ML_Facebook_Like_Predictor_Regression_English.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Applying a linear regression algorithm to real world data with the purpose of predicting a potential number of \"likes\" on a future post of a Cosmetics Brand on Facebook.\n",
    "Dataset acquired from: https://archive.ics.uci.edu/ml/datasets/Facebook+metrics\n"
   ]
  },
  {
   "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",
    "%matplotlib inline\n",
    "##\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn import metrics\n",
    "##\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "##\n",
    "from sklearn.neighbors import KNeighborsRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Insertion of the dataset and data exploration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fb=pd.read_csv('data/Facebook_Cosmetics_Brand.csv',';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Page total likes', 'Type', 'Category', 'Post Month', 'Post Weekday',\n",
       "       'Post Hour', 'Paid', 'Lifetime Post Total Reach',\n",
       "       'Lifetime Post Total Impressions', 'Lifetime Engaged Users',\n",
       "       'Lifetime Post Consumers', 'Lifetime Post Consumptions',\n",
       "       'Lifetime Post Impressions by people who have liked your Page',\n",
       "       'Lifetime Post reach by people who like your Page',\n",
       "       'Lifetime People who have liked your Page and engaged with your post',\n",
       "       'comment', 'like', 'share', 'Total Interactions'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Checking for null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Page total likes                                                       False\n",
       "Type                                                                   False\n",
       "Category                                                               False\n",
       "Post Month                                                             False\n",
       "Post Weekday                                                           False\n",
       "Post Hour                                                              False\n",
       "Paid                                                                    True\n",
       "Lifetime Post Total Reach                                              False\n",
       "Lifetime Post Total Impressions                                        False\n",
       "Lifetime Engaged Users                                                 False\n",
       "Lifetime Post Consumers                                                False\n",
       "Lifetime Post Consumptions                                             False\n",
       "Lifetime Post Impressions by people who have liked your Page           False\n",
       "Lifetime Post reach by people who like your Page                       False\n",
       "Lifetime People who have liked your Page and engaged with your post    False\n",
       "comment                                                                False\n",
       "like                                                                    True\n",
       "share                                                                   True\n",
       "Total Interactions                                                     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Sum of null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Page total likes                                                       0\n",
       "Type                                                                   0\n",
       "Category                                                               0\n",
       "Post Month                                                             0\n",
       "Post Weekday                                                           0\n",
       "Post Hour                                                              0\n",
       "Paid                                                                   1\n",
       "Lifetime Post Total Reach                                              0\n",
       "Lifetime Post Total Impressions                                        0\n",
       "Lifetime Engaged Users                                                 0\n",
       "Lifetime Post Consumers                                                0\n",
       "Lifetime Post Consumptions                                             0\n",
       "Lifetime Post Impressions by people who have liked your Page           0\n",
       "Lifetime Post reach by people who like your Page                       0\n",
       "Lifetime People who have liked your Page and engaged with your post    0\n",
       "comment                                                                0\n",
       "like                                                                   1\n",
       "share                                                                  4\n",
       "Total Interactions                                                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Showing dataset's shape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 19)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Dropping null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "fb=fb.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Showing dataset's shape again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(495, 19)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Showing the first five entries of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Page total likes</th>\n",
       "      <th>Type</th>\n",
       "      <th>Category</th>\n",
       "      <th>Post Month</th>\n",
       "      <th>Post Weekday</th>\n",
       "      <th>Post Hour</th>\n",
       "      <th>Paid</th>\n",
       "      <th>Lifetime Post Total Reach</th>\n",
