Decision tree and random forest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'C:\\Users\\Laptop\\Desktop\\Refactored_Py_DS_ML_Bootcamp-master\\15-Decision-Trees-and-Random-Forests\\kyphosis.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Kyphosis</th>\n",
       "      <th>Age</th>\n",
       "      <th>Number</th>\n",
       "      <th>Start</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>absent</td>\n",
       "      <td>71</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>absent</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>present</td>\n",
       "      <td>128</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>absent</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>absent</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Kyphosis  Age  Number  Start\n",
       "0   absent   71       3      5\n",
       "1   absent  158       3     14\n",
       "2  present  128       4      5\n",
       "3   absent    2       5      1\n",
       "4   absent    1       4     15"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 81 entries, 0 to 80\n",
      "Data columns (total 4 columns):\n",
      "Kyphosis    81 non-null object\n",
      "Age         81 non-null int64\n",
      "Number      81 non-null int64\n",
      "Start       81 non-null int64\n",
      "dtypes: int64(3), object(1)\n",
      "memory usage: 2.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "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>Age</th>\n",
       "      <th>Number</th>\n",
       "      <th>Start</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>81.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>81.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>83.654321</td>\n",
       "      <td>4.049383</td>\n",
       "      <td>11.493827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>58.104251</td>\n",
       "      <td>1.619423</td>\n",
       "      <td>4.883962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>87.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>130.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>206.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age     Number      Start\n",
       "count   81.000000  81.000000  81.000000\n",
       "mean    83.654321   4.049383  11.493827\n",
       "std     58.104251   1.619423   4.883962\n",
       "min      1.000000   2.000000   1.000000\n",
       "25%     26.000000   3.000000   9.000000\n",
       "50%     87.000000   4.000000  13.000000\n",
       "75%    130.000000   5.000000  16.000000\n",
       "max    206.000000  10.000000  18.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x199dfdcbec8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 614.125x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df,hue='Kyphosis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = df.drop('Kyphosis',axis=1)\n",
    "y = df['Kyphosis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = dtree.predict(X_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[13  4]\n",
      " [ 6  2]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      absent       0.68      0.76      0.72        17\n",
      "     present       0.33      0.25      0.29         8\n",
      "\n",
      "    accuracy                           0.60        25\n",
      "   macro avg       0.51      0.51      0.50        25\n",
      "weighted avg       0.57      0.60      0.58        25\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions))\n",
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=200,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc_pred = rfc.predict(X_test)"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}