Predicting Concrete Strength - Random Forest Regressor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting Concrete Strength - Linear Regression vs Random Forest Regressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from skew import skew_label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def showoutliers(df, column_name):\n",
    "        iqr = df[column_name].quantile(.75) - df[column_name].quantile(.25)\n",
    "        lowerbound = (df[column_name].quantile(.25)) - iqr * 1.5\n",
    "        upperbound = (df[column_name].quantile(.75)) + iqr * 1.5\n",
    "        lowerbound_outliers = df[df[column_name] < lowerbound]\n",
    "        higherbound_outliers = df[df[column_name] > upperbound]\n",
    "        outliers = pd.concat([lowerbound_outliers,higherbound_outliers])\n",
    "        return outliers\n",
    "\n",
    "def countoutliers(df, column_name):\n",
    "        iqr = df[column_name].quantile(.75) - df[column_name].quantile(.25)\n",
    "        lowerbound = (df[column_name].quantile(.25)) - iqr * 1.5\n",
    "        upperbound = (df[column_name].quantile(.75)) + iqr * 1.5\n",
    "        lowerbound_outliers = df[df[column_name] < lowerbound]\n",
    "        higherbound_outliers = df[df[column_name] > upperbound]\n",
    "        outliers = pd.concat([lowerbound_outliers,higherbound_outliers])\n",
    "        return len(outliers)\n",
    "\n",
    "def removeoutliers(df, column_name):\n",
    "        iqr = df[column_name].quantile(.75) - df[column_name].quantile(.25)\n",
    "        lowerbound = (df[column_name].quantile(.25)) - iqr * 1.5\n",
    "        upperbound = (df[column_name].quantile(.75)) + iqr * 1.5\n",
    "        lowerbound_outliers = df[df[column_name] < lowerbound]\n",
    "        higherbound_outliers = df[df[column_name] > upperbound]\n",
    "        outliers = pd.concat([lowerbound_outliers,higherbound_outliers])\n",
    "        normal = df[~ df.index.isin(outliers.index)]\n",
    "        return normal\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cement</th>\n",
       "      <th>slag</th>\n",
       "      <th>fly_ash</th>\n",
       "      <th>water</th>\n",
       "      <th>superplasticizer</th>\n",
       "      <th>coarse_aggregate</th>\n",
       "      <th>fine_aggregate</th>\n",
       "      <th>age</th>\n",
       "      <th>strength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>540.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>28</td>\n",
       "      <td>79.986111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>540.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1055.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>28</td>\n",
       "      <td>61.887366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>332.5</td>\n",
       "      <td>142.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>270</td>\n",
       "      <td>40.269535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>332.5</td>\n",
       "      <td>142.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>365</td>\n",
       "      <td>41.052780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>198.6</td>\n",
       "      <td>132.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978.4</td>\n",
       "      <td>825.5</td>\n",
       "      <td>360</td>\n",
       "      <td>44.296075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cement   slag  fly_ash  water  superplasticizer  coarse_aggregate  \\\n",
       "0   540.0    0.0      0.0  162.0               2.5            1040.0   \n",
       "1   540.0    0.0      0.0  162.0               2.5            1055.0   \n",
       "2   332.5  142.5      0.0  228.0               0.0             932.0   \n",
       "3   332.5  142.5      0.0  228.0               0.0             932.0   \n",
       "4   198.6  132.4      0.0  192.0               0.0             978.4   \n",
       "\n",
       "   fine_aggregate  age   strength  \n",
       "0           676.0   28  79.986111  \n",
       "1           676.0   28  61.887366  \n",
       "2           594.0  270  40.269535  \n",
       "3           594.0  365  41.052780  \n",
       "4           825.5  360  44.296075  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"concrete_data.csv\")\n",
    "data .head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Skew</th>\n",
       "      <th>Skew Label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feature</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>3.269177</td>\n",
       "      <td>Highly Skewed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>superplasticizer</th>\n",
       "      <td>0.908113</td>\n",
       "      <td>Moderately Skewed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slag</th>\n",
       "      <td>0.800737</td>\n",
       "      <td>Moderately Skewed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fly_ash</th>\n",
       "      <td>0.537445</td>\n",
       "      <td>Moderately Skewed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cement</th>\n",
       "      <td>0.509517</td>\n",
       "      <td>Moderately Skewed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>strength</th>\n",
       "      <td>0.416922</td>\n",
       "      <td>Approximately Symmetric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>water</th>\n",
