1. ExploringDatasets.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "\n",
    "# for machine learning models\n",
    "import sklearn\n",
    "# for computational work\n",
    "import numpy as np\n",
    "# for scientific calulation\n",
    "import scipy as sp\n",
    "# for data wrangling and cleaning\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scikit-learn Version : 0.20.3\n",
      "NumPy Version : 1.16.2\n",
      "SciPy Version : 1.2.1\n",
      "Pandas Version : 0.24.2\n"
     ]
    }
   ],
   "source": [
    "# printing versions of libraries\n",
    "print('Scikit-learn Version :', sklearn.__version__)\n",
    "print('NumPy Version :', np.__version__)\n",
    "print('SciPy Version :', sp.__version__)\n",
    "print('Pandas Version :', pd.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _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",
      "        largest values) of these features were computed for each image,\n",
      "        resulting in 30 features.  For instance, field 3 is Mean Radius, field\n",
      "        13 is Radius SE, field 23 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": [
    "# importing dataset from sklearn datasets\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "# invoking the function\n",
    "breast_cancer_dataset = load_breast_cancer()\n",
    "# describing the data\n",
    "print(breast_cancer_dataset.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting keys from dataset\n",
    "breast_cancer_dataset.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features Name :\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"
     ]
    }
   ],
   "source": [
    "# getting features name\n",
    "print('Features Name :\\n', breast_cancer_dataset.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows and Columns : (569, 30)\n"
     ]
    }
   ],
   "source": [
    "# getting the shape of data\n",
    "print('Rows and Columns :', breast_cancer_dataset.data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target Names : ['malignant' 'benign']\n"
     ]
    }
   ],
   "source": [
    "# getting target names\n",
    "print('Target Names :', breast_cancer_dataset.target_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting data into pandas dataframes\n",
    "df_features = pd.DataFrame(breast_cancer_dataset.data,\\\n",
    "                           columns=breast_cancer_dataset.feature_names)\n",
    "df_target = pd.DataFrame(breast_cancer_dataset.target, columns=['cancer'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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",
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       "    }\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>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>cancer</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>0.3001</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>0.1622</td>\n",
       "      <td>0.6656</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>0.0869</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>0.1238</td>\n",
       "      <td>0.1866</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>0.1974</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>0.1444</td>\n",
       "      <td>0.4245</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>0.2414</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>0.2098</td>\n",
       "      <td>0.8663</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>0.1980</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>0.1374</td>\n",
       "      <td>0.2050</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   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",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \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",
       "   worst smoothness  worst compactness  worst concavity  worst concave points  \\\n",
       "0            0.1622             0.6656           0.7119                0.2654   \n",
       "1            0.1238             0.1866           0.2416                0.1860   \n",
       "2            0.1444             0.4245           0.4504                0.2430   \n",
       "3            0.2098             0.8663           0.6869                0.2575   \n",
       "4            0.1374             0.2050           0.4000                0.1625   \n",
       "\n",
       "   worst symmetry  worst fractal dimension  cancer  \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",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concatenating both the data frames along x-axis\n",
    "df = pd.concat([df_features, df_target], axis=1)\n",
    "# getting first 5 records of dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows and Columns : (569, 31)\n"
     ]
    }
   ],
   "source": [
    "# getting shape of dataframe\n",
    "print('Rows and Columns :', df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# working on boston dataset\n",
    "from sklearn.datasets import load_boston\n",
    "boston_dataset = load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename'])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting keys\n",