       "      <th>Lifetime Post Total Impressions</th>\n",
       "      <th>Lifetime Engaged Users</th>\n",
       "      <th>Lifetime Post Consumers</th>\n",
       "      <th>Lifetime Post Consumptions</th>\n",
       "      <th>Lifetime Post Impressions by people who have liked your Page</th>\n",
       "      <th>Lifetime Post reach by people who like your Page</th>\n",
       "      <th>Lifetime People who have liked your Page and engaged with your post</th>\n",
       "      <th>comment</th>\n",
       "      <th>like</th>\n",
       "      <th>share</th>\n",
       "      <th>Total Interactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>139441</td>\n",
       "      <td>Photo</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2752</td>\n",
       "      <td>5091</td>\n",
       "      <td>178</td>\n",
       "      <td>109</td>\n",
       "      <td>159</td>\n",
       "      <td>3078</td>\n",
       "      <td>1640</td>\n",
       "      <td>119</td>\n",
       "      <td>4</td>\n",
       "      <td>79.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>139441</td>\n",
       "      <td>Status</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10460</td>\n",
       "      <td>19057</td>\n",
       "      <td>1457</td>\n",
       "      <td>1361</td>\n",
       "      <td>1674</td>\n",
       "      <td>11710</td>\n",
       "      <td>6112</td>\n",
       "      <td>1108</td>\n",
       "      <td>5</td>\n",
       "      <td>130.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>139441</td>\n",
       "      <td>Photo</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2413</td>\n",
       "      <td>4373</td>\n",
       "      <td>177</td>\n",
       "      <td>113</td>\n",
       "      <td>154</td>\n",
       "      <td>2812</td>\n",
       "      <td>1503</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>139441</td>\n",
       "      <td>Photo</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50128</td>\n",
       "      <td>87991</td>\n",
       "      <td>2211</td>\n",
       "      <td>790</td>\n",
       "      <td>1119</td>\n",
       "      <td>61027</td>\n",
       "      <td>32048</td>\n",
       "      <td>1386</td>\n",
       "      <td>58</td>\n",
       "      <td>1572.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>1777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>139441</td>\n",
       "      <td>Photo</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7244</td>\n",
       "      <td>13594</td>\n",
       "      <td>671</td>\n",
       "      <td>410</td>\n",
       "      <td>580</td>\n",
       "      <td>6228</td>\n",
       "      <td>3200</td>\n",
       "      <td>396</td>\n",
       "      <td>19</td>\n",
       "      <td>325.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Page total likes    Type  Category  Post Month  Post Weekday  Post Hour  \\\n",
       "0            139441   Photo         2          12             4          3   \n",
       "1            139441  Status         2          12             3         10   \n",
       "2            139441   Photo         3          12             3          3   \n",
       "3            139441   Photo         2          12             2         10   \n",
       "4            139441   Photo         2          12             2          3   \n",
       "\n",
       "   Paid  Lifetime Post Total Reach  Lifetime Post Total Impressions  \\\n",
       "0   0.0                       2752                             5091   \n",
       "1   0.0                      10460                            19057   \n",
       "2   0.0                       2413                             4373   \n",
       "3   1.0                      50128                            87991   \n",
       "4   0.0                       7244                            13594   \n",
       "\n",
       "   Lifetime Engaged Users  Lifetime Post Consumers  \\\n",
       "0                     178                      109   \n",
       "1                    1457                     1361   \n",
       "2                     177                      113   \n",
       "3                    2211                      790   \n",
       "4                     671                      410   \n",
       "\n",
       "   Lifetime Post Consumptions  \\\n",
       "0                         159   \n",
       "1                        1674   \n",
       "2                         154   \n",
       "3                        1119   \n",
       "4                         580   \n",
       "\n",
       "   Lifetime Post Impressions by people who have liked your Page  \\\n",
       "0                                               3078              \n",
       "1                                              11710              \n",
       "2                                               2812              \n",
       "3                                              61027              \n",
       "4                                               6228              \n",
       "\n",
       "   Lifetime Post reach by people who like your Page  \\\n",
       "0                                              1640   \n",
       "1                                              6112   \n",
       "2                                              1503   \n",
       "3                                             32048   \n",
       "4                                              3200   \n",
       "\n",
       "   Lifetime People who have liked your Page and engaged with your post  \\\n",
       "0                                                119                     \n",
       "1                                               1108                     \n",
       "2                                                132                     \n",
       "3                                               1386                     \n",
