{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7ad7e9f2",
   "metadata": {},
   "source": [
    "### <font color='purple'> Business Case: Pharmaceutical Advertising Optimization\n",
    "\n",
    "**<font color='black'> Challenge:** Nova, A pharmaceutical company, lacks bsuiness insghts on how their advertising spending influences the sales, from their data. Nova needs to allocate its budget wisely to maximize sales but lacks insight into the most effective strategy.\n",
    "\n",
    "<font color='black'>**Objectives:**\n",
    "- **Data Analysis:** Analyze historical advertising costs and sales data.\n",
    "- **Correlation Modeling:** Establish the relationship between advertising spend and sales.\n",
    "- **Budget Optimization:** Develop a model to allocate the advertising budget for optimal sales outcomes.\n",
    "\n",
    "<font color='black'>**Strategy:**\n",
    "- **Data Collection:** Gather historical advertising and sales data.\n",
    "- **Analysis:** Identify patterns and correlations in the data.\n",
    "- **Modeling:** Create a predictive model to determine the best budget allocation.\n",
    "- **Optimization:** Use the model to optimize advertising spending for maximum sales impact.\n",
    "\n",
    "<font color='black'>**Expected Outcomes:**\n",
    "- **Informed Decisions:** Gain insights into the advertising-sales relationship.\n",
    "- **Efficient Budgeting:** Optimize budget allocation for maximum sales.\n",
    "- **Risk Reduction:** Mitigate uncertainties associated with budget decisions.\n",
    "\n",
    "**Benefits:**\n",
    "- **Improved ROI:** Maximize returns on advertising investments.\n",
    "- **Strategic Planning:** Align budget with long-term sales goals.\n",
    "- **Competitive Advantage:** Gain an edge through data-driven advertising strategies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cba6ee3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "21f4e4ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\Teni\\Desktop\\Advertising.csv\")\n",
    "\n",
    "# data for year 2022 and 2023"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cbd615b0",
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Instagram</th>\n",
       "      <th>Youtube</th>\n",
       "      <th>Podcasts</th>\n",
       "      <th>generated_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.7</td>\n",
       "      <td>48.9</td>\n",
       "      <td>75.0</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>57.5</td>\n",
       "      <td>32.8</td>\n",
       "      <td>23.5</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>120.2</td>\n",
       "      <td>19.6</td>\n",
       "      <td>11.6</td>\n",
       "      <td>13.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>199.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>21.2</td>\n",
       "      <td>10.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Instagram  Youtube  Podcasts  generated_sales\n",
       "0      230.1     37.8      69.2             22.1\n",
       "1       44.5     39.3      45.1             10.4\n",
       "2       17.2     45.9      69.3              9.3\n",
       "3      151.5     41.3      58.5             18.5\n",
       "4      180.8     10.8      58.4             12.9\n",
       "5        8.7     48.9      75.0              7.2\n",
       "6       57.5     32.8      23.5             11.8\n",
       "7      120.2     19.6      11.6             13.2\n",
       "8        8.6      2.1       1.0              4.8\n",
       "9      199.8      2.6      21.2             10.6"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)\n",
    "\n",
    "# This record shows us how much Nova has spent on TV, radio and newspaper in the year 2023.\n",
    "# As well as the total_sales made at each Ad attempt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "59eff6aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To help us determine how much has been spent on Ad in general and the resultant sales, an additional column-summing up total costs- with will be created\n",
    "\n",
    "df['total_AdSpend'] = df['Instagram'] + df['Youtube'] + df['Podcasts']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9716b0a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Instagram</th>\n",
       "      <th>Youtube</th>\n",
       "      <th>Podcasts</th>\n",
       "      <th>generated_sales</th>\n",
       "      <th>total_AdSpend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "      <td>337.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "      <td>128.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "      <td>132.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "      <td>251.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>38.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>13.8</td>\n",
       "      <td>7.6</td>\n",
       "      <td>55.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>94.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9.7</td>\n",
       "      <td>107.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>177.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>12.8</td>\n",
       "      <td>192.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>283.6</td>\n",
       "      <td>42.0</td>\n",
       "      <td>66.2</td>\n",
       "      <td>25.5</td>\n",
       "      <td>391.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>232.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>13.4</td>\n",
       "      <td>249.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Instagram  Youtube  Podcasts  generated_sales  total_AdSpend\n",
