Calculation-of-Lead-Generation-with-Rule-Based-Classification.ipynb

{"cells":[{"cell_type":"markdown","source":["# Rule Based Classification\n","📌 **Rule-based classifiers** are just another type of classifier which makes the class decision depending by using various “if..else” rules. These rules are easily interpretable and thus these classifiers are generally used to generate descriptive models. The condition used with “if” is called the antecedent and the predicted class of each rule is called the consequent. If you want to read more about this topic, you can visit this <a href=\"https://www.geeksforgeeks.org/rule-based-classifier-machine-learning/\">site</a>.\n","\n","# Problem Definition\n","📌 A game company wants to create level-based new customer definitions (personas) using some features of its customers, and to create segments according to these new customer definitions and to estimate how much the new customers can earn on average according to these segments.\n","\n","# Dataset\n","📌 The Persona.csv dataset contains the prices of the products sold by an international game company and some demographic information of the users who buy these products. The data set consists of records created in each sales transaction. This means that the table is not deduplicated. In other words, a user with certain demographic characteristics may have made more than one purchase.\n","# Variables\n","* *PRICE*: Customer spend amount,\n","* *SOURCE*: The type of device the customer is connecting to (IOS/Android),\n","* *SEX*: Gender of the customer,\n","* *COUNTRY*: Country of the customer,\n","* *AGE*: Age of the customer"],"metadata":{"id":"2703Njmy4Gwf"}},{"cell_type":"markdown","source":["### Let's add the necessary libraries and run our work through them.\n"],"metadata":{"id":"G3buy2Dr8dfj"}},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","pd.set_option(\"display.max_columns\",None)\n","pd.set_option(\"display.width\",500)\n","persona = pd.read_csv(\"/content/persona.csv\")\n","df = persona.copy()\n","df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"AGSCcrUO8KwY","executionInfo":{"status":"ok","timestamp":1667228438832,"user_tz":-180,"elapsed":437,"user":{"displayName":"tohid yousefi","userId":"09164737776488206070"}},"outputId":"7d38861a-dbaa-48d9-ac85-7adc4205ef77"},"execution_count":23,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   PRICE   SOURCE   SEX COUNTRY  AGE\n","0     39  android  male     bra   17\n","1     39  android  male     bra   17\n","2     49  android  male     bra   17\n","3     29  android  male     tur   17\n","4     49  android  male     tur   17"],"text/html":["\n","  <div id=\"df-782cf82d-78d2-443b-aa4e-f3d59f3c819e\">\n","    <div class=\"colab-df-container\">\n","      <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","    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print(\"################################ Tail ################################\\n\")\n","  print(dataframe.tail(head))\n","  print(\"################################ Shape ################################\\n\")\n","  print(dataframe.shape)\n","  print(\"################################ Types ################################\\n\")\n","  print(dataframe.dtypes)\n","  print(\"################################ NA ################################\\n\")\n","  print(dataframe.isnull().sum())\n","  print(\"################################ Qurtiles ################################\\n\")\n","  print(dataframe.describe([0, 0.05, 0.50, 0.95, 0.99, 1]).T)"],"metadata":{"id":"tvTrsEMd-nUI","executionInfo":{"status":"ok","timestamp":1667228440267,"user_tz":-180,"elapsed":149,"user":{"displayName":"tohid yousefi","userId":"09164737776488206070"}}},"execution_count":24,"outputs":[]},{"cell_type":"code","source":["check_df(df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qjqdacIA_-IE","executionInfo":{"status":"ok","timestamp":1667228440270,"user_tz":-180,"elapsed":144,"user":{"displayName":"tohid yousefi","userId":"09164737776488206070"}},"outputId":"d9deb616-e8e8-4efb-c515-e543b4906256"},"execution_count":25,"outputs":[{"output_type":"stream","name":"stdout","text":["################################ Head ################################\n","\n","   PRICE   SOURCE   SEX COUNTRY  AGE\n","0     39  android  male     bra   17\n","1     39  android  male     bra   17\n","2     49  android  male     bra   17\n","3     29  android  male     tur   17\n","4     49  android  male     tur   17\n","################################ Tail ################################\n","\n","      PRICE   SOURCE     SEX COUNTRY  AGE\n","4995     29  android  female     bra   31\n","4996     29  android  female     bra   31\n","4997     29  android  female     bra   31\n","4998     39  android  female     bra   31\n","4999     29  android  female     bra   31\n","################################ Shape ################################\n","\n","(5000, 5)\n","################################ Types ################################\n","\n","PRICE       int64\n","SOURCE     object\n","SEX        object\n","COUNTRY    object\n","AGE         int64\n","dtype: object\n","################################ NA ################################\n","\n","PRICE      0\n","SOURCE     0\n","SEX        0\n","COUNTRY    0\n","AGE        0\n","dtype: int64\n","################################ Qurtiles ################################\n","\n","        count     mean        std   min    0%    5%   50%   95%   99%  100%   max\n","PRICE  5000.0  34.1320  12.464897   9.0   9.0  19.0  39.0  49.0  59.0  59.0  59.0\n","AGE    5000.0  23.5814   8.995908  15.0  15.0  15.0  21.0  43.0  53.0  66.0  66.0\n"]}]},{"cell_type":"markdown","source":["### Capturing categorical and numerical variables"],"metadata":{"id":"lliLEuOPGNUD"}},{"cell_type":"code","source":["def grab_col_names(dataframe,cat_th=10,car_th=20):\n","  #Catgeorical Variable Selection\n","  cat_cols = [col for col in dataframe.columns if str(dataframe[col].dtypes) in [\"category\",\"object\",\"bool\"]]\n","  num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and dataframe[col].dtypes in [\"int\",\"float\"]]\n","  cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and dataframe[col].dtypes in [\"category\",\"object\"]]\n","  all_cat_cols = cat_cols + num_but_cat\n","  all_cat_cols = [col for col in all_cat_cols if col not in cat_but_car]\n","\n","  #Numerical Variable Selection\n","  num_cols = [col for col in dataframe.columns if dataframe[col].dtypes in [\"int\",\"float\"]]\n","  num_cols = [col for col in num_cols if col not in all_cat_cols]\n","\n","  #Print Categorical and Numerical Variables\n","  print(f\"Observations: {dataframe.shape[0]}\")\n","  print(f\"Variables: {dataframe.shape[1]}\")\n","  print(f\"Cat_cols: {len(all_cat_cols)}\")\n","  print(f\"Num_but_cat: {len(num_but_cat)}\")\n","  print(f\"Num_cols: {len(num_cols)}\")\n","  print(f\"Cat_but_car: {len(cat_but_car)}\")\n","\n","  return all_cat_cols, num_cols, cat_but_car"],"metadata":{"id":"fkheI6Iw__68","executionInfo":{"status":"ok","timestamp":1667228440273,"user_tz":-180,"elapsed":131,"user":{"displayName":"tohid yousefi","userId":"09164737776488206070"}}},"execution_count":26,"outputs":[]},{"cell_type":"code","source":["all_cat_cols,num_cols,cat_but_car = grab_col_names(df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xjHkeKaTI1Rt","executionInfo":{"status":"ok","timestamp":1667228440276,"user_tz":-180,"elapsed":129,"user":{"displayName":"tohid yousefi","userId":"09164737776488206070"}},"outputId":"1fb35c59-9e3e-47bf-b91a-cb4f27068e0a"},"execution_count":27,"outputs":[{"output_type":"stream","name":"stdout","text":["Observations: 5000\n","Variables: 5\n","Cat_cols: 4\n","Num_but_cat: 1\n","Num_cols: 1\n","Cat_but_car: 0\n"]}]},{"cell_type":"markdown","source":["### General Exploration for Categorical Data"],"metadata":{"id":"3xzojAkYKOPy"}},{"cell_type":"code","source":["def cat_summary(dataframe,plot=False):\n","  for col_name in all_cat_cols:\n","    print(\"################################ Unique Observations of Categorical Data ################################\")\n","    print(\"The unique number of \" + col_name + \" : \" + str(dataframe[col_name].nunique()))\n","\n","    print(\"################################ Frequency of Categorical Data ################################\")\n","    print(pd.DataFrame({col_name: dataframe[col_name].value_counts(),\n","                        'Ratio': 100 * dataframe[col_name].value_counts() / len(dataframe)}))\n","    \n","    if plot:\n","      sns.countplot(x=dataframe[col_name], data=dataframe)\n","      plt.show(block=True)"],"metadata":{"id":"0cSXyBDrI4ih","executionInfo":{"status":"ok","timestamp":1667228440279,"user_tz":-180,"elapsed":110,"user":{"displayName":"tohid yousefi","userId":"09164737776488206070"}}},"execution_count":28,"outputs":[]},{"cell_type":"code","source":["cat_summary(df,plot=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"C55n9m8rOh3o","executionInfo":{"status":"ok","timestamp":1667228440280,"user_tz":-180,"elapsed":107,"user":{"displayName":"tohid yousefi","userId":"09164737776488206070"}},"outputId":"548c9f4f-b1cc-48a4-b297-5ada04017779"},"execution_count":29,"outputs":[{"output_type":"stream","name":"stdout","text":["################################ Unique Observations of Categorical Data ################################\n","The unique number of SOURCE : 2\n","################################ Frequency of Categorical Data ################################\n","         SOURCE  Ratio\n","android    2974  59.48\n","ios        2026  40.52\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["################################ Unique Observations of Categorical Data ################################\n","The unique number of SEX : 2\n","################################ Frequency of Categorical Data ################################\n","         SEX  Ratio\n","female  2621  52.42\n","male    2379  47.58\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["################################ Unique Observations of Categorical Data ################################\n","The unique number of COUNTRY : 6\n","################################ Frequency of Categorical Data ################################\n","     COUNTRY  Ratio\n","usa     2065  41.30\n","bra     1496  29.92\n","deu      455   9.10\n","tur      451   9.02\n","fra      303   6.06\n","can      230   4.60\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["################################ Unique Observations of Categorical Data ################################\n","The unique number of PRICE : 6\n","################################ Frequency of Categorical Data ################################\n","    PRICE  Ratio\n","29   1305  26.10\n","39   1260  25.20\n","49   1031  20.62\n","19    992  19.84\n","59    212   4.24\n","9     200   4.00\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["### General Exploration for Numerical Data"],"metadata":{"id":"Vp8k2ti6Qp_O"}},{"cell_type":"code","source":["def num_summary(dataframe,plot=False):\n","  quantiles = [0.05, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.95, 0.99]\n","  for col_name in num_cols:\n","    print(\"################################ Summary Statistics of \" + col_name + \" ################################\")\n","    print(dataframe[col_name].describe(quantiles).T)\n","\n","    if plot:\n","      dataframe[col_name].hist()\n","      plt.xlabel(col_name)\n","      plt.title(col_name)\n","      plt.show(block=True)"],"metadata":{"id":"Po988dp1Ojcg","executionInfo":{"status":"ok","timestamp":1667228440283,"user_tz":-180,"elapsed":86,"user":{"displayName":"tohid yousefi","userId":"09164737776488206070"}}},"execution_count":30,"outputs":[]},{"cell_type":"code","source":["num_summary(df,plot=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":625},"id":"5t3mN98cSldP","executionInfo":{"status":"ok","timestamp":1667228440284,"user_tz":-180,"elapsed":83,"user":{"displayName":"tohid yousefi","userId":"09164737776488206070"}},"outputId":"5734b149-7361-43b4-c5e1-4d3c3375a58b"},"execution_count":31,"outputs":[{"output_type":"stream","name":"stdout","text":["################################ Summary Statistics of AGE ################################\n","count    5000.000000\n","mean       