       "4                                                396                     \n",
       "\n",
       "   comment    like  share  Total Interactions  \n",
       "0        4    79.0   17.0                 100  \n",
       "1        5   130.0   29.0                 164  \n",
       "2        0    66.0   14.0                  80  \n",
       "3       58  1572.0  147.0                1777  \n",
       "4       19   325.0   49.0                 393  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Describing the columns of the table.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Page total likes</th>\n",
       "      <th>Category</th>\n",
       "      <th>Post Month</th>\n",
       "      <th>Post Weekday</th>\n",
       "      <th>Post Hour</th>\n",
       "      <th>Paid</th>\n",
       "      <th>Lifetime Post Total Reach</th>\n",
       "      <th>Lifetime Post Total Impressions</th>\n",
       "      <th>Lifetime Engaged Users</th>\n",
       "      <th>Lifetime Post Consumers</th>\n",
       "      <th>Lifetime Post Consumptions</th>\n",
       "      <th>Lifetime Post Impressions by people who have liked your Page</th>\n",
       "      <th>Lifetime Post reach by people who like your Page</th>\n",
       "      <th>Lifetime People who have liked your Page and engaged with your post</th>\n",
       "      <th>comment</th>\n",
       "      <th>like</th>\n",
       "      <th>share</th>\n",
       "      <th>Total Interactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>4.950000e+02</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>4.950000e+02</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>495.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>123173.268687</td>\n",
       "      <td>1.886869</td>\n",
       "      <td>7.028283</td>\n",
       "      <td>4.133333</td>\n",
       "      <td>7.844444</td>\n",
       "      <td>0.280808</td>\n",
       "      <td>14028.101010</td>\n",
       "      <td>2.985698e+04</td>\n",
       "      <td>926.830303</td>\n",
       "      <td>804.155556</td>\n",
       "      <td>1425.921212</td>\n",
       "      <td>1.691628e+04</td>\n",
       "      <td>6641.355556</td>\n",
       "      <td>614.135354</td>\n",
       "      <td>7.557576</td>\n",
       "      <td>179.145455</td>\n",
       "      <td>27.264646</td>\n",
       "      <td>213.967677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16203.818031</td>\n",
       "      <td>0.853268</td>\n",
       "      <td>3.304274</td>\n",
       "      <td>2.030735</td>\n",
       "      <td>4.385064</td>\n",
       "      <td>0.449849</td>\n",
       "      <td>22821.050008</td>\n",
       "      <td>7.714291e+04</td>\n",
       "      <td>987.713267</td>\n",
       "      <td>885.184440</td>\n",
       "      <td>2007.663460</td>\n",
       "      <td>6.007402e+04</td>\n",
       "      <td>7700.266455</td>\n",
       "      <td>614.346297</td>\n",
       "      <td>21.274384</td>\n",
       "      <td>324.412161</td>\n",
       "      <td>42.656388</td>\n",
       "      <td>381.677449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>81370.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>238.000000</td>\n",
       "      <td>5.700000e+02</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.670000e+02</td>\n",
       "      <td>236.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>112324.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3331.000000</td>\n",
       "      <td>5.798000e+03</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>335.000000</td>\n",
       "      <td>512.500000</td>\n",
       "      <td>4.073500e+03</td>\n",
       "      <td>2213.000000</td>\n",
       "      <td>297.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>129600.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5290.000000</td>\n",
       "      <td>9.084000e+03</td>\n",
       "      <td>630.000000</td>\n",
       "      <td>555.000000</td>\n",
       "      <td>861.000000</td>\n",
       "      <td>6.282000e+03</td>\n",
       "      <td>3478.000000</td>\n",
       "      <td>416.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>125.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>136393.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13248.000000</td>\n",
       "      <td>2.250300e+04</td>\n",
       "      <td>1062.000000</td>\n",
       "      <td>969.000000</td>\n",
       "      <td>1479.000000</td>\n",
       "      <td>1.514300e+04</td>\n",
       "      <td>8018.000000</td>\n",
       "      <td>658.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>188.000000</td>\n",
       "      <td>32.500000</td>\n",
       "      <td>231.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>139441.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>180480.000000</td>\n",
       "      <td>1.110282e+06</td>\n",
       "      <td>11452.000000</td>\n",
       "      <td>11328.000000</td>\n",
       "      <td>19779.000000</td>\n",
       "      <td>1.107833e+06</td>\n",
       "      <td>51456.000000</td>\n",
       "      <td>4376.000000</td>\n",
       "      <td>372.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>790.000000</td>\n",
       "      <td>6334.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Page total likes    Category  Post Month  Post Weekday   Post Hour  \\\n",
       "count        495.000000  495.000000  495.000000    495.000000  495.000000   \n",
       "mean      123173.268687    1.886869    7.028283      4.133333    7.844444   \n",
       "std        16203.818031    0.853268    3.304274      2.030735    4.385064   \n",
       "min        81370.000000    1.000000    1.000000      1.000000    1.000000   \n",
       "25%       112324.000000    1.000000    4.000000      2.000000    3.000000   \n",
       "50%       129600.000000    2.000000    7.000000      4.000000    9.000000   \n",
