Random Forest/Random Forest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Here we use dataset downloaded from kaggle to compare between decision tree and random forest algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n\n  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n\n  YrSold  SaleType  SaleCondition  SalePrice  \n0   2008        WD         Normal     208500  \n1   2007        WD         Normal     181500  \n2   2008        WD         Normal     223500  \n3   2006        WD        Abnorml     140000  \n4   2008        WD         Normal     250000  \n\n[5 rows x 81 columns]",
      "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>Id</th>\n      <th>MSSubClass</th>\n      <th>MSZoning</th>\n      <th>LotFrontage</th>\n      <th>LotArea</th>\n      <th>Street</th>\n      <th>Alley</th>\n      <th>LotShape</th>\n      <th>LandContour</th>\n      <th>Utilities</th>\n      <th>...</th>\n      <th>PoolArea</th>\n      <th>PoolQC</th>\n      <th>Fence</th>\n      <th>MiscFeature</th>\n      <th>MiscVal</th>\n      <th>MoSold</th>\n      <th>YrSold</th>\n      <th>SaleType</th>\n      <th>SaleCondition</th>\n      <th>SalePrice</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>60</td>\n      <td>RL</td>\n      <td>65.0</td>\n      <td>8450</td>\n      <td>Pave</td>\n      <td>NaN</td>\n      <td>Reg</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>...</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2008</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>208500</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>20</td>\n      <td>RL</td>\n      <td>80.0</td>\n      <td>9600</td>\n      <td>Pave</td>\n      <td>NaN</td>\n      <td>Reg</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>...</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>5</td>\n      <td>2007</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>181500</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>60</td>\n      <td>RL</td>\n      <td>68.0</td>\n      <td>11250</td>\n      <td>Pave</td>\n      <td>NaN</td>\n      <td>IR1</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>...</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>9</td>\n      <td>2008</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>223500</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>70</td>\n      <td>RL</td>\n      <td>60.0</td>\n      <td>9550</td>\n      <td>Pave</td>\n      <td>NaN</td>\n      <td>IR1</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>...</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2006</td>\n      <td>WD</td>\n      <td>Abnorml</td>\n      <td>140000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>60</td>\n      <td>RL</td>\n      <td>84.0</td>\n      <td>14260</td>\n      <td>Pave</td>\n      <td>NaN</td>\n      <td>IR1</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>...</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>12</td>\n      <td>2008</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>250000</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 81 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "# Path of the file to read\n",
    "iowa_file_path = 'E:/ML Exercises/Random Forest/home-data-kaggle/train.csv'\n",
    "home_data = pd.read_csv(iowa_file_path)\n",
    "home_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create target object and call it y\n",
    "y = home_data.SalePrice\n",
    "# Create X\n",
    "features = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']\n",
    "X = home_data[features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Split into validation and training data\n",
    "train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', 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='deprecated',\n                      random_state=1, splitter='best')"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "# Specify Model\n",
    "iowa_model = DecisionTreeRegressor(random_state=1)\n",
    "# Fit Model\n",
    "iowa_model.fit(train_X, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Validation MAE when not specifying max_leaf_nodes: 29,653\n"
    }
   ],
   "source": [
    "# Make validation predictions and calculate mean absolute error\n",
    "val_predictions = iowa_model.predict(val_X)\n",
    "val_mae = mean_absolute_error(val_predictions, val_y)\n",
    "print(\"Validation MAE when not specifying max_leaf_nodes: {:,.0f}\".format(val_mae))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Validation MAE for best value of max_leaf_nodes: 27,283\n"
    }
   ],
   "source": [
    "# Using best value for max_leaf_nodes\n",
    "iowa_model = DecisionTreeRegressor(max_leaf_nodes=100, random_state=1)\n",
    "iowa_model.fit(train_X, train_y)\n",
    "val_predictions = iowa_model.predict(val_X)\n",
    "val_mae = mean_absolute_error(val_predictions, val_y)\n",
    "print(\"Validation MAE for best value of max_leaf_nodes: {:,.0f}\".format(val_mae))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Validation MAE for Random Forest Model: 21857.15912981083\n"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "# Define the model. Set random_state to 1\n",
    "rf_model = RandomForestRegressor(random_state=1)\n",
    "\n",
    "# fitting model\n",
    "rf_model.fit(train_X, train_y)\n",
    "\n",
    "# Calculate the mean absolute error of Random Forest model on the validation data\n",
    "rf_val_mae = mean_absolute_error(val_y, rf_model.predict(val_X))\n",
    "\n",
    "print(\"Validation MAE for Random Forest Model: {}\".format(rf_val_mae))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  replacing the decision tree with a Random Forest is going to be an easy win."
   ]
  }
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