       "0        230.1     37.8      69.2             22.1          337.1\n",
       "1         44.5     39.3      45.1             10.4          128.9\n",
       "2         17.2     45.9      69.3              9.3          132.4\n",
       "3        151.5     41.3      58.5             18.5          251.3\n",
       "4        180.8     10.8      58.4             12.9          250.0\n",
       "..         ...      ...       ...              ...            ...\n",
       "195       38.2      3.7      13.8              7.6           55.7\n",
       "196       94.2      4.9       8.1              9.7          107.2\n",
       "197      177.0      9.3       6.4             12.8          192.7\n",
       "198      283.6     42.0      66.2             25.5          391.8\n",
       "199      232.1      8.6       8.7             13.4          249.4\n",
       "\n",
       "[200 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d98fbefb",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 2000x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10, 6], dpi= 200)\n",
    "\n",
    "sns.scatterplot(data=df, x = 'total_AdSpend', y = 'generated_sales');\n",
    "\n",
    "\n",
    "# This shows there's an existing positive Linear relationship between the total cost spent on Ads and the sales generated by Nova"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02ad021d",
   "metadata": {},
   "source": [
    "<font color='purple'> **Defining the Coefficient Variable**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "07175fce",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df['total_AdSpend']\n",
    "y = df['generated_sales']\n",
    "\n",
    "# X is the Feature (Independent Variable)\n",
    "# y is the Label (Dependent Variable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ac4d8ab2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.04868788, 4.24302822])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# From the Linear Regression equationy = mx + b \n",
    "# We using the deg 1 because the relationshop is linear\n",
    "\n",
    "np.polyfit(X, y, deg=1)\n",
    "# This form the coefficients for the m and b "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49544374",
   "metadata": {},
   "source": [
    "<font color='purple'> **Hypothetical Determinant**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d17948a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "projected_cost = np.linspace(2, 340, 300)\n",
    "\n",
    "# Here we have an array of 300 numbers spaced from 2 to 340."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ec81b7e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using the y = mx + b\n",
    "\n",
    "projected_sales = 0.04868788*projected_cost + 4.24302822"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fc1fd791",
   "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>Ad_costs</th>\n",
       "      <th>Projected_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.340404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.130435</td>\n",
       "      <td>4.395442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.260870</td>\n",
       "      <td>4.450481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.391304</td>\n",
       "      <td>4.505519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.521739</td>\n",
       "      <td>4.560558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>335.478261</td>\n",
       "      <td>20.576754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>336.608696</td>\n",
       "      <td>20.631792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>337.739130</td>\n",
       "      <td>20.686830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>338.869565</td>\n",
       "      <td>20.741869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>340.000000</td>\n",
       "      <td>20.796907</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Ad_costs  Projected_sales\n",
       "0      2.000000         4.340404\n",
       "1      3.130435         4.395442\n",
       "2      4.260870         4.450481\n",
       "3      5.391304         4.505519\n",
       "4      6.521739         4.560558\n",
       "..          ...              ...\n",
       "295  335.478261        20.576754\n",
       "296  336.608696        20.631792\n",
       "297  337.739130        20.686830\n",
       "298  338.869565        20.741869\n",
       "299  340.000000        20.796907\n",
       "\n",
       "[300 rows x 2 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q3_AdDeterminant = pd.DataFrame({'Ad_costs': projected_cost, 'Projected_sales': projected_sales})\n",
    "Q3_AdDeterminant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4e0884b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df, x = 'total_AdSpend', y = 'generated_sales');\n",
    "plt.plot(projected_cost, projected_sales, color ='purple');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9cb3691",
   "metadata": {},
   "source": [
    "**If we had an Ad budget of 10k, what'd our projected sales value be?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "19c6d657",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.729907020000001"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spend = 10\n",
    "\n",
    "sales = 0.04868788*spend + 4.24302822\n",
    "\n",
    "sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f28f322",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2c75934",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}