       "75%       136393.000000    3.000000   10.000000      6.000000   11.000000   \n",
       "max       139441.000000    3.000000   12.000000      7.000000   23.000000   \n",
       "\n",
       "             Paid  Lifetime Post Total Reach  Lifetime Post Total Impressions  \\\n",
       "count  495.000000                 495.000000                     4.950000e+02   \n",
       "mean     0.280808               14028.101010                     2.985698e+04   \n",
       "std      0.449849               22821.050008                     7.714291e+04   \n",
       "min      0.000000                 238.000000                     5.700000e+02   \n",
       "25%      0.000000                3331.000000                     5.798000e+03   \n",
       "50%      0.000000                5290.000000                     9.084000e+03   \n",
       "75%      1.000000               13248.000000                     2.250300e+04   \n",
       "max      1.000000              180480.000000                     1.110282e+06   \n",
       "\n",
       "       Lifetime Engaged Users  Lifetime Post Consumers  \\\n",
       "count              495.000000               495.000000   \n",
       "mean               926.830303               804.155556   \n",
       "std                987.713267               885.184440   \n",
       "min                  9.000000                 9.000000   \n",
       "25%                399.000000               335.000000   \n",
       "50%                630.000000               555.000000   \n",
       "75%               1062.000000               969.000000   \n",
       "max              11452.000000             11328.000000   \n",
       "\n",
       "       Lifetime Post Consumptions  \\\n",
       "count                  495.000000   \n",
       "mean                  1425.921212   \n",
       "std                   2007.663460   \n",
       "min                      9.000000   \n",
       "25%                    512.500000   \n",
       "50%                    861.000000   \n",
       "75%                   1479.000000   \n",
       "max                  19779.000000   \n",
       "\n",
       "       Lifetime Post Impressions by people who have liked your Page  \\\n",
       "count                                       4.950000e+02              \n",
       "mean                                        1.691628e+04              \n",
       "std                                         6.007402e+04              \n",
       "min                                         5.670000e+02              \n",
       "25%                                         4.073500e+03              \n",
       "50%                                         6.282000e+03              \n",
       "75%                                         1.514300e+04              \n",
       "max                                         1.107833e+06              \n",
       "\n",
       "       Lifetime Post reach by people who like your Page  \\\n",
       "count                                        495.000000   \n",
       "mean                                        6641.355556   \n",
       "std                                         7700.266455   \n",
       "min                                          236.000000   \n",
       "25%                                         2213.000000   \n",
       "50%                                         3478.000000   \n",
       "75%                                         8018.000000   \n",
       "max                                        51456.000000   \n",
       "\n",
       "       Lifetime People who have liked your Page and engaged with your post  \\\n",
       "count                                         495.000000                     \n",
       "mean                                          614.135354                     \n",
       "std                                           614.346297                     \n",
       "min                                             9.000000                     \n",
       "25%                                           297.500000                     \n",
       "50%                                           416.000000                     \n",
       "75%                                           658.500000                     \n",
       "max                                          4376.000000                     \n",
       "\n",
       "          comment         like       share  Total Interactions  \n",
       "count  495.000000   495.000000  495.000000          495.000000  \n",
       "mean     7.557576   179.145455   27.264646          213.967677  \n",
       "std     21.274384   324.412161   42.656388          381.677449  \n",
       "min      0.000000     0.000000    0.000000            0.000000  \n",
       "25%      1.000000    57.000000   10.000000           72.000000  \n",
       "50%      3.000000   101.000000   19.000000          125.000000  \n",
       "75%      7.000000   188.000000   32.500000          231.000000  \n",
       "max    372.000000  5172.000000  790.000000         6334.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Replacing the values of type Object with unique int32 values and creating a new column \"Type_c\" with those values to assist later into the data analysis of the dataset. \n",
    "##### \"Photo\"=0, \"Status\"=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "TypeString = fb.Type\n",
    "TypeVal = fb.Type.unique()\n",
    "TypeValCount = len(TypeVal)\n",
    "Type_dict = {}\n",
    "for i in range(0,TypeValCount):\n",
    "    Type_dict[TypeVal[i]] = i\n",
    "fb[\"Type_c\"] = fb.Type.map(Type_dict).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Page total likes</th>\n",
       "      <th>Type</th>\n",
       "      <th>Category</th>\n",
       "      <th>Post Month</th>\n",
       "      <th>Post Weekday</th>\n",
       "      <th>Post Hour</th>\n",
       "      <th>Paid</th>\n",