    "boston_dataset.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _boston_dataset:\n",
      "\n",
      "Boston house prices dataset\n",
      "---------------------------\n",
      "\n",
      "**Data Set Characteristics:**  \n",
      "\n",
      "    :Number of Instances: 506 \n",
      "\n",
      "    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n",
      "\n",
      "    :Attribute Information (in order):\n",
      "        - CRIM     per capita crime rate by town\n",
      "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
      "        - INDUS    proportion of non-retail business acres per town\n",
      "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
      "        - NOX      nitric oxides concentration (parts per 10 million)\n",
      "        - RM       average number of rooms per dwelling\n",
      "        - AGE      proportion of owner-occupied units built prior to 1940\n",
      "        - DIS      weighted distances to five Boston employment centres\n",
      "        - RAD      index of accessibility to radial highways\n",
      "        - TAX      full-value property-tax rate per $10,000\n",
      "        - PTRATIO  pupil-teacher ratio by town\n",
      "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
      "        - LSTAT    % lower status of the population\n",
      "        - MEDV     Median value of owner-occupied homes in $1000's\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
      "\n",
      "This is a copy of UCI ML housing dataset.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n",
      "\n",
      "\n",
      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
      "\n",
      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
      "pages 244-261 of the latter.\n",
      "\n",
      "The Boston house-price data has been used in many machine learning papers that address regression\n",
      "problems.   \n",
      "     \n",
      ".. topic:: References\n",
      "\n",
      "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
      "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# description of data\n",
    "print(boston_dataset.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting news group data\n",
    "from sklearn.datasets import fetch_20newsgroups\n",
    "fetch_20_traindata = fetch_20newsgroups(subset='train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'filenames', 'target_names', 'target', 'DESCR'])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting keys\n",
    "fetch_20_traindata.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _20newsgroups_dataset:\n",
      "\n",
      "The 20 newsgroups text dataset\n",
      "------------------------------\n",
      "\n",
      "The 20 newsgroups dataset comprises around 18000 newsgroups posts on\n",
      "20 topics split in two subsets: one for training (or development)\n",
      "and the other one for testing (or for performance evaluation). The split\n",
      "between the train and test set is based upon a messages posted before\n",
      "and after a specific date.\n",
      "\n",
      "This module contains two loaders. The first one,\n",
      ":func:`sklearn.datasets.fetch_20newsgroups`,\n",
      "returns a list of the raw texts that can be fed to text feature\n",
      "extractors such as :class:`sklearn.feature_extraction.text.CountVectorizer`\n",
      "with custom parameters so as to extract feature vectors.\n",
      "The second one, :func:`sklearn.datasets.fetch_20newsgroups_vectorized`,\n",
      "returns ready-to-use features, i.e., it is not necessary to use a feature\n",
      "extractor.\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    =================   ==========\n",
      "    Classes                     20\n",
      "    Samples total            18846\n",
      "    Dimensionality               1\n",
      "    Features                  text\n",
      "    =================   ==========\n",
      "\n",
      "Usage\n",
      "~~~~~\n",
      "\n",
      "The :func:`sklearn.datasets.fetch_20newsgroups` function is a data\n",
      "fetching / caching functions that downloads the data archive from\n",
      "the original `20 newsgroups website`_, extracts the archive contents\n",
      "in the ``~/scikit_learn_data/20news_home`` folder and calls the\n",
      ":func:`sklearn.datasets.load_files` on either the training or\n",
      "testing set folder, or both of them::\n",
      "\n",
      "  >>> from sklearn.datasets import fetch_20newsgroups\n",
      "  >>> newsgroups_train = fetch_20newsgroups(subset='train')\n",
      "\n",
      "  >>> from pprint import pprint\n",
      "  >>> pprint(list(newsgroups_train.target_names))\n",
      "  ['alt.atheism',\n",
      "   'comp.graphics',\n",
      "   'comp.os.ms-windows.misc',\n",
      "   'comp.sys.ibm.pc.hardware',\n",
      "   'comp.sys.mac.hardware',\n",
      "   'comp.windows.x',\n",
      "   'misc.forsale',\n",
      "   'rec.autos',\n",
      "   'rec.motorcycles',\n",
      "   'rec.sport.baseball',\n",
      "   'rec.sport.hockey',\n",
      "   'sci.crypt',\n",
