Decision Tress and Random Forests.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#kyphosis dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('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": [],
   "source": [
    "#exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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: 2.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x14032d0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdee190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df, hue='Kyphosis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Machine learning modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop('Kyphosis', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df['Kyphosis']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree= DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            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=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions =dtree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[14  5]\n",
      " [ 2  4]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "     absent       0.88      0.74      0.80        19\n",
      "    present       0.44      0.67      0.53         6\n",
      "\n",
      "avg / total       0.77      0.72      0.74        25\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', 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, n_estimators=200, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc_predictions= rfc.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[17  2]\n",
      " [ 3  3]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "     absent       0.85      0.89      0.87        19\n",
      "    present       0.60      0.50      0.55         6\n",
      "\n",
      "avg / total       0.79      0.80      0.79        25\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,rfc_predictions))\n",
    "print(classification_report(y_test,rfc_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Random forest will perform better as datasets become large and convulated, as of now both decision trees and random forest perform equally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#also, the no of absents are more than presents and thus the dataset is disbalanced which is one of the\n",
    "#major reason for low accuracy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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