First_Touch_Attribution_Model.ipynb

{
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   "execution_count": 1,
   "id": "f24001f1-504c-417f-bb52-1acc2f435a79",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cf038059-b504-4b52-9179-1f7203dc5494",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "3520422"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('attribution_data.csv')\n",
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5c75a8a7-87f9-4f4c-a75b-cdf66b23e125",
   "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>cookie</th>\n",
       "      <th>time</th>\n",
       "      <th>interaction</th>\n",
       "      <th>conversion</th>\n",
       "      <th>conversion_value</th>\n",
       "      <th>channel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00000FkCnDfDDf0iC97iC703B</td>\n",
       "      <td>2018-07-03T13:02:11Z</td>\n",
       "      <td>impression</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Instagram</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00000FkCnDfDDf0iC97iC703B</td>\n",
       "      <td>2018-07-17T19:15:07Z</td>\n",
       "      <td>impression</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Online Display</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00000FkCnDfDDf0iC97iC703B</td>\n",
       "      <td>2018-07-24T15:51:46Z</td>\n",
       "      <td>impression</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Online Display</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00000FkCnDfDDf0iC97iC703B</td>\n",
       "      <td>2018-07-29T07:44:51Z</td>\n",
       "      <td>impression</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Online Display</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0000nACkD9nFkBBDECD3ki00E</td>\n",
       "      <td>2018-07-03T09:44:57Z</td>\n",
       "      <td>impression</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Paid Search</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      cookie                  time interaction  conversion  \\\n",
       "0  00000FkCnDfDDf0iC97iC703B  2018-07-03T13:02:11Z  impression           0   \n",
       "1  00000FkCnDfDDf0iC97iC703B  2018-07-17T19:15:07Z  impression           0   \n",
       "2  00000FkCnDfDDf0iC97iC703B  2018-07-24T15:51:46Z  impression           0   \n",
       "3  00000FkCnDfDDf0iC97iC703B  2018-07-29T07:44:51Z  impression           0   \n",
       "4  0000nACkD9nFkBBDECD3ki00E  2018-07-03T09:44:57Z  impression           0   \n",
       "\n",
       "   conversion_value         channel  \n",
       "0               0.0       Instagram  \n",
       "1               0.0  Online Display  \n",
       "2               0.0  Online Display  \n",
       "3               0.0  Online Display  \n",
       "4               0.0     Paid Search  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cccb476e-dae5-43f1-9080-3195e5a3d437",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cookie               object\n",
       "time                 object\n",
       "interaction          object\n",
       "conversion            int64\n",
       "conversion_value    float64\n",
       "channel              object\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "13496fe8-e03c-4c23-9d23-2f58f5254583",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cookie</th>\n",
       "      <th>time</th>\n",
       "      <th>interaction</th>\n",
       "      <th>conversion</th>\n",
       "      <th>conversion_value</th>\n",
       "      <th>channel</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0007oEBhnoF97AoEE3BCkFnhB</td>\n",
       "      <td>2018-07-06T13:45:29Z</td>\n",
       "      <td>conversion</td>\n",
       "      <td>1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>Paid Search</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>00090n9EBBEkA000C7Cik999D</td>\n",
       "      <td>2018-07-05T06:53:53Z</td>\n",
       "      <td>conversion</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Facebook</td>\n",
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       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>000h3n9nC0hFhE3CCnkkAof7n</td>\n",
       "      <td>2018-07-19T14:31:57Z</td>\n",
       "      <td>conversion</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Facebook</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>000hCBnCB7oi7ADAEnEBCnBEE</td>\n",
       "      <td>2018-07-25T11:15:16Z</td>\n",
       "      <td>conversion</td>\n",
       "      <td>1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>Online Video</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>000kiDB3D0fCfDAohCDB3ohko</td>\n",
       "      <td>2018-07-26T16:16:21Z</td>\n",
       "      <td>conversion</td>\n",
       "      <td>1</td>\n",
       "      <td>7.5</td>\n",
       "      <td>Facebook</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        cookie                  time interaction  conversion  \\\n",
       "22   0007oEBhnoF97AoEE3BCkFnhB  2018-07-06T13:45:29Z  conversion           1   \n",
       "23   00090n9EBBEkA000C7Cik999D  2018-07-05T06:53:53Z  conversion           1   \n",
       "83   000h3n9nC0hFhE3CCnkkAof7n  2018-07-19T14:31:57Z  conversion           1   \n",
       "109  000hCBnCB7oi7ADAEnEBCnBEE  2018-07-25T11:15:16Z  conversion           1   \n",
