Peter</td>\n",
       "      <td>Osaka</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kelly</td>\n",
       "      <td>Dubai</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name Location  Age\n",
       "1    Ann    Accra   56\n",
       "3  Peter    Osaka   45\n",
       "4  Kelly    Dubai   41"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(dt_pandas[dt_pandas.Age >35]) # where age is greater than 30 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "70a8d82c-2184-462f-8be4-ceec11b67a7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: scikit-learn in /home/x89max/.local/lib/python3.13/site-packages (1.5.2)\n",
      "Requirement already satisfied: numpy>=1.19.5 in /usr/lib64/python3.13/site-packages (from scikit-learn) (1.26.4)\n",
      "Requirement already satisfied: scipy>=1.6.0 in /usr/lib64/python3.13/site-packages (from scikit-learn) (1.11.3)\n",
      "Requirement already satisfied: joblib>=1.2.0 in /home/x89max/.local/lib/python3.13/site-packages (from scikit-learn) (1.4.2)\n",
      "Requirement already satisfied: threadpoolctl>=3.1.0 in /home/x89max/.local/lib/python3.13/site-packages (from scikit-learn) (3.5.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install scikit-learn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "15260ab1-c8bb-46b1-a1cd-610b79d5bfb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python version: 3.13.0 (main, Oct  8 2024, 00:00:00) [GCC 14.2.1 20240912 (Red Hat 14.2.1-3)]\n",
      "pandas version: 2.2.1\n",
      "matplotlib version: 3.9.1\n",
      "NumPy version: 1.26.4\n",
      "SciPy version: 1.11.3\n",
      "IPython version: 8.26.0\n",
      "scikit-learn version: 1.5.2\n"
     ]
    }
   ],
   "source": [
    "import sys  \n",
    "print(\"Python version: {}\".format(sys.version))\n",
    "import pandas as pd  \n",
    "print(\"pandas version: {}\".format(pd.__version__))  \n",
    "import matplotlib  \n",
    "print(\"matplotlib version: {}\".format(matplotlib.__version__))  \n",
    "import numpy as np  \n",
    "print(\"NumPy version: {}\".format(np.__version__))  \n",
    "import scipy as sp  \n",
    "print(\"SciPy version: {}\".format(sp.__version__))  \n",
    "import IPython  \n",
    "print(\"IPython version: {}\".format(IPython.__version__))  \n",
    "import sklearn  \n",
    "print(\"scikit-learn version: {}\".format(sklearn.__version__))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b16a0bc-49b7-4fb7-a620-a0d08f733b47",
   "metadata": {},
   "source": [
    "\n",
    "---\n",
    "\n",
    "**Version**\n",
    "Checking library versions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "365e3245-70ce-4892-a91e-dbfde1e84c78",
   "metadata": {},
   "source": [
    "\n",
    "---\n",
    "\n",
    "Figure 1.1 Parts of the iris flower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4e0fd7e3-1aa8-44ba-addf-fd67bce910da",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "\n",
    "# open the flower image\n",
    "iris_img = Image.open('partsofirisflower.png')\n",
    "\n",
    "# show image \n",
    "iris_img.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87ae9b5e-03a6-4ff6-9072-4fa86062dea4",
   "metadata": {},
   "source": [
    "\n",
    "---\n",
    "\n",
    "**Iris Example**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "287c6669-a3fd-4654-81c1-0fe2c94cc911",
   "metadata": {},
   "source": [
    "Lets use the iris dataset included in the scikit-learn library modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "24712951-db45-4e52-82f0-94da5a2b65c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keys of iris_dataset: \n",
      "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris # importing the dataset iris\n",
    "iris_dts = load_iris()\n",
    "\n",
    "print(\"Keys of iris_dataset: \\n{}\".format(iris_dts.keys()))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6945e99-18f6-4450-802d-eccd665c56c6",
   "metadata": {},
   "source": [
    "The value of the key DESCR is a short description of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1fb843e8-f67b-4bc0-b38a-c0457f35e11d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _iris_dataset:\n",
      "\n",
      "Iris 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 \n",
      "...\n"
     ]
    }
   ],
   "source": [
    "# key description\n",
    "print(iris_dts['DESCR'][:193] + \"\\n...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dd97b68-6b74-4393-aca1-2040f18fce12",
   "metadata": {},
   "source": [
    "**Overview of the Iris Dataset**\n",
    "**Number of Instances (Samples): 150**\n",
    "\n",
    "The dataset contains 150 instances, which are individual samples of iris flowers.\n",
    "These 150 samples are equally divided into three classes, each containing 50 samples.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28c2cd02-0a75-4004-a48c-3c6f25b5beff",
   "metadata": {},
   "source": [
    "**Types of Target Names**\n",
    "**Target Classes (Labels)**\n",
    "The dataset contains three classes of iris plants:\n",
    "\n",
    "1. Iris-setosa\n",
    "2. Iris-versicolor\n",
    "3. Iris-virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "32211ffb-ebe0-4911-a699-9eba1b415600",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target Names: ['setosa' 'versicolor' 'virginica']\n"
     ]
    }
   ],
   "source": [
    "print(\"Target Names: {}\".format(iris_dts['target_names']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ab0e54e-0d61-4497-af14-6ea2df4cf183",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "**Feature Names**\n",
    "**Attributes (Features)**\n",
    "\n",
    "The four features are:\n",
    "\n",
    "1. Sepal Length (cm): A numerical value representing the length of the sepal in centimeters.\n",
