water1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mlib\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>district_code</th>\n",
       "      <th>population</th>\n",
       "      <th>water_quality</th>\n",
       "      <th>quality_group</th>\n",
       "      <th>quantity</th>\n",
       "      <th>quantity_group</th>\n",
       "      <th>source</th>\n",
       "      <th>region_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Iringa</td>\n",
       "      <td>5</td>\n",
       "      <td>109</td>\n",
       "      <td>soft</td>\n",
       "      <td>good</td>\n",
       "      <td>enough</td>\n",
       "      <td>enough</td>\n",
       "      <td>spring</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mara</td>\n",
       "      <td>2</td>\n",
       "      <td>280</td>\n",
       "      <td>soft</td>\n",
       "      <td>good</td>\n",
       "      <td>insufficient</td>\n",
       "      <td>insufficient</td>\n",
       "      <td>rainwater harvesting</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Manyara</td>\n",
       "      <td>4</td>\n",
       "      <td>250</td>\n",
       "      <td>soft</td>\n",
       "      <td>good</td>\n",
       "      <td>enough</td>\n",
       "      <td>enough</td>\n",
       "      <td>dam</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mtwara</td>\n",
       "      <td>63</td>\n",
       "      <td>58</td>\n",
       "      <td>soft</td>\n",
       "      <td>good</td>\n",
       "      <td>dry</td>\n",
       "      <td>dry</td>\n",
       "      <td>machine dbh</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kagera</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>soft</td>\n",
       "      <td>good</td>\n",
       "      <td>seasonal</td>\n",
       "      <td>seasonal</td>\n",
       "      <td>rainwater harvesting</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    region  district_code  population water_quality quality_group  \\\n",
       "0   Iringa              5         109          soft          good   \n",
       "1     Mara              2         280          soft          good   \n",
       "2  Manyara              4         250          soft          good   \n",
       "3   Mtwara             63          58          soft          good   \n",
       "4   Kagera              1           0          soft          good   \n",
       "\n",
       "       quantity quantity_group                source  region_code  \n",
       "0        enough         enough                spring           11  \n",
       "1  insufficient   insufficient  rainwater harvesting           20  \n",
       "2        enough         enough                   dam           21  \n",
       "3           dry            dry           machine dbh           90  \n",
       "4      seasonal       seasonal  rainwater harvesting           18  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_excel('WATER1.XLS')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>population</th>\n",
       "      <th>water_quality</th>\n",
       "      <th>quantity</th>\n",
       "      <th>region_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Iringa</td>\n",
       "      <td>109</td>\n",
       "      <td>soft</td>\n",
       "      <td>enough</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mara</td>\n",
       "      <td>280</td>\n",
       "      <td>soft</td>\n",
       "      <td>insufficient</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Manyara</td>\n",
       "      <td>250</td>\n",
       "      <td>soft</td>\n",
       "      <td>enough</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mtwara</td>\n",
       "      <td>58</td>\n",
       "      <td>soft</td>\n",
       "      <td>dry</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kagera</td>\n",
       "      <td>0</td>\n",
       "      <td>soft</td>\n",
       "      <td>seasonal</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    region  population water_quality      quantity  region_code\n",
       "0   Iringa         109          soft        enough           11\n",
       "1     Mara         280          soft  insufficient           20\n",
       "2  Manyara         250          soft        enough           21\n",
       "3   Mtwara          58          soft           dry           90\n",
       "4   Kagera           0          soft      seasonal           18"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.drop(['district_code' , 'quality_group' , 'quantity_group','source'  ], axis=1 , inplace=True)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>population</th>\n",
       "      <th>water_quality</th>\n",
       "      <th>quantity</th>\n",
       "      <th>region_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>109</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>280</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>250</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>58</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   region  population  water_quality  quantity  region_code\n",
       "0       3         109              6         1           11\n",
       "1       9         280              6         2           20\n",
       "2       8         250              6         1           21\n",
       "3      12          58              6         0           90\n",
       "4       4           0              6         3           18"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "encoder = LabelEncoder()\n",
