MiniCourse.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Understand Data with Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.plotting import scatter_matrix\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import numpy as np\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import pickle as pkl\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(url, names=names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "description = data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>preg</th>\n",
       "      <th>plas</th>\n",
       "      <th>pres</th>\n",
       "      <th>skin</th>\n",
       "      <th>test</th>\n",
       "      <th>mass</th>\n",
       "      <th>pedi</th>\n",
       "      <th>age</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             preg        plas        pres        skin        test        mass  \\\n",
       "count  768.000000  768.000000  768.000000  768.000000  768.000000  768.000000   \n",
       "mean     3.845052  120.894531   69.105469   20.536458   79.799479   31.992578   \n",
       "std      3.369578   31.972618   19.355807   15.952218  115.244002    7.884160   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      1.000000   99.000000   62.000000    0.000000    0.000000   27.300000   \n",
       "50%      3.000000  117.000000   72.000000   23.000000   30.500000   32.000000   \n",
       "75%      6.000000  140.250000   80.000000   32.000000  127.250000   36.600000   \n",
       "max     17.000000  199.000000  122.000000   99.000000  846.000000   67.100000   \n",
       "\n",
       "             pedi         age       class  \n",
       "count  768.000000  768.000000  768.000000  \n",
       "mean     0.471876   33.240885    0.348958  \n",
       "std      0.331329   11.760232    0.476951  \n",
       "min      0.078000   21.000000    0.000000  \n",
       "25%      0.243750   24.000000    0.000000  \n",
       "50%      0.372500   29.000000    0.000000  \n",
       "75%      0.626250   41.000000    1.000000  \n",
       "max      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 432x288 with 81 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_matrix(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "array = data.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  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.   ]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = array[:,0:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = array[:,8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler().fit(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "rescaledX = scaler.transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.set_printoptions(precision=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.64 ,  0.848,  0.15 ,  0.907, -0.693,  0.204,  0.468,  1.426],\n",
       "       [-0.845, -1.123, -0.161,  0.531, -0.693, -0.684, -0.365, -0.191],\n",
       "       [ 1.234,  1.944, -0.264, -1.288, -0.693, -1.103,  0.604, -0.106],\n",
       "       [-0.845, -0.998, -0.161,  0.155,  0.123, -0.494, -0.921, -1.042],\n",
       "       [-1.142,  0.504, -1.505,  0.907,  0.766,  1.41 ,  5.485, -0.02 ]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rescaledX[0:5,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rahul/Workspace/spograde-recommendation-engine/venv/lib/python3.6/site-packages/scikit_learn-0.22-py3.6-linux-x86_64.egg/sklearn/model_selection/_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  FutureWarning\n"
     ]
    }
   ],
   "source": [
    "kfold = KFold(n_splits=10, random_state=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression(solver='liblinear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = cross_val_score(model, x, y, cv=kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Accuracy 76.95146958304852 4.841051924567195'"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"Accuracy {} {}\".format(results.mean() * 100.0, results.std() * 100.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Improve accuracy ```with algorithm tunning```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "alphas = np.array([1,0.1,0.01,0.001,0.0001,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = dict(alpha=alphas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Ridge()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = GridSearchCV(estimator=model, param_grid=param_grid, cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score=nan,\n",
       "             estimator=Ridge(alpha=1.0, copy_X=True, fit_intercept=True,\n",
       "                             max_iter=None, normalize=False, random_state=None,\n",
       "                             solver='auto', tol=0.001),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'alpha': array([1.e+00, 1.e-01, 1.e-02, 1.e-03, 1.e-04, 0.e+00])},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.27961755931297233"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_estimator_.alpha"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Improve accuracy with ```ensemble predictions```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way that you can improve the performance of your models is to combine the predictions from multiple models\n",
    "* Bagging ensemble with random forest and extra trees algorithm\n",
    "* Boosting with gradeint boosting and adaboost algorithms\n",
    "* voting ensembles using by combining the predictions from multiple models together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_trees = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_features = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rahul/Workspace/spograde-recommendation-engine/venv/lib/python3.6/site-packages/scikit_learn-0.22-py3.6-linux-x86_64.egg/sklearn/model_selection/_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  FutureWarning\n"
     ]
    }
   ],
   "source": [
    "kfold = KFold(n_splits=10, random_state=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomForestClassifier(n_estimators=num_trees, max_features=max_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = cross_val_score(model, x, y, cv=kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7695146958304853"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finalize and Save your model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_size = 0.33"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size=test_size, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the model on 33%\n",
    "model = LogisticRegression(solver='liblinear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the model to the disk\n",
    "filename = 'finalized_model.pkl'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "pkl.dump(model, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_model = pkl.load(open(filename, 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = loaded_model.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7559055118110236"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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