    "2. Sepal Width (cm): A numerical value representing the width of the sepal in centimeters.\n",
    "3. Petal Length (cm): A numerical value representing the length of the petal in centimeters.\n",
    "4. Petal Width (cm): A numerical value representing the width of the petal in centimeters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1006fde0-4474-44de-b6d7-25d393e08fa1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature names: \n",
      " ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
     ]
    }
   ],
   "source": [
    "print(\"Feature names: \\n {}\".format(iris_dts['feature_names']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bb2d552-112a-47b2-ae2f-02e158bacc43",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "**Iris DataSet Type**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2f59a25d-c5f1-4421-8839-fb6d582446d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type of data: <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(\"Type of data: {}\".format(type(iris_dts['data'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "39e3441e-d489-4f7e-93d3-9ff1b1716d3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type of data: (150, 4)\n"
     ]
    }
   ],
   "source": [
    "print(\"Type of data: {}\".format(iris_dts['data'].shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a3e4a6c-3c43-415b-a1d8-1a21500a7097",
   "metadata": {},
   "source": [
    "We see that the array contains measurements for 150 different flowers. Remember  that the individual items are called samples in machine learning, and their properties  are called features. The shape of the data array is the number of samples multiplied by  the number of features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "19601457-5424-4dcd-ba99-4c0829145f45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First five columns of data:\n",
      "[[5.1 3.5 1.4 0.2]\n",
      " [4.9 3.  1.4 0.2]\n",
      " [4.7 3.2 1.3 0.2]\n",
      " [4.6 3.1 1.5 0.2]\n",
      " [5.  3.6 1.4 0.2]\n",
      " [5.4 3.9 1.7 0.4]\n",
      " [4.6 3.4 1.4 0.3]\n",
      " [5.  3.4 1.5 0.2]\n",
      " [4.4 2.9 1.4 0.2]\n",
      " [4.9 3.1 1.5 0.1]]\n"
     ]
    }
   ],
   "source": [
    "print(\"First five columns of data:\\n{}\".format(iris_dts['data'][:10]) )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbaea95d-d69d-4746-b08a-53b72f4346ca",
   "metadata": {},
   "source": [
    "From this data, we can see that all of the first five flowers have a petal width of 0.2 cm  and that the first flower has the longest sepal, at 5.1 cm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b05f4806-685b-4090-84c6-b3822cc5e2fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First five columns of data:\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(\"First five columns of data:\\n{}\".format(type(iris_dts['target'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98dbe899-a3cd-430b-a1d5-6abf4eb12136",
   "metadata": {},
   "source": [
    "target is a one-dimensional array, with one entry per flower:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e05efa51-3874-4b02-9b0b-064585587551",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of target: (150,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of target: {}\".format(iris_dts['target'].shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a619b14-733f-48da-90f6-fcbc0cedd80b",
   "metadata": {},
   "source": [
    "The species are encoded as integers from 0 to 2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a0415691-13f3-48c1-9a6f-93a30c62c8f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target: \n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2] \n"
     ]
    }
   ],
   "source": [
    "print(\"Target: \\n{} \". format(iris_dts['target']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6fb61ed-134e-48ab-a982-f452f16c9481",
   "metadata": {},
   "source": [
    "The meanings of the numbers are given by the iris['target_names'] array:  0 means setosa, 1 means versicolor, and 2 means virginica."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cc29d7f-4b3d-4e9b-a8ef-4ffaf37c573f",
   "metadata": {},
   "source": [
    "Let’s call train_test_split on our data and assign the outputs using this nomenclature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7d0bf955-157b-442f-8812-ab741660f94c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test=train_test_split(iris_dts['data'],iris_dts['target'], random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "705ec80c-6c68-436a-839c-75b047eb8709",
   "metadata": {},
   "source": [
    "The output of the train_test_split function is X_train, X_test, y_train, and  y_test, which are all NumPy arrays. X_train contains 75% of the rows of the dataset,  and X_test contains the remaining 25%:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e06f4d41-80d2-478b-b305-957a6e0999a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape:(112, 4)\n",
      "y_train shape:(112,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_train shape:{}\".format(X_train.shape))\n",
    "print(\"y_train shape:{}\".format(y_train.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "aafe5458-1081-4746-8bd3-383fd8114b26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_test shape: (38, 4)\n",
      "y_test shape: (38,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_test shape: {}\".format(X_test.shape)) \n",
    "print(\"y_test shape: {}\".format(y_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fd900e7-bc8b-4df6-959c-ba43f3109346",
   "metadata": {},
   "source": [
    "Data Visualization:\n",
    "To look at how the data, that is to see if the data is easily solveable by machine learning or if the desired informaation might not be contained in the data."
   ]
  }
 ],
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