notebook1-week11-DecisionTrees-and-RandomForest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Decision Trees and Random Forests in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": "markdown",
   "metadata": {},
   "source": [
    "## Get the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('kyphosis.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   Kyphosis  81 non-null     object\n",
      " 1   Age       81 non-null     int64 \n",
      " 2   Number    81 non-null     int64 \n",
      " 3   Start     81 non-null     int64 \n",
      "dtypes: int64(3), object(1)\n",
      "memory usage: 2.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA\n",
    "\n",
    "We'll just check out a simple pairplot for this small dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1fc4c7e2af0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 433.875x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x=df['Age'],hue=df['Kyphosis'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1fc4d016d60>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x=df['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "present=df[df['Kyphosis']=='present']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1fc4d076fa0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(present['Age'],color='DarkSlateGrey')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "absent=df[df['Kyphosis']=='absent']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1fc4cfbc370>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(absent['Age'],bins=7,color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1fc4d161e50>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(data=df,col='Kyphosis')\n",
    "g.map(plt.hist,'Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Test Split\n",
    "\n",
    "Let's split up the data into a training set and a test set!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('Kyphosis',axis=1)\n",
    "y = df['Kyphosis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Trees\n",
    "\n",
    "We'll start just by training a single decision tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction and Evaluation \n",
    "\n",
    "Let's evaluate our decision tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = dtree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      absent       0.73      0.94      0.82        17\n",
      "     present       0.67      0.25      0.36         8\n",
      "\n",
      "    accuracy                           0.72        25\n",
      "   macro avg       0.70      0.60      0.59        25\n",
      "weighted avg       0.71      0.72      0.67        25\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[17  0]\n",
      " [ 5  3]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(y_test=='absent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(y_test=='present')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forests\n",
    "\n",
    "Now let's compare the decision tree model to a random forest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc = RandomForestClassifier(n_estimators=100)\n",
    "rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc_pred = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[17  0]\n",
      " [ 7  1]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,rfc_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      absent       0.71      1.00      0.83        17\n",
      "     present       1.00      0.12      0.22         8\n",
      "\n",
      "    accuracy                           0.72        25\n",
      "   macro avg       0.85      0.56      0.53        25\n",
      "weighted avg       0.80      0.72      0.64        25\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,rfc_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression model\n",
    "\n",
    "Now let's compare the decision tree model and random forest to Logistic Regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel = LogisticRegression()\n",
    "logmodel.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = logmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[17,  0],\n",
       "       [ 5,  3]], dtype=int64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      absent       0.77      1.00      0.87        17\n",
      "     present       1.00      0.38      0.55         8\n",
      "\n",
      "    accuracy                           0.80        25\n",
      "   macro avg       0.89      0.69      0.71        25\n",
      "weighted avg       0.85      0.80      0.77        25\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Great Job!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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