    "train.iloc[:,0] = encoder.fit_transform(train.iloc[:,0])\n",
    "train.iloc[:,3] = encoder.fit_transform(train.iloc[:,3])\n",
    "train.iloc[:,2] = encoder.fit_transform(train.iloc[:,2])\n",
    "\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>population</th>\n",
       "      <th>water_quality</th>\n",
       "      <th>quantity</th>\n",
       "      <th>region_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>109</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>280</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>250</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>58</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   region  population  water_quality  quantity  region_code\n",
       "0       3         109              6         1           11\n",
       "1       9         280              6         2           20\n",
       "2       8         250              6         1           21\n",
       "3      12          58              6         0           90\n",
       "4       4           0              6         3           18"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train , x_test , y_train , y_test = train_test_split(train.drop('quantity' , axis=1), train['quantity'] , test_size=0.3 , random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logmodel = LogisticRegression()\n",
    "logmodel.fit(x_train,y_train)\n",
    "y_pred=logmodel.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.00      0.00      0.00      1923\n",
      "          1       0.56      0.99      0.72     10019\n",
      "          2       0.40      0.02      0.04      4484\n",
      "          3       0.00      0.00      0.00      1164\n",
      "          4       0.00      0.00      0.00       230\n",
      "\n",
      "avg / total       0.42      0.56      0.41     17820\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aniru\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=1000, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=15, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n",
       "            oob_score=False, random_state=3, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "clf = RandomForestClassifier(n_estimators=100, max_depth=1000,random_state=3,min_samples_leaf=15)\n",
    "clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred=clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.64      0.28      0.39      1923\n",
      "          1       0.63      0.92      0.75     10019\n",
      "          2       0.60      0.30      0.39      4484\n",
      "          3       0.57      0.04      0.07      1164\n",
      "          4       0.61      0.43      0.50       230\n",
      "\n",
      "avg / total       0.62      0.63      0.57     17820\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "mlp=MLPClassifier(hidden_layer_sizes=(50,50,50),activation='relu',solver='adam',max_iter=500)\n",
    "mlp.fit(x_train,y_train)\n",
    "predict_train=mlp.predict(x_train)\n",
    "predict_test=mlp.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.71      0.17      0.27      1923\n",
      "          1       0.59      0.93      0.73     10019\n",
      "          2       0.52      0.19      0.27      4484\n",
      "          3       0.49      0.02      0.04      1164\n",
      "          4       0.67      0.23      0.34       230\n",
      "\n",
      "avg / total       0.58      0.59      0.51     17820\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predict_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
      "       colsample_bynode=1, colsample_bytree=1, gamma=0, learning_rate=0.1,\n",
      "       max_delta_step=0, max_depth=3, min_child_weight=1, missing=None,\n",
      "       n_estimators=100, n_jobs=1, nthread=None,\n",
      "       objective='multi:softprob', random_state=0, reg_alpha=0,\n",
      "       reg_lambda=1, scale_pos_weight=1, seed=None, silent=None,\n",
      "       subsample=1, verbosity=1)\n",
      "accuracy: 60.36%\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "seed=7\n",
    "test_size=0.33\n",
    "x_train , x_test , y_train , y_test = train_test_split(train.drop('quantity' , axis=1), train['quantity'] , test_size=test_size , random_state=seed)\n",
    "model=XGBClassifier()\n",
    "model.fit(x_train,y_train)\n",
    "print(model)\n",
    "y_pred=model.predict(x_test)\n",
    "predictions=[round(value) for value in y_pred]\n",
    "accuracy=accuracy_score(y_test,predictions)\n",
    "print(\"accuracy: %.2f%%\" %(accuracy * 100.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from lightgbm import LGBMClassifier\n",
    "\n",
    "lgbm = LGBMClassifier(objective='multiclass', random_state=5)\n",
    "\n",
    "lgbm.fit(x_train, y_train)\n",
    "\n",
    "y_pred = lgbm.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.65      0.27      0.38      2043\n",
      "          1       0.62      0.92      0.74     10907\n",
      "          2       0.60      0.28      0.38      5002\n",
      "          3       0.61      0.04      0.08      1384\n",
      "          4       0.61      0.46      0.52       266\n",
      "\n",
      "avg / total       0.62      0.62      0.56     19602\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "clf = SVC(kernel='linear' )\n",
    "clf.fit(x_train, y_train)  \n",
    "\n",
    "y_pred=clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print(classification_report(y_test,y_pred))"
   ]
  }
 ],
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