       "      <td>0.074324</td>\n",
       "      <td>Approximately Symmetric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coarse_aggregate</th>\n",
       "      <td>-0.040206</td>\n",
       "      <td>Approximately Symmetric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fine_aggregate</th>\n",
       "      <td>-0.252979</td>\n",
       "      <td>Approximately Symmetric</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Skew               Skew Label\n",
       "Feature                                            \n",
       "age               3.269177            Highly Skewed\n",
       "superplasticizer  0.908113        Moderately Skewed\n",
       "slag              0.800737        Moderately Skewed\n",
       "fly_ash           0.537445        Moderately Skewed\n",
       "cement            0.509517        Moderately Skewed\n",
       "strength          0.416922  Approximately Symmetric\n",
       "water             0.074324  Approximately Symmetric\n",
       "coarse_aggregate -0.040206  Approximately Symmetric\n",
       "fine_aggregate   -0.252979  Approximately Symmetric"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_info = pd.DataFrame({'Feature': [],\n",
    "                         'Skew':[],\n",
    "                         'Skew Label': []})\n",
    "\n",
    "\n",
    "for col in data:\n",
    "    skew = data[col].skew().astype('float')\n",
    "    label = skew_label(data, col)\n",
    "\n",
    "    data_info = data_info.append({'Feature': col,\n",
    "                         'Skew':skew,\n",
    "                         'Skew Label': label}, ignore_index = True).sort_values('Skew', ascending = False)\n",
    "\n",
    "data_info.set_index('Feature', inplace = True)\n",
    "data_info\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Skew</th>\n",
       "      <th>Skew Label</th>\n",
       "      <th>Outliers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feature</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>3.269177</td>\n",
       "      <td>Highly Skewed</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>superplasticizer</th>\n",
       "      <td>0.908113</td>\n",
       "      <td>Moderately Skewed</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slag</th>\n",
       "      <td>0.800737</td>\n",
       "      <td>Moderately Skewed</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fly_ash</th>\n",
       "      <td>0.537445</td>\n",
       "      <td>Moderately Skewed</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cement</th>\n",
       "      <td>0.509517</td>\n",
       "      <td>Moderately Skewed</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>strength</th>\n",
       "      <td>0.416922</td>\n",
       "      <td>Approximately Symmetric</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>water</th>\n",
       "      <td>0.074324</td>\n",
       "      <td>Approximately Symmetric</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coarse_aggregate</th>\n",
       "      <td>-0.040206</td>\n",
       "      <td>Approximately Symmetric</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fine_aggregate</th>\n",
       "      <td>-0.252979</td>\n",
       "      <td>Approximately Symmetric</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Skew               Skew Label Outliers\n",
       "Feature                                                     \n",
       "age               3.269177            Highly Skewed       59\n",
       "superplasticizer  0.908113        Moderately Skewed       10\n",
       "slag              0.800737        Moderately Skewed        2\n",
       "fly_ash           0.537445        Moderately Skewed        0\n",
       "cement            0.509517        Moderately Skewed        0\n",
       "strength          0.416922  Approximately Symmetric        4\n",
       "water             0.074324  Approximately Symmetric        9\n",
       "coarse_aggregate -0.040206  Approximately Symmetric        0\n",
       "fine_aggregate   -0.252979  Approximately Symmetric        5"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_info['Outliers'] = ''\n",
    "for col in data:\n",
    "    outlier = countoutliers(data, col)\n",
    "    data_info.loc[col, 'Outliers'] = outlier\n",
    "\n",
    "data_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Drop outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the `age` column has the highest skewness value, I decided to drop the rows with outliers in the `age`.\n",
    "To maintain the interpretability of the model, log transformation and scaling will not be used for this project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cement</th>\n",
       "      <th>slag</th>\n",
       "      <th>fly_ash</th>\n",
       "      <th>water</th>\n",
       "      <th>superplasticizer</th>\n",
       "      <th>coarse_aggregate</th>\n",
       "      <th>fine_aggregate</th>\n",
       "      <th>age</th>\n",
       "      <th>strength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>540.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>28</td>\n",
       "      <td>79.986111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>540.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1055.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>28</td>\n",
