How To Load Machine Learning Data.ipynb

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    "## Chapter 1. How To Load Machine Learning Data\n",
    "Any Machine Learning problem begins with loading of data into your workspace. The most common formate for Machine Learning data is CSV Files. In this chapter we will see the various techniques for loading CSV files into our workspace.\n",
    "#### Content\n",
    "1. Load CSV Files with the Python Standard Library.\n",
    "2. Load CSV Files with NumPy.\n",
    "3. Load CSV Files with Pandas.\n",
    "\n",
    "### Pima Indians Dataset\n",
    "This dataset describes the medical records for Pima Indians and whether or not each patient will have an onset of diabetes within ﬁve years. This dataset is perfect to start with all the input variables are numericals and it is a problem of binary classification.\n",
    "\n",
    "### 1. Load CSV Files with the Python Standard Library"
   ]
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      "[[  6.    148.     72.    ...   0.627  50.      1.   ]\n",
      " [  1.     85.     66.    ...   0.351  31.      0.   ]\n",
      " [  8.    183.     64.    ...   0.672  32.      1.   ]\n",
      " ...\n",
      " [  5.    121.     72.    ...   0.245  30.      0.   ]\n",
      " [  1.    126.     60.    ...   0.349  47.      1.   ]\n",
      " [  1.     93.     70.    ...   0.315  23.      0.   ]]\n",
      "Shape of DataFrame:  (768, 9)\n"
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    "# Load CSV file using Python Standard Library.\n",
    "\n",
    "# Import python standard library module\n",
    "import csv\n",
    "import numpy as np\n",
    "\n",
    "# Open csv file in read only mode\n",
    "raw_data = open(\"diabetes.csv\", \"r\")\n",
    "\n",
    "# Load the CSV file\n",
    "reader = csv.reader(raw_data, delimiter = \",\")\n",
    "\n",
    "# Skip the first line as the first line is the name of variables\n",
    "next(reader, None)\n",
    "x = list(reader)\n",
    "\n",
    "# Convert datatype to float\n",
    "data = np.array(x).astype('float')\n",
    "\n",
    "# display data\n",
    "print(data)\n",
    "\n",
    "# display data dimension\n",
    "print(\"Shape of DataFrame: \", data.shape)"
   ]
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    "The resultant data is numpy array.\n",
    "### 2.  Load CSV Files with NumPy"
   ]
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      "[[  6.    148.     72.    ...   0.627  50.      1.   ]\n",
      " [  1.     85.     66.    ...   0.351  31.      0.   ]\n",
      " [  8.    183.     64.    ...   0.672  32.      1.   ]\n",
      " ...\n",
      " [  5.    121.     72.    ...   0.245  30.      0.   ]\n",
      " [  1.    126.     60.    ...   0.349  47.      1.   ]\n",
      " [  1.     93.     70.    ...   0.315  23.      0.   ]]\n",
      "(768, 9)\n"
     ]
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   "source": [
    "# Load CSV using numpy\n",
    "from numpy import loadtxt\n",
    "\n",
    "raw_data = open(\"diabetes.csv\", \"r\")\n",
    "\n",
    "# load the data and skip the first row\n",
    "data = loadtxt(raw_data, delimiter = \",\",  skiprows = 1)\n",
    "\n",
    "print(data)\n",
    "print(data.shape)"
   ]
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   "source": [
    "The **limitation** with both the above approaches is - the functions assumes no header row and all data has the same format. Thus these methods of loading data are not so common and rarely used. The methods which is most commonly used is discussed below.\n",
    "\n",
    "### 3. Load CSV Files with Pandas\n",
    "You can load your CSV data using Pandas and the pandas.read csv() function. This function is very ﬂexible and is perhaps my recommended approach for loading your machine learning data. The function returns a pandas dataframe."
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
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       "      <th>0</th>\n",
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       "      <td>89</td>\n",
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       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
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       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
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       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
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   "source": [
    "# Load CSV using Pandas \n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "# Load the data\n",
    "data = pd.read_csv(\"diabetes.csv\")\n",
    "\n",
    "# display the top 5 rows\n",
    "data.head()"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Next\n",
    "Now that you know how to load your CSV data using Python it is time to start looking at it. In the next lesson you will discover how to use simple descriptive statistics to better understand your data."
   ]
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