       "      <th>Lifetime Post Total Reach</th>\n",
       "      <th>Lifetime Post Total Impressions</th>\n",
       "      <th>Lifetime Engaged Users</th>\n",
       "      <th>Lifetime Post Consumers</th>\n",
       "      <th>Lifetime Post Consumptions</th>\n",
       "      <th>Lifetime Post Impressions by people who have liked your Page</th>\n",
       "      <th>Lifetime Post reach by people who like your Page</th>\n",
       "      <th>Lifetime People who have liked your Page and engaged with your post</th>\n",
       "      <th>comment</th>\n",
       "      <th>like</th>\n",
       "      <th>share</th>\n",
       "      <th>Total Interactions</th>\n",
       "      <th>Type_c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>139441</td>\n",
       "      <td>Photo</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2752</td>\n",
       "      <td>5091</td>\n",
       "      <td>178</td>\n",
       "      <td>109</td>\n",
       "      <td>159</td>\n",
       "      <td>3078</td>\n",
       "      <td>1640</td>\n",
       "      <td>119</td>\n",
       "      <td>4</td>\n",
       "      <td>79.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>139441</td>\n",
       "      <td>Status</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10460</td>\n",
       "      <td>19057</td>\n",
       "      <td>1457</td>\n",
       "      <td>1361</td>\n",
       "      <td>1674</td>\n",
       "      <td>11710</td>\n",
       "      <td>6112</td>\n",
       "      <td>1108</td>\n",
       "      <td>5</td>\n",
       "      <td>130.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>164</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>139441</td>\n",
       "      <td>Photo</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2413</td>\n",
       "      <td>4373</td>\n",
       "      <td>177</td>\n",
       "      <td>113</td>\n",
       "      <td>154</td>\n",
       "      <td>2812</td>\n",
       "      <td>1503</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>139441</td>\n",
       "      <td>Photo</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50128</td>\n",
       "      <td>87991</td>\n",
       "      <td>2211</td>\n",
       "      <td>790</td>\n",
       "      <td>1119</td>\n",
       "      <td>61027</td>\n",
       "      <td>32048</td>\n",
       "      <td>1386</td>\n",
       "      <td>58</td>\n",
       "      <td>1572.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>1777</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>139441</td>\n",
       "      <td>Photo</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7244</td>\n",
       "      <td>13594</td>\n",
       "      <td>671</td>\n",
       "      <td>410</td>\n",
       "      <td>580</td>\n",
       "      <td>6228</td>\n",
       "      <td>3200</td>\n",
       "      <td>396</td>\n",
       "      <td>19</td>\n",
       "      <td>325.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>393</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Page total likes    Type  Category  Post Month  Post Weekday  Post Hour  \\\n",
       "0            139441   Photo         2          12             4          3   \n",
       "1            139441  Status         2          12             3         10   \n",
       "2            139441   Photo         3          12             3          3   \n",
       "3            139441   Photo         2          12             2         10   \n",
       "4            139441   Photo         2          12             2          3   \n",
       "\n",
       "   Paid  Lifetime Post Total Reach  Lifetime Post Total Impressions  \\\n",
       "0   0.0                       2752                             5091   \n",
       "1   0.0                      10460                            19057   \n",
       "2   0.0                       2413                             4373   \n",
       "3   1.0                      50128                            87991   \n",
       "4   0.0                       7244                            13594   \n",
       "\n",
       "   Lifetime Engaged Users  Lifetime Post Consumers  \\\n",
       "0                     178                      109   \n",
       "1                    1457                     1361   \n",
       "2                     177                      113   \n",
       "3                    2211                      790   \n",
       "4                     671                      410   \n",
       "\n",
       "   Lifetime Post Consumptions  \\\n",
       "0                         159   \n",
       "1                        1674   \n",
       "2                         154   \n",
       "3                        1119   \n",
       "4                         580   \n",
       "\n",
       "   Lifetime Post Impressions by people who have liked your Page  \\\n",
       "0                                               3078              \n",
       "1                                              11710              \n",
       "2                                               2812              \n",
       "3                                              61027              \n",
       "4                                               6228              \n",
       "\n",
       "   Lifetime Post reach by people who like your Page  \\\n",
       "0                                              1640   \n",
       "1                                              6112   \n",
       "2                                              1503   \n",
       "3                                             32048   \n",
       "4                                              3200   \n",
       "\n",
       "   Lifetime People who have liked your Page and engaged with your post  \\\n",
       "0                                                119                     \n",
       "1                                               1108                     \n",