       "      <td>61.887366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>266.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>670.0</td>\n",
       "      <td>90</td>\n",
       "      <td>47.029847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>380.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>28</td>\n",
       "      <td>36.447770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>266.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>670.0</td>\n",
       "      <td>28</td>\n",
       "      <td>45.854291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1025</th>\n",
       "      <td>276.4</td>\n",
       "      <td>116.0</td>\n",
       "      <td>90.3</td>\n",
       "      <td>179.6</td>\n",
       "      <td>8.9</td>\n",
       "      <td>870.1</td>\n",
       "      <td>768.3</td>\n",
       "      <td>28</td>\n",
       "      <td>44.284354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1026</th>\n",
       "      <td>322.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>115.6</td>\n",
       "      <td>196.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>817.9</td>\n",
       "      <td>813.4</td>\n",
       "      <td>28</td>\n",
       "      <td>31.178794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1027</th>\n",
       "      <td>148.5</td>\n",
       "      <td>139.4</td>\n",
       "      <td>108.6</td>\n",
       "      <td>192.7</td>\n",
       "      <td>6.1</td>\n",
       "      <td>892.4</td>\n",
       "      <td>780.0</td>\n",
       "      <td>28</td>\n",
       "      <td>23.696601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1028</th>\n",
       "      <td>159.1</td>\n",
       "      <td>186.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>175.6</td>\n",
       "      <td>11.3</td>\n",
       "      <td>989.6</td>\n",
       "      <td>788.9</td>\n",
       "      <td>28</td>\n",
       "      <td>32.768036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1029</th>\n",
       "      <td>260.9</td>\n",
       "      <td>100.5</td>\n",
       "      <td>78.3</td>\n",
       "      <td>200.6</td>\n",
       "      <td>8.6</td>\n",
       "      <td>864.5</td>\n",
       "      <td>761.5</td>\n",
       "      <td>28</td>\n",
       "      <td>32.401235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>971 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      cement   slag  fly_ash  water  superplasticizer  coarse_aggregate  \\\n",
       "0      540.0    0.0      0.0  162.0               2.5            1040.0   \n",
       "1      540.0    0.0      0.0  162.0               2.5            1055.0   \n",
       "5      266.0  114.0      0.0  228.0               0.0             932.0   \n",
       "7      380.0   95.0      0.0  228.0               0.0             932.0   \n",
       "8      266.0  114.0      0.0  228.0               0.0             932.0   \n",
       "...      ...    ...      ...    ...               ...               ...   \n",
       "1025   276.4  116.0     90.3  179.6               8.9             870.1   \n",
       "1026   322.2    0.0    115.6  196.0              10.4             817.9   \n",
       "1027   148.5  139.4    108.6  192.7               6.1             892.4   \n",
       "1028   159.1  186.7      0.0  175.6              11.3             989.6   \n",
       "1029   260.9  100.5     78.3  200.6               8.6             864.5   \n",
       "\n",
       "      fine_aggregate  age   strength  \n",
       "0              676.0   28  79.986111  \n",
       "1              676.0   28  61.887366  \n",
       "5              670.0   90  47.029847  \n",
       "7              594.0   28  36.447770  \n",
       "8              670.0   28  45.854291  \n",
       "...              ...  ...        ...  \n",
       "1025           768.3   28  44.284354  \n",
       "1026           813.4   28  31.178794  \n",
       "1027           780.0   28  23.696601  \n",
       "1028           788.9   28  32.768036  \n",
       "1029           761.5   28  32.401235  \n",
       "\n",
       "[971 rows x 9 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clean = removeoutliers(data, 'age')\n",
    "data_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#separate features\n",
    "y = data_clean['strength']\n",
    "X = data_clean.drop(['strength'], axis = 1)\n",
    "\n",
    "#initialize model\n",
    "LR = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, shuffle = True, random_state = 1006)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>Features</th>\n",
       "      <th>Coefficients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>age</td>\n",
       "      <td>0.313983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>superplasticizer</td>\n",
       "      <td>0.181774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cement</td>\n",
       "      <td>0.107064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>slag</td>\n",
       "      <td>0.083688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fly_ash</td>\n",
       "      <td>0.061572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>fine_aggregate</td>\n",
       "      <td>0.000080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>coarse_aggregate</td>\n",
