{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "05e4b4c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ef0e3a1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\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>Results</th>\n",
       "      <th>Reach</th>\n",
       "      <th>Impressions</th>\n",
       "      <th>Video_plays</th>\n",
       "      <th>Link_clicks</th>\n",
       "      <th>Engagement</th>\n",
       "      <th>Amount_Spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1534</td>\n",
       "      <td>1534</td>\n",
       "      <td>1535</td>\n",
       "      <td>1448</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8593</td>\n",
       "      <td>8593</td>\n",
       "      <td>10599</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>140</td>\n",
       "      <td>571</td>\n",
       "      <td>572</td>\n",
       "      <td>457</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13</td>\n",
       "      <td>1396</td>\n",
       "      <td>1479</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>47</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37616</td>\n",
       "      <td>5092</td>\n",
       "      <td>40135</td>\n",
       "      <td>39568</td>\n",
       "      <td>0</td>\n",
       "      <td>35711</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Results  Reach  Impressions  Video_plays  Link_clicks  Engagement  \\\n",
       "0     1534   1534         1535         1448            0          62   \n",
       "1     8593   8593        10599            0            2           2   \n",
       "2      140    571          572          457            0         140   \n",
       "3       13   1396         1479            0           13          47   \n",
       "4    37616   5092        40135        39568            0       35711   \n",
       "\n",
       "   Amount_Spent  \n",
       "0             2  \n",
       "1            20  \n",
       "2             5  \n",
       "3            23  \n",
       "4           133  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'Results': [1534,8593,140,13,37616,1060,694,64,17744],\n",
    "        'Reach': [1534,8593,571,1396,5092,6933,2008,2825,6154],\n",
    "        'Impressions': [1535,10599,572,1479,40135,11468,2435,5087,21332],\n",
    "        'Video_plays': [1448,0,457,0,39568,0,1225,0,20905],\n",
    "       'Link_clicks': [0,2,0,13,0,100,1,49,0],\n",
    "       'Engagement': [62,2,140,47,35711,1060,694,145,15604],\n",
    "       'Amount_Spent': [2,20,5,23,133,89,37,85,76]}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b7bba8a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.multioutput import MultiOutputRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "764ebe28",
   "metadata": {},
   "outputs": [],
   "source": [
    "# X contains the features (Amount spent)\n",
    "X = df['Amount_Spent']\n",
    "\n",
    "# y contains the target variable (Results, Reach, Impressions, Video Plays, Link clicks, and Post engagement)\n",
    "y = df[['Results', 'Reach', 'Impressions', 'Video_plays', 'Link_clicks', 'Engagement']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fa4f048c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiOutputRegressor(estimator=LinearRegression())"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Create a Linear Regression model and wrap it in MultiOutputRegressor\n",
    "model = MultiOutputRegressor(LinearRegression())\n",
    "\n",
    "# Train the model with the training data\n",
    "model.fit(X_train.values.reshape(-1, 1), y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "53fda5c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict the columns using the test data\n",
    "y_pred = model.predict(X_test.values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2fcd8845",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 100374744.3348547\n",
      "R-squared: -3679.080219755561\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model's performance using metrics like Mean Squared Error (MSE) and R-squared (R2)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(\"Mean Squared Error:\", mse)\n",
    "print(\"R-squared:\", r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7c2584f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted values: [ 726.14777024 1945.43688854 2292.37143629 1016.27642398    8.87683812\n",
      "  240.56563231]\n"
     ]
    }
   ],
   "source": [
    "# Predict the columns for a new value of 'Amount spent'\n",
    "X_new = [[20]]\n",
    "predicted_values = model.predict([[20]])\n",
    "print(\"Predicted values:\", predicted_values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "545f8034",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>Results</th>\n",
       "      <th>Reach</th>\n",
       "      <th>Impressions</th>\n",
       "      <th>Video_plays</th>\n",
       "      <th>Link_clicks</th>\n",
       "      <th>Engagement</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>726.14777</td>\n",
       "      <td>1945.436889</td>\n",
       "      <td>2292.371436</td>\n",
       "      <td>1016.276424</td>\n",
       "      <td>8.876838</td>\n",
       "      <td>240.565632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Results        Reach  Impressions  Video_plays  Link_clicks  Engagement\n",
       "0  726.14777  1945.436889  2292.371436  1016.276424     8.876838  240.565632"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the predicted values into a new DataFrame\n",
    "predicted_df = pd.DataFrame(data=predicted_values, columns=y.columns)\n",
    "predicted_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf9c18ec",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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