       "2                                                132                     \n",
       "3                                               1386                     \n",
       "4                                                396                     \n",
       "\n",
       "   comment    like  share  Total Interactions  Type_c  \n",
       "0        4    79.0   17.0                 100       0  \n",
       "1        5   130.0   29.0                 164       1  \n",
       "2        0    66.0   14.0                  80       0  \n",
       "3       58  1572.0  147.0                1777       0  \n",
       "4       19   325.0   49.0                 393       0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Page total likes                                                         int64\n",
       "Type                                                                    object\n",
       "Category                                                                 int64\n",
       "Post Month                                                               int64\n",
       "Post Weekday                                                             int64\n",
       "Post Hour                                                                int64\n",
       "Paid                                                                   float64\n",
       "Lifetime Post Total Reach                                                int64\n",
       "Lifetime Post Total Impressions                                          int64\n",
       "Lifetime Engaged Users                                                   int64\n",
       "Lifetime Post Consumers                                                  int64\n",
       "Lifetime Post Consumptions                                               int64\n",
       "Lifetime Post Impressions by people who have liked your Page             int64\n",
       "Lifetime Post reach by people who like your Page                         int64\n",
       "Lifetime People who have liked your Page and engaged with your post      int64\n",
       "comment                                                                  int64\n",
       "like                                                                   float64\n",
       "share                                                                  float64\n",
       "Total Interactions                                                       int64\n",
       "Type_c                                                                   int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Deleting unnecessary columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "fb.drop(labels = ['Type'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>Page total likes</th>\n",
       "      <th>Category</th>\n",
       "      <th>Post Month</th>\n",
       "      <th>Post Weekday</th>\n",
       "      <th>Post Hour</th>\n",
       "      <th>Paid</th>\n",
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       "      <th>Lifetime Post Consumptions</th>\n",
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       "      <th>Lifetime Post reach by people who like your Page</th>\n",
       "      <th>Lifetime People who have liked your Page and engaged with your post</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>5091</td>\n",
       "      <td>178</td>\n",
       "      <td>109</td>\n",
       "      <td>159</td>\n",
       "      <td>3078</td>\n",
       "      <td>1640</td>\n",
       "      <td>119</td>\n",
       "      <td>4</td>\n",
       "      <td>79.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>139441</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10460</td>\n",
       "      <td>19057</td>\n",
       "      <td>1457</td>\n",
       "      <td>1361</td>\n",
       "      <td>1674</td>\n",
       "      <td>11710</td>\n",
       "      <td>6112</td>\n",
       "      <td>1108</td>\n",
       "      <td>5</td>\n",
       "      <td>130.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>164</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>139441</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2413</td>\n",
       "      <td>4373</td>\n",
       "      <td>177</td>\n",
       "      <td>113</td>\n",
       "      <td>154</td>\n",
       "      <td>2812</td>\n",
       "      <td>1503</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>139441</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50128</td>\n",
       "      <td>87991</td>\n",
       "      <td>2211</td>\n",
       "      <td>790</td>\n",
       "      <td>1119</td>\n",
       "      <td>61027</td>\n",
       "      <td>32048</td>\n",
       "      <td>1386</td>\n",
       "      <td>58</td>\n",
       "      <td>1572.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>1777</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>139441</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7244</td>\n",
       "      <td>13594</td>\n",
       "      <td>671</td>\n",
       "      <td>410</td>\n",
       "      <td>580</td>\n",
       "      <td>6228</td>\n",
       "      <td>3200</td>\n",
       "      <td>396</td>\n",
       "      <td>19</td>\n",
       "      <td>325.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>393</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Page total likes  Category  Post Month  Post Weekday  Post Hour  Paid  \\\n",
       "0            139441         2          12             4          3   0.0   \n",
       "1            139441         2          12             3         10   0.0   \n",
       "2            139441         3          12             3          3   0.0   \n",
       "3            139441         2          12             2         10   1.0   \n",
       "4            139441         2          12             2          3   0.0   \n",