       "      <td>-0.002352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>water</td>\n",
       "      <td>-0.196084</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Features  Coefficients\n",
       "7               age      0.313983\n",
       "4  superplasticizer      0.181774\n",
       "0            cement      0.107064\n",
       "1              slag      0.083688\n",
       "2           fly_ash      0.061572\n",
       "6    fine_aggregate      0.000080\n",
       "5  coarse_aggregate     -0.002352\n",
       "3             water     -0.196084"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fit model\n",
    "LR.fit(X_train, y_train)\n",
    "\n",
    "#determine coefficients\n",
    "features = X.columns\n",
    "coefficients = LR.coef_\n",
    "\n",
    "coef_df = pd.DataFrame(zip(features, coefficients)).sort_values(1, ascending = False)\n",
    "coef_df.columns = ['Features', 'Coefficients']\n",
    "\n",
    "coef_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>cement</th>\n",
       "      <th>slag</th>\n",
       "      <th>fly_ash</th>\n",
       "      <th>water</th>\n",
       "      <th>superplasticizer</th>\n",
       "      <th>coarse_aggregate</th>\n",
       "      <th>fine_aggregate</th>\n",
       "      <th>age</th>\n",
       "      <th>actual_strength</th>\n",
       "      <th>predicted_strength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>133.00</td>\n",
       "      <td>200.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>192.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>927.4</td>\n",
       "      <td>839.20</td>\n",
       "      <td>3</td>\n",
       "      <td>6.883728</td>\n",
       "      <td>13.856883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>218.23</td>\n",
       "      <td>54.64</td>\n",
       "      <td>123.78</td>\n",
       "      <td>140.75</td>\n",
       "      <td>11.91</td>\n",
       "      <td>1075.7</td>\n",
       "      <td>792.67</td>\n",
       "      <td>56</td>\n",
       "      <td>61.990787</td>\n",
       "      <td>46.941225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>108.30</td>\n",
       "      <td>162.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>203.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>938.2</td>\n",
       "      <td>849.00</td>\n",
       "      <td>7</td>\n",
       "      <td>7.723510</td>\n",
       "      <td>7.042076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>424.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>132.00</td>\n",
       "      <td>178.00</td>\n",
       "      <td>8.48</td>\n",
       "      <td>882.0</td>\n",
       "      <td>750.00</td>\n",
       "      <td>3</td>\n",
       "      <td>32.011386</td>\n",
       "      <td>42.629712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>331.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>192.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1025.0</td>\n",
       "      <td>821.00</td>\n",
       "      <td>7</td>\n",
       "      <td>17.436848</td>\n",
       "      <td>19.342796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cement    slag  fly_ash   water  superplasticizer  coarse_aggregate  \\\n",
       "0  133.00  200.00     0.00  192.00              0.00             927.4   \n",
       "1  218.23   54.64   123.78  140.75             11.91            1075.7   \n",
       "2  108.30  162.40     0.00  203.50              0.00             938.2   \n",
       "3  424.00   22.00   132.00  178.00              8.48             882.0   \n",
       "4  331.00    0.00     0.00  192.00              0.00            1025.0   \n",
       "\n",
       "   fine_aggregate  age  actual_strength  predicted_strength  \n",
       "0          839.20    3         6.883728           13.856883  \n",
       "1          792.67   56        61.990787           46.941225  \n",
       "2          849.00    7         7.723510            7.042076  \n",
       "3          750.00    3        32.011386           42.629712  \n",
       "4          821.00    7        17.436848           19.342796  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#predict values\n",
    "y_pred = LR.predict(X_test)\n",
    "\n",
    "#create predictions dataframe\n",
    "predictions_df = pd.DataFrame(zip(y_test, y_pred))\n",
    "predictions_df.columns = ['actual_strength', 'predicted_strength']\n",
    "\n",
    "#merge features value to the predictions dataframe\n",
    "predictions_df = X_test.reset_index(drop = True).join(predictions_df)\n",
    "predictions_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>actual_strength</th>\n",
       "      <th>predicted_strength</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21.294705</td>\n",
       "      <td>29.266367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>28.861817</td>\n",
       "      <td>29.764591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>31.316627</td>\n",
       "      <td>27.246846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>39.495168</td>\n",
       "      <td>34.594474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>55.461843</td>\n",
       "      <td>49.458156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>37.648524</td>\n",