      "   'sci.electronics',\n",
      "   'sci.med',\n",
      "   'sci.space',\n",
      "   'soc.religion.christian',\n",
      "   'talk.politics.guns',\n",
      "   'talk.politics.mideast',\n",
      "   'talk.politics.misc',\n",
      "   'talk.religion.misc']\n",
      "\n",
      "The real data lies in the ``filenames`` and ``target`` attributes. The target\n",
      "attribute is the integer index of the category::\n",
      "\n",
      "  >>> newsgroups_train.filenames.shape\n",
      "  (11314,)\n",
      "  >>> newsgroups_train.target.shape\n",
      "  (11314,)\n",
      "  >>> newsgroups_train.target[:10]\n",
      "  array([ 7,  4,  4,  1, 14, 16, 13,  3,  2,  4])\n",
      "\n",
      "It is possible to load only a sub-selection of the categories by passing the\n",
      "list of the categories to load to the\n",
      ":func:`sklearn.datasets.fetch_20newsgroups` function::\n",
      "\n",
      "  >>> cats = ['alt.atheism', 'sci.space']\n",
      "  >>> newsgroups_train = fetch_20newsgroups(subset='train', categories=cats)\n",
      "\n",
      "  >>> list(newsgroups_train.target_names)\n",
      "  ['alt.atheism', 'sci.space']\n",
      "  >>> newsgroups_train.filenames.shape\n",
      "  (1073,)\n",
      "  >>> newsgroups_train.target.shape\n",
      "  (1073,)\n",
      "  >>> newsgroups_train.target[:10]\n",
      "  array([0, 1, 1, 1, 0, 1, 1, 0, 0, 0])\n",
      "\n",
      "Converting text to vectors\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "\n",
      "In order to feed predictive or clustering models with the text data,\n",
      "one first need to turn the text into vectors of numerical values suitable\n",
      "for statistical analysis. This can be achieved with the utilities of the\n",
      "``sklearn.feature_extraction.text`` as demonstrated in the following\n",
      "example that extract `TF-IDF`_ vectors of unigram tokens\n",
      "from a subset of 20news::\n",
      "\n",
      "  >>> from sklearn.feature_extraction.text import TfidfVectorizer\n",
      "  >>> categories = ['alt.atheism', 'talk.religion.misc',\n",
      "  ...               'comp.graphics', 'sci.space']\n",
      "  >>> newsgroups_train = fetch_20newsgroups(subset='train',\n",
      "  ...                                       categories=categories)\n",
      "  >>> vectorizer = TfidfVectorizer()\n",
      "  >>> vectors = vectorizer.fit_transform(newsgroups_train.data)\n",
      "  >>> vectors.shape\n",
      "  (2034, 34118)\n",
      "\n",
      "The extracted TF-IDF vectors are very sparse, with an average of 159 non-zero\n",
      "components by sample in a more than 30000-dimensional space\n",
      "(less than .5% non-zero features)::\n",
      "\n",
      "  >>> vectors.nnz / float(vectors.shape[0])       # doctest: +ELLIPSIS\n",
      "  159.01327...\n",
      "\n",
      ":func:`sklearn.datasets.fetch_20newsgroups_vectorized` is a function which \n",
      "returns ready-to-use token counts features instead of file names.\n",
      "\n",
      ".. _`20 newsgroups website`: http://people.csail.mit.edu/jrennie/20Newsgroups/\n",
      ".. _`TF-IDF`: https://en.wikipedia.org/wiki/Tf-idf\n",
      "\n",
      "\n",
      "Filtering text for more realistic training\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "\n",
      "It is easy for a classifier to overfit on particular things that appear in the\n",
      "20 Newsgroups data, such as newsgroup headers. Many classifiers achieve very\n",
      "high F-scores, but their results would not generalize to other documents that\n",
      "aren't from this window of time.\n",
      "\n",
      "For example, let's look at the results of a multinomial Naive Bayes classifier,\n",
      "which is fast to train and achieves a decent F-score::\n",
      "\n",
      "  >>> from sklearn.naive_bayes import MultinomialNB\n",
      "  >>> from sklearn import metrics\n",
      "  >>> newsgroups_test = fetch_20newsgroups(subset='test',\n",
      "  ...                                      categories=categories)\n",
      "  >>> vectors_test = vectorizer.transform(newsgroups_test.data)\n",
      "  >>> clf = MultinomialNB(alpha=.01)\n",
      "  >>> clf.fit(vectors, newsgroups_train.target)\n",
      "  MultinomialNB(alpha=0.01, class_prior=None, fit_prior=True)\n",
      "\n",
      "  >>> pred = clf.predict(vectors_test)\n",
      "  >>> metrics.f1_score(newsgroups_test.target, pred, average='macro')  # doctest: +ELLIPSIS\n",
      "  0.88213...\n",
      "\n",
      "(The example :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py` shuffles\n",
      "the training and test data, instead of segmenting by time, and in that case\n",
      "multinomial Naive Bayes gets a much higher F-score of 0.88. Are you suspicious\n",
      "yet of what's going on inside this classifier?)\n",
      "\n",
      "Let's take a look at what the most informative features are:\n",
      "\n",
      "  >>> import numpy as np\n",
      "  >>> def show_top10(classifier, vectorizer, categories):\n",
      "  ...     feature_names = np.asarray(vectorizer.get_feature_names())\n",
      "  ...     for i, category in enumerate(categories):\n",