       "\n",
       "   Lifetime Post Total Reach  Lifetime Post Total Impressions  \\\n",
       "0                       2752                             5091   \n",
       "1                      10460                            19057   \n",
       "2                       2413                             4373   \n",
       "3                      50128                            87991   \n",
       "4                       7244                            13594   \n",
       "\n",
       "   Lifetime Engaged Users  Lifetime Post Consumers  \\\n",
       "0                     178                      109   \n",
       "1                    1457                     1361   \n",
       "2                     177                      113   \n",
       "3                    2211                      790   \n",
       "4                     671                      410   \n",
       "\n",
       "   Lifetime Post Consumptions  \\\n",
       "0                         159   \n",
       "1                        1674   \n",
       "2                         154   \n",
       "3                        1119   \n",
       "4                         580   \n",
       "\n",
       "   Lifetime Post Impressions by people who have liked your Page  \\\n",
       "0                                               3078              \n",
       "1                                              11710              \n",
       "2                                               2812              \n",
       "3                                              61027              \n",
       "4                                               6228              \n",
       "\n",
       "   Lifetime Post reach by people who like your Page  \\\n",
       "0                                              1640   \n",
       "1                                              6112   \n",
       "2                                              1503   \n",
       "3                                             32048   \n",
       "4                                              3200   \n",
       "\n",
       "   Lifetime People who have liked your Page and engaged with your post  \\\n",
       "0                                                119                     \n",
       "1                                               1108                     \n",
       "2                                                132                     \n",
       "3                                               1386                     \n",
       "4                                                396                     \n",
       "\n",
       "   comment    like  share  Total Interactions  Type_c  \n",
       "0        4    79.0   17.0                 100       0  \n",
       "1        5   130.0   29.0                 164       1  \n",
       "2        0    66.0   14.0                  80       0  \n",
       "3       58  1572.0  147.0                1777       0  \n",
       "4       19   325.0   49.0                 393       0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data splitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### A correlation matrix too assist me into picking the right \"in's\" for my purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_matrix = fb.corr().round(2)\n",
    "f, ax = plt.subplots(figsize=(10,10))\n",
    "#### Χρήση απεικόνισης μορφής heatmap της βιβλιοθήκης seaborn \n",
    "sns.heatmap(data=correlation_matrix, annot=True,cmap='YlGnBu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### My y_vector is column \"like\" My x_vector contains all those column with a correlation percentage >= 0.49."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = fb[['Total Interactions', 'share', 'comment','Lifetime People who have liked your Page and engaged with your post',\n",
    "        'Lifetime Post reach by people who like your Page','Lifetime Engaged Users','Lifetime Post Total Reach']]\n",
    "y= fb['like']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train dimensions: (346, 7)\n",
      "y_train dimensions: (346,)\n",
      "X_test dimensions: (149, 7)\n",
      "y_test dimensions: (149,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_train dimensions:\", X_train.shape)\n",
    "print(\"y_train dimensions:\", y_train.shape)\n",
    "\n",
    "print(\"X_test dimensions:\", X_test.shape)\n",
    "print(\"y_test dimensions:\", y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying Linear Regression algorithm. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "linregressor = LinearRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training my model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linregressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model's score with test data:\n",
      "--------------------------------------\n",
      "Mean Absolute Error=  1.5689766081577591e-12\n",
      "Root Mean Squared Error =  2.915849445974614e-12\n",
      "R2 score =  1.0\n"
     ]
    }
   ],
   "source": [
    "y_pred_test = linregressor.predict(X_test)\n",
    "\n",
    "# mean absolute error\n",
    "mae=metrics.mean_absolute_error(y_test, y_pred_test)\n",
    "# root mean square error\n",
    "rmse = (np.sqrt(mean_squared_error(y_test, y_pred_test)))\n",
    "# r-squared scor\n",
    "r2 = r2_score(y_test, y_pred_test)\n",
    "linregressor.score(X_test, y_test)\n",
    "\n",
    "print(\"Model's score with test data:\")\n",
    "print(\"--------------------------------------\")\n",
    "print('Mean Absolute Error= ', mae)  \n",
    "print('Root Mean Squared Error = ',rmse)\n",
    "print('R2 score = ',linregressor.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting real result - expected result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, y_pred_test)\n",
    "\n",
    "plt.plot([0, 50], [0, 50], '--k')\n",
    "\n",
    "plt.title('**Predicted value**')\n",