       "      <td>43.090488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>68.597937</td>\n",
       "      <td>73.120272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>44.345717</td>\n",
       "      <td>53.816355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>39.779318</td>\n",
       "      <td>54.641045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     actual_strength  predicted_strength\n",
       "age                                     \n",
       "3          21.294705           29.266367\n",
       "7          28.861817           29.764591\n",
       "14         31.316627           27.246846\n",
       "28         39.495168           34.594474\n",
       "56         55.461843           49.458156\n",
       "90         37.648524           43.090488\n",
       "91         68.597937           73.120272\n",
       "100        44.345717           53.816355\n",
       "120        39.779318           54.641045"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get mean predictions per age group\n",
    "\n",
    "age_group = predictions_df.groupby(['age'])[['actual_strength', 'predicted_strength']].mean()\n",
    "age_group\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Squared: 0.73\n"
     ]
    }
   ],
   "source": [
    "#evaluate linear regression model\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(f'R-Squared: {round(r2,2)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize actual vs predicted values\n",
    "sns.set(style = 'ticks',\n",
    "        rc={'figure.figsize':(20,10)})\n",
    "\n",
    "sns.regplot(x = 'predicted_strength', y = 'actual_strength', data = predictions_df)\n",
    "\n",
    "plt.title('Actual vs Predictions', fontsize = 15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import model\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "#initialize random forest regressor model\n",
    "RND = RandomForestRegressor(ccp_alpha = 0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Features</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>age</td>\n",
       "      <td>0.348123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cement</td>\n",
       "      <td>0.289162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>superplasticizer</td>\n",
       "      <td>0.105275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>water</td>\n",
       "      <td>0.097265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>slag</td>\n",
       "      <td>0.077716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>fine_aggregate</td>\n",
       "      <td>0.030860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>coarse_aggregate</td>\n",
       "      <td>0.030404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fly_ash</td>\n",
       "      <td>0.021195</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Features  Importance\n",
       "7               age    0.348123\n",
       "0            cement    0.289162\n",
       "4  superplasticizer    0.105275\n",
       "3             water    0.097265\n",
       "1              slag    0.077716\n",
       "6    fine_aggregate    0.030860\n",
       "5  coarse_aggregate    0.030404\n",
       "2           fly_ash    0.021195"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fit model\n",
    "RND.fit(X_train, y_train)\n",
    "\n",
    "#determine feature importances\n",
    "\n",
    "features = X.columns\n",
    "importances = RND.feature_importances_\n",
    "\n",
    "importances_df = pd.DataFrame(zip(features, importances)).sort_values(1, ascending = False)\n",
    "importances_df.columns = ['Features', 'Importance']\n",
    "\n",
    "importances_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>cement</th>\n",
       "      <th>slag</th>\n",
       "      <th>fly_ash</th>\n",
       "      <th>water</th>\n",
       "      <th>superplasticizer</th>\n",
       "      <th>coarse_aggregate</th>\n",
       "      <th>fine_aggregate</th>\n",
       "      <th>age</th>\n",
       "      <th>actual_strength</th>\n",
       "      <th>predicted_strength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>133.00</td>\n",
       "      <td>200.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>192.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>927.4</td>\n",
       "      <td>839.20</td>\n",
       "      <td>3</td>\n",
       "      <td>6.883728</td>\n",
       "      <td>5.578633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>218.23</td>\n",
       "      <td>54.64</td>\n",
       "      <td>123.78</td>\n",
       "      <td>140.75</td>\n",
       "      <td>11.91</td>\n",
       "      <td>1075.7</td>\n",
       "      <td>792.67</td>\n",
       "      <td>56</td>\n",
       "      <td>61.990787</td>\n",
       "      <td>58.222761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>108.30</td>\n",
       "      <td>162.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>203.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>938.2</td>\n",
       "      <td>849.00</td>\n",
       "      <td>7</td>\n",
       "      <td>7.723510</td>\n",
       "      <td>9.354121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>424.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>132.00</td>\n",
       "      <td>178.00</td>\n",
       "      <td>8.48</td>\n",