      "  ...         top10 = np.argsort(classifier.coef_[i])[-10:]\n",
      "  ...         print(\"%s: %s\" % (category, \" \".join(feature_names[top10])))\n",
      "  ...\n",
      "  >>> show_top10(clf, vectorizer, newsgroups_train.target_names)\n",
      "  alt.atheism: edu it and in you that is of to the\n",
      "  comp.graphics: edu in graphics it is for and of to the\n",
      "  sci.space: edu it that is in and space to of the\n",
      "  talk.religion.misc: not it you in is that and to of the\n",
      "\n",
      "\n",
      "You can now see many things that these features have overfit to:\n",
      "\n",
      "- Almost every group is distinguished by whether headers such as\n",
      "  ``NNTP-Posting-Host:`` and ``Distribution:`` appear more or less often.\n",
      "- Another significant feature involves whether the sender is affiliated with\n",
      "  a university, as indicated either by their headers or their signature.\n",
      "- The word \"article\" is a significant feature, based on how often people quote\n",
      "  previous posts like this: \"In article [article ID], [name] <[e-mail address]>\n",
      "  wrote:\"\n",
      "- Other features match the names and e-mail addresses of particular people who\n",
      "  were posting at the time.\n",
      "\n",
      "With such an abundance of clues that distinguish newsgroups, the classifiers\n",
      "barely have to identify topics from text at all, and they all perform at the\n",
      "same high level.\n",
      "\n",
      "For this reason, the functions that load 20 Newsgroups data provide a\n",
      "parameter called **remove**, telling it what kinds of information to strip out\n",
      "of each file. **remove** should be a tuple containing any subset of\n",
      "``('headers', 'footers', 'quotes')``, telling it to remove headers, signature\n",
      "blocks, and quotation blocks respectively.\n",
      "\n",
      "  >>> newsgroups_test = fetch_20newsgroups(subset='test',\n",
      "  ...                                      remove=('headers', 'footers', 'quotes'),\n",
      "  ...                                      categories=categories)\n",
      "  >>> vectors_test = vectorizer.transform(newsgroups_test.data)\n",
      "  >>> pred = clf.predict(vectors_test)\n",
      "  >>> metrics.f1_score(pred, newsgroups_test.target, average='macro')  # doctest: +ELLIPSIS\n",
      "  0.77310...\n",
      "\n",
      "This classifier lost over a lot of its F-score, just because we removed\n",
      "metadata that has little to do with topic classification.\n",
      "It loses even more if we also strip this metadata from the training data:\n",
      "\n",
      "  >>> newsgroups_train = fetch_20newsgroups(subset='train',\n",
      "  ...                                       remove=('headers', 'footers', 'quotes'),\n",
      "  ...                                       categories=categories)\n",
      "  >>> vectors = vectorizer.fit_transform(newsgroups_train.data)\n",
      "  >>> clf = MultinomialNB(alpha=.01)\n",
      "  >>> clf.fit(vectors, newsgroups_train.target)\n",
      "  MultinomialNB(alpha=0.01, class_prior=None, fit_prior=True)\n",
      "\n",
      "  >>> vectors_test = vectorizer.transform(newsgroups_test.data)\n",
      "  >>> pred = clf.predict(vectors_test)\n",
      "  >>> metrics.f1_score(newsgroups_test.target, pred, average='macro')  # doctest: +ELLIPSIS\n",
      "  0.76995...\n",
      "\n",
      "Some other classifiers cope better with this harder version of the task. Try\n",
      "running :ref:`sphx_glr_auto_examples_model_selection_grid_search_text_feature_extraction.py` with and without\n",
      "the ``--filter`` option to compare the results.\n",
      "\n",
      ".. topic:: Recommendation\n",
      "\n",
      "  When evaluating text classifiers on the 20 Newsgroups data, you\n",
      "  should strip newsgroup-related metadata. In scikit-learn, you can do this by\n",
      "  setting ``remove=('headers', 'footers', 'quotes')``. The F-score will be\n",
      "  lower because it is more realistic.\n",
      "\n",
      ".. topic:: Examples\n",
      "\n",
      "   * :ref:`sphx_glr_auto_examples_model_selection_grid_search_text_feature_extraction.py`\n",
      "\n",
      "   * :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# describing data set\n",