    "plt.xlabel('Real value')\n",
    "plt.ylabel('Expected value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Judging by the R2 score I can see that my model is overfitted, so re-adjusting now.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = fb[['Total Interactions', 'share', 'comment','Lifetime People who have liked your Page and engaged with your post',\n",
    "        'Lifetime Post reach by people who like your Page','Lifetime Engaged Users','Lifetime Post Total Reach',\n",
    "       'Lifetime Post Total Impressions','Lifetime Post Consumers']]\n",
    "y= fb['like']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train dimensions: (346, 9)\n",
      "y_train dimensions: (346,)\n",
      "X_test dimensions: (149, 9)\n",
      "y_test dimensions: (149,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_train dimensions:\", X_train.shape)\n",
    "print(\"y_train dimensions:\", y_train.shape)\n",
    "\n",
    "print(\"X_test dimensions:\", X_test.shape)\n",
    "print(\"y_test dimensions:\", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "linregressor = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linregressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model's score with test data:\n",
      "--------------------------------------\n",
      "Mean Absolute Error=  6.986210911112275e-12\n",
      "Root Mean Squared Error =  9.424006632183837e-12\n",
      "R2 score =  1.0\n"
     ]
    }
   ],
   "source": [
    "y_pred_test = linregressor.predict(X_test)\n",
    "\n",
    "# mean absolute error\n",
    "mae=metrics.mean_absolute_error(y_test, y_pred_test)\n",
    "# root mean square error\n",
    "rmse = (np.sqrt(mean_squared_error(y_test, y_pred_test)))\n",
    "# r-squared score\n",
    "r2 = r2_score(y_test, y_pred_test)\n",
    "linregressor.score(X_test, y_test)\n",
    "\n",
    "print(\"Model's score with test data:\")\n",
    "print(\"--------------------------------------\")\n",
    "print('Mean Absolute Error= ', mae)  \n",
    "print('Root Mean Squared Error = ',rmse)\n",
    "print('R2 score = ',linregressor.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Even though in my x_vector were columns with a correlation percentage > 0.34, and afterwards the columns with a percentage of <= 0.49 where removed, the R2 score stayed the same.\n",
    "\n",
    "##### Applying a different algorithm choosing as x_vector the columns with a correlation percentage >=0.49.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying Random Forest Regressor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = fb[['Total Interactions', 'share', 'comment','Lifetime People who have liked your Page and engaged with your post',\n",
    "        'Lifetime Post reach by people who like your Page','Lifetime Engaged Users','Lifetime Post Total Reach']]\n",
    "y= fb['like']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfr = RandomForestRegressor(n_estimators=250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(n_estimators=250)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model's score with test data:\n",
      "--------------------------------------\n",
      "Mean Absolute Error=  13.088375838926176\n",
      "Root Mean Squared Error =  37.31647136112467\n",
      "R2 score =  0.963038014395293\n"
     ]
    }
   ],
   "source": [
    "y_pred = rfr.predict(X_test)\n",
    "\n",
    "# mean absolute error\n",
    "mae=metrics.mean_absolute_error(y_test, y_pred)\n",
    "# root mean square error\n",
    "rmse = (np.sqrt(mean_squared_error(y_test, y_pred)))\n",
    "# r-squared score\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "rfr.score(X_test, y_test)\n",
    "\n",
    "print(\"Model's score with test data:\")\n",
    "print(\"--------------------------------------\")\n",
    "print('Mean Absolute Error= ', mae)  \n",
    "print('Root Mean Squared Error = ',rmse)\n",
    "print('R2 score = ',rfr.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, y_pred)\n",
    "\n",
    "plt.plot([0, 50], [0, 50], '--k')\n",
    "\n",
    "plt.title('Predicted value ')\n",
    "plt.xlabel('Real value')\n",
    "plt.ylabel('Expected value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### With an R2 score of 0.95, thr Random Forest Regressor algorithm, seems to be performing better. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying KNeighbours Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "knnRegr = KNeighborsRegressor(n_neighbors=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(n_neighbors=1)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knnRegr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = knnRegr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model's score with test data:\n",
      "--------------------------------------\n",
      "Mean Absolute Error=  130.18120805369128\n",
      "Root Mean Squared Error =  316.02819303338475\n",
      "R2 score =  0.963038014395293\n"
     ]
    }
   ],
   "source": [
    "# mean absolute error\n",
    "mae=metrics.mean_absolute_error(y_test, y_pred)\n",
    "# root mean square error\n",
    "rmse = (np.sqrt(mean_squared_error(y_test, y_pred)))\n",
    "# r-squared score \n",
    "r2 = r2_score(y_test, y_pred)\n",
    "rfr.score(X_test, y_test)\n",
    "\n",
    "print(\"Model's score with test data:\")\n",
    "print(\"--------------------------------------\")\n",
    "print('Mean Absolute Error= ', mae)  \n",
    "print('Root Mean Squared Error = ',rmse)\n",
    "print('R2 score = ',rfr.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The R2 Score of the last method is the same as this one, with changes in the Mean Absolute Error and the Root Mean Squared Error."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best algorithm for this dataset is the Random Forest Regressor algorithm."
   ]
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}