       "124  000kiDB3D0fCfDAohCDB3ohko  2018-07-26T16:16:21Z  conversion           1   \n",
       "\n",
       "     conversion_value       channel  \n",
       "22                6.5   Paid Search  \n",
       "23                8.0      Facebook  \n",
       "83                6.0      Facebook  \n",
       "109               6.5  Online Video  \n",
       "124               7.5      Facebook  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filterd_convert = df[df['conversion'] == 1]\n",
    "df_filterd_convert.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "57d91a00-7778-450e-9d7f-e7d25d72cb5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "cookie_index = list(df_filterd_convert[\"cookie\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8bb6bdf7-2ee2-441b-963e-b8c8f1f844ef",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>interaction</th>\n",
       "      <th>conversion</th>\n",
       "      <th>conversion_value</th>\n",
       "      <th>channel</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cookie</th>\n",
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       "      <td>2018-07-06T13:45:29Z</td>\n",
       "      <td>conversion</td>\n",
       "      <td>1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>Paid Search</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00090n9EBBEkA000C7Cik999D</th>\n",
       "      <td>2018-07-05T06:53:53Z</td>\n",
       "      <td>conversion</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Facebook</td>\n",
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       "      <th>000h3n9nC0hFhE3CCnkkAof7n</th>\n",
       "      <td>2018-07-19T14:31:57Z</td>\n",
       "      <td>conversion</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Facebook</td>\n",
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       "      <th>000hCBnCB7oi7ADAEnEBCnBEE</th>\n",
       "      <td>2018-07-07T14:34:54Z</td>\n",
       "      <td>impression</td>\n",
       "      <td>0</td>\n",
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       "      <th>000hCBnCB7oi7ADAEnEBCnBEE</th>\n",
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       "      <td>impression</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Online Video</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                                           time interaction  conversion  \\\n",
       "cookie                                                                    \n",
       "0007oEBhnoF97AoEE3BCkFnhB  2018-07-06T13:45:29Z  conversion           1   \n",
       "00090n9EBBEkA000C7Cik999D  2018-07-05T06:53:53Z  conversion           1   \n",
       "000h3n9nC0hFhE3CCnkkAof7n  2018-07-19T14:31:57Z  conversion           1   \n",
       "000hCBnCB7oi7ADAEnEBCnBEE  2018-07-07T14:34:54Z  impression           0   \n",
       "000hCBnCB7oi7ADAEnEBCnBEE  2018-07-07T14:34:54Z  impression           0   \n",
       "\n",
       "                           conversion_value       channel  \n",
       "cookie                                                     \n",
       "0007oEBhnoF97AoEE3BCkFnhB               6.5   Paid Search  \n",
       "00090n9EBBEkA000C7Cik999D               8.0      Facebook  \n",
       "000h3n9nC0hFhE3CCnkkAof7n               6.0      Facebook  \n",
       "000hCBnCB7oi7ADAEnEBCnBEE               0.0  Online Video  \n",
       "000hCBnCB7oi7ADAEnEBCnBEE               0.0  Online Video  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_df = pd.DataFrame(df.groupby(\"cookie\").first())\n",
    "filter_df = grouped_df.loc[cookie_index]\n",
    "filter_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "34424295-9a5b-44cc-8111-a4c9473dc586",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Weight (%)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>channel</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Facebook</th>\n",
       "      <td>32.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Online Video</th>\n",
       "      <td>25.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paid Search</th>\n",
       "      <td>18.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Instagram</th>\n",
       "      <td>14.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Online Display</th>\n",
       "      <td>8.95</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                Weight (%)\n",
       "channel                   \n",
       "Facebook             32.59\n",
       "Online Video         25.20\n",
       "Paid Search          18.47\n",
       "Instagram            14.78\n",
       "Online Display        8.95"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_touch = pd.DataFrame(round(filter_df[\"channel\"].value_counts(normalize=True) * 100, 2))\n",
    "first_touch.columns = [\"Weight (%)\"]\n",
    "first_touch"
   ]
  },
  {
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   "execution_count": 18,
   "id": "4d6fd01d-96f4-4a7a-8405-31d8369aaa2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(10, 6))\n",
    "sns.barplot(data=first_touch, x=first_touch.index, y=\"Weight (%)\")\n",
    "plt.xlabel(\"Channel\")\n",
    "plt.title(\"First Touch Attribution Model\", fontsize=20)\n",
    "plt.savefig('plot/first_touch.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df0b39c5-416b-4877-86d1-4970788db0ba",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}