4.Modeling and Evaluation DC Crime.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MODELING AND EVALUATION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Load Necessary Library and Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import Necessary Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from __future__ import print_function\n",
    "from pandas import read_csv\n",
    "from pandas.tools.plotting import parallel_coordinates\n",
    "from sklearn import metrics\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "# Visual setting\n",
    "sns.set(font_scale=1)\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True) # one of the many color mappings\n",
    "plt.style.use('fivethirtyeight')\n",
    "sns.set_style('whitegrid')\n",
    "plt.style.use('ggplot')\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv('./input/dc_crime3.csv')\n",
    "ward_data =pd.read_csv('./input/warddata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "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>Unnamed: 0</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>CCN</th>\n",
       "      <th>REPORT_DAT</th>\n",
       "      <th>SHIFT</th>\n",
       "      <th>METHOD</th>\n",
       "      <th>OFFENSE</th>\n",
       "      <th>XBLOCK</th>\n",
       "      <th>YBLOCK</th>\n",
       "      <th>...</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>hour</th>\n",
       "      <th>dayofyear</th>\n",
       "      <th>week</th>\n",
       "      <th>weekofyear</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>weekday</th>\n",
       "      <th>quarter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-76.999518</td>\n",
       "      <td>38.901924</td>\n",
       "      <td>9074624</td>\n",
       "      <td>25/04/2012 00:00</td>\n",
       "      <td>MIDNIGHT</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>SEX ABUSE</td>\n",
       "      <td>400042.0</td>\n",
       "      <td>137118.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-76.995541</td>\n",
       "      <td>38.905032</td>\n",
       "      <td>10123633</td>\n",
       "      <td>29/02/2012 00:00</td>\n",
       "      <td>MIDNIGHT</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>SEX ABUSE</td>\n",
       "      <td>400387.0</td>\n",
       "      <td>137463.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0          X          Y       CCN        REPORT_DAT     SHIFT  \\\n",
       "0           0 -76.999518  38.901924   9074624  25/04/2012 00:00  MIDNIGHT   \n",
       "1           1 -76.995541  38.905032  10123633  29/02/2012 00:00  MIDNIGHT   \n",
       "\n",
       "   METHOD    OFFENSE    XBLOCK    YBLOCK   ...      Year  Month   Day  hour  \\\n",
       "0  OTHERS  SEX ABUSE  400042.0  137118.0   ...    2009.0    6.0   1.0   6.0   \n",
       "1  OTHERS  SEX ABUSE  400387.0  137463.0   ...    2010.0    8.0  27.0   7.0   \n",
       "\n",
       "   dayofyear  week  weekofyear  dayofweek  weekday quarter  \n",
       "0      152.0  23.0        23.0        0.0      0.0     2.0  \n",
       "1      239.0  34.0        34.0        4.0      4.0     3.0  \n",
       "\n",
       "[2 rows x 42 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()[0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## X  Features and y target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Features =['SHIFT', 'OFFENSE', 'METHOD', 'DISTRICT', 'PSA', 'WARD', 'CENSUS_TRACT', \n",
    "#                'VOTING_PRECINCT', 'CRIME_TYPE', 'CCN', 'XBLOCK', 'YBLOCK', 'START_DATE', 'DistrictID', 'SHIFT_Code', \n",
    "#                'METHOD_Code', 'AGE', 'TIME_TO_REPORT']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # please choose features of your interest such as Age, time, crime type, unemployment, Housing price\n",
    "# X = df.drop('Features')\n",
    "# y= df[Features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # create ANC attribute names and values\n",
    "# y = df['OFFENSE_Code']\n",
    "# X = df.drop('OFFENSE_Code',axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.preprocessing import Imputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_train,X_test,y_train,y_test = train_test_split(X,y,stratify =y,random_state=1837)\n",
    "# imp= Imputer()\n",
    "# X_train = imp.fit_transform(X_train)\n",
    "# X_test = imp.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating & Training KNN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# from sklearn.neighbors import KNeighborsClassifier\n",
    "# knn = KNeighborsClassifier(n_neighbors=10)\n",
    "# knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# knn.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# knn.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating & Training Decision Tree Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.tree import DecisionTreeClassifier\n",
    "# dtree = DecisionTreeClassifier(max_depth=50, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dtree.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y_pred=dtree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# dtree.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dtree.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# treefeatures=dtree.feature_importances_\n",
    "# indices = np.argsort(treefeatures)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# treefeatures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tree Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature_names=[ 'dayofweek', 'dayofyear', 'hour', 'month', 'week','latitude', 'longitude']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating & Training Random Tree Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# from sklearn.ensemble import RandomForestClassifier\n",
    "# rfc = RandomForestClassifier(n_estimators=100)\n",
    "# rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y_pred=rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# rfc.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rfc.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Think of doing  Confusion Matrix"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:dsi]",
   "language": "python",
   "name": "conda-env-dsi-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}