    "print(fetch_20_traindata.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\"From: lerxst@wam.umd.edu (where's my thing)\\nSubject: WHAT car is this!?\\nNntp-Posting-Host: rac3.wam.umd.edu\\nOrganization: University of Maryland, College Park\\nLines: 15\\n\\n I was wondering if anyone out there could enlighten me on this car I saw\\nthe other day. It was a 2-door sports car, looked to be from the late 60s/\\nearly 70s. It was called a Bricklin. The doors were really small. In addition,\\nthe front bumper was separate from the rest of the body. This is \\nall I know. If anyone can tellme a model name, engine specs, years\\nof production, where this car is made, history, or whatever info you\\nhave on this funky looking car, please e-mail.\\n\\nThanks,\\n- IL\\n   ---- brought to you by your neighborhood Lerxst ----\\n\\n\\n\\n\\n\", \"From: guykuo@carson.u.washington.edu (Guy Kuo)\\nSubject: SI Clock Poll - Final Call\\nSummary: Final call for SI clock reports\\nKeywords: SI,acceleration,clock,upgrade\\nArticle-I.D.: shelley.1qvfo9INNc3s\\nOrganization: University of Washington\\nLines: 11\\nNNTP-Posting-Host: carson.u.washington.edu\\n\\nA fair number of brave souls who upgraded their SI clock oscillator have\\nshared their experiences for this poll. Please send a brief message detailing\\nyour experiences with the procedure. Top speed attained, CPU rated speed,\\nadd on cards and adapters, heat sinks, hour of usage per day, floppy disk\\nfunctionality with 800 and 1.4 m floppies are especially requested.\\n\\nI will be summarizing in the next two days, so please add to the network\\nknowledge base if you have done the clock upgrade and haven't answered this\\npoll. Thanks.\\n\\nGuy Kuo <guykuo@u.washington.edu>\\n\"]\n"
     ]
    }
   ],
   "source": [
    "# knowing form of data\n",
    "print(fetch_20_traindata.data[0:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['alt.atheism',\n",
       " 'comp.graphics',\n",
       " 'comp.os.ms-windows.misc',\n",
       " 'comp.sys.ibm.pc.hardware',\n",
       " 'comp.sys.mac.hardware',\n",
       " 'comp.windows.x',\n",
       " 'misc.forsale',\n",
       " 'rec.autos',\n",
       " 'rec.motorcycles',\n",
       " 'rec.sport.baseball',\n",
       " 'rec.sport.hockey',\n",
       " 'sci.crypt',\n",
       " 'sci.electronics',\n",
       " 'sci.med',\n",
       " 'sci.space',\n",
       " 'soc.religion.christian',\n",
       " 'talk.politics.guns',\n",
       " 'talk.politics.mideast',\n",
       " 'talk.politics.misc',\n",
       " 'talk.religion.misc']"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting target names\n",
    "fetch_20_traindata.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 4, 4, ..., 3, 1, 8])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting numeric target\n",
    "fetch_20_traindata.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading digits data\n",
    "from sklearn.datasets import load_digits\n",
    "digits_dataset = load_digits(n_class=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'target_names', 'images', 'DESCR'])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting keys\n",
    "digits_dataset.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _digits_dataset:\n",
      "\n",
      "Optical recognition of handwritten digits dataset\n",
      "--------------------------------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 5620\n",
      "    :Number of Attributes: 64\n",
      "    :Attribute Information: 8x8 image of integer pixels in the range 0..16.\n",
      "    :Missing Attribute Values: None\n",
      "    :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)\n",
      "    :Date: July; 1998\n",
      "\n",
      "This is a copy of the test set of the UCI ML hand-written digits datasets\n",
      "http://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\n",
      "\n",
      "The data set contains images of hand-written digits: 10 classes where\n",
      "each class refers to a digit.\n",
      "\n",
      "Preprocessing programs made available by NIST were used to extract\n",
      "normalized bitmaps of handwritten digits from a preprinted form. From a\n",
      "total of 43 people, 30 contributed to the training set and different 13\n",
      "to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of\n",
      "4x4 and the number of on pixels are counted in each block. This generates\n",
      "an input matrix of 8x8 where each element is an integer in the range\n",
      "0..16. This reduces dimensionality and gives invariance to small\n",
      "distortions.\n",
      "\n",
      "For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.\n",
      "T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.\n",
      "L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,\n",
      "1994.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "  - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their\n",
      "    Applications to Handwritten Digit Recognition, MSc Thesis, Institute of\n",
      "    Graduate Studies in Science and Engineering, Bogazici University.\n",
      "  - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\n",
      "  - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.\n",
      "    Linear dimensionalityreduction using relevance weighted LDA. School of\n",
      "    Electrical and Electronic Engineering Nanyang Technological University.\n",
      "    2005.\n",
      "  - Claudio Gentile. A New Approximate Maximal Margin Classification\n",
      "    Algorithm. NIPS. 2000.\n"
     ]
    }
   ],
   "source": [
    "# describing data set\n",
    "print(digits_dataset.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# importing matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(digits_dataset.images[1], cmap='Greys')\n",
    "# visualizing the data using graph\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}