       "      <td>882.0</td>\n",
       "      <td>750.00</td>\n",
       "      <td>3</td>\n",
       "      <td>32.011386</td>\n",
       "      <td>31.803304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>331.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>192.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1025.0</td>\n",
       "      <td>821.00</td>\n",
       "      <td>7</td>\n",
       "      <td>17.436848</td>\n",
       "      <td>15.913506</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cement    slag  fly_ash   water  superplasticizer  coarse_aggregate  \\\n",
       "0  133.00  200.00     0.00  192.00              0.00             927.4   \n",
       "1  218.23   54.64   123.78  140.75             11.91            1075.7   \n",
       "2  108.30  162.40     0.00  203.50              0.00             938.2   \n",
       "3  424.00   22.00   132.00  178.00              8.48             882.0   \n",
       "4  331.00    0.00     0.00  192.00              0.00            1025.0   \n",
       "\n",
       "   fine_aggregate  age  actual_strength  predicted_strength  \n",
       "0          839.20    3         6.883728            5.578633  \n",
       "1          792.67   56        61.990787           58.222761  \n",
       "2          849.00    7         7.723510            9.354121  \n",
       "3          750.00    3        32.011386           31.803304  \n",
       "4          821.00    7        17.436848           15.913506  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#predict values\n",
    "y_pred = RND.predict(X_test)\n",
    "\n",
    "#create predictions dataframe\n",
    "predictions_df = pd.DataFrame(zip(y_test, y_pred))\n",
    "predictions_df.columns = ['actual_strength', 'predicted_strength']\n",
    "\n",
    "#merge features value to the predictions dataframe\n",
    "predictions_df = X_test.reset_index(drop = True).join(predictions_df)\n",
    "predictions_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>actual_strength</th>\n",
       "      <th>predicted_strength</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21.294705</td>\n",
       "      <td>21.322920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>28.861817</td>\n",
       "      <td>25.495983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>31.316627</td>\n",
       "      <td>30.638019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>39.495168</td>\n",
       "      <td>39.488878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>55.461843</td>\n",
       "      <td>53.653240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>37.648524</td>\n",
       "      <td>37.449691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>68.597937</td>\n",
       "      <td>67.115441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>44.345717</td>\n",
       "      <td>44.983927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>39.779318</td>\n",
       "      <td>38.812228</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     actual_strength  predicted_strength\n",
       "age                                     \n",
       "3          21.294705           21.322920\n",
       "7          28.861817           25.495983\n",
       "14         31.316627           30.638019\n",
       "28         39.495168           39.488878\n",
       "56         55.461843           53.653240\n",
       "90         37.648524           37.449691\n",
       "91         68.597937           67.115441\n",
       "100        44.345717           44.983927\n",
       "120        39.779318           38.812228"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get mean predictions per age group\n",
    "\n",
    "age_group = predictions_df.groupby(['age'])[['actual_strength', 'predicted_strength']].mean()\n",
    "age_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Squared: 0.9\n"
     ]
    }
   ],
   "source": [
    "#evaluate random forest regressor model\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(f'R-Squared: {round(r2,2)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize actual vs predicted values\n",
    "sns.set(style = 'ticks',\n",
    "        rc={'figure.figsize':(20,10)})\n",
    "\n",
    "sns.regplot(x = 'predicted_strength', y = 'actual_strength', data = predictions_df)\n",
    "\n",
    "plt.title('Actual vs Predictions', fontsize = 15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[DecisionTreeRegressor(max_features='auto', random_state=1645727303),\n",
       " DecisionTreeRegressor(max_features='auto', random_state=843231027),\n",
       " DecisionTreeRegressor(max_features='auto', random_state=511606565),\n",
       " DecisionTreeRegressor(max_features='auto', random_state=353565710),\n",
       " DecisionTreeRegressor(max_features='auto', random_state=1005945732)]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RND.estimators_[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "tree.plot_tree(RND.estimators_[0], filled = True);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.8 ('base')",
   "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": "3.8.8"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "68fddc5acd51dc20c45bbbe191aa2f6a38a80287ab1b8b33a66121335d90c70f"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}