23.581400\n","std         8.995908\n","min        15.000000\n","5%         15.000000\n","10%        15.000000\n","20%        16.000000\n","30%        17.000000\n","40%        19.000000\n","50%        21.000000\n","60%        23.000000\n","70%        26.000000\n","80%        30.000000\n","90%        36.000000\n","95%        43.000000\n","99%        53.000000\n","max        66.000000\n","Name: AGE, dtype: float64\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["### Data Analysis"],"metadata":{"id":"_KGcR_-ne_Lt"}},{"cell_type":"code","source":["def data_analysis(dataframe):\n","  print(\"Unique Values of Source: \" , dataframe[\"SOURCE\"].nunique())\n","  print(\"##############################################\")\n","  print(pd.DataFrame({'Frequency of Source': dataframe[\"SOURCE\"].value_counts()}))\n","  print(\"##############################################\")\n","  print(\"Unique of Price: \", dataframe[\"PRICE\"].nunique())\n","  print(\"##############################################\")\n","  print(pd.DataFrame({'Number of product sales by price': dataframe[\"PRICE\"].value_counts()}))\n","  print(\"##############################################\")\n","  print(pd.DataFrame({'Number of product sales by country': dataframe[\"COUNTRY\"].value_counts()}))\n","  print(\"##############################################\")\n","  print('Total & average amount of sales by country', dataframe.groupby([\"COUNTRY\"]).agg({'PRICE':[\"sum\",\"mean\"]}))\n","  print(\"##############################################\")\n","  print(\"Count of sales by source\", dataframe.groupby([\"SOURCE\"]).agg({'PRICE': 'count'}))\n","  print(\"##############################################\")\n","  print(\"Average amount of sales by country\", dataframe.groupby([\"COUNTRY\"]).agg({'PRICE': 'mean'}))\n","  print(\"##############################################\")\n","  print(\"Average amount of sales by source\", dataframe.groupby([\"SOURCE\"]).agg({'PRICE': 'mean'}))\n","  print(\"##############################################\")\n","  print(\"Average amount of sales by source and country\",dataframe.groupby([\"SOURCE\",\"COUNTRY\"]).agg({'PRICE':'mean'}))"],"metadata":{"id":"WD8BXJNOfa4W","executionInfo":{"status":"ok","timestamp":1667228440785,"user_tz":-180,"elapsed":142,"user":{"displayName":"tohid yousefi","userId":"09164737776488206070"}}},"execution_count":32,"outputs":[]},{"cell_type":"code","source":["data_analysis(df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2loFaWwgfwC_","executionInfo":{"status":"ok","timestamp":1667228440787,"user_tz":-180,"elapsed":141,"user":{"displayName":"tohid yousefi","userId":"09164737776488206070"}},"outputId":"bd3d9a8e-1541-416e-8c64-69b736c7d71a"},"execution_count":33,"outputs":[{"output_type":"stream","name":"stdout","text":["Unique Values of Source:  2\n","##############################################\n","         Frequency of Source\n","android                 2974\n","ios                     2026\n","##############################################\n","Unique of Price:  6\n","##############################################\n","    Number of product sales by price\n","29                              1305\n","39                              1260\n","49                              1031\n","19                               992\n","59                               212\n","9                                200\n","##############################################\n","     Number of product sales by country\n","usa                                2065\n","bra                                1496\n","deu                                 455\n","tur                                 451\n","fra                                 303\n","can                                 230\n","##############################################\n","Total & average amount of sales by country          PRICE           \n","           sum       mean\n","COUNTRY                  \n","bra      51354  34.327540\n","can       7730  33.608696\n","deu      15485  34.032967\n","fra      10177  33.587459\n","tur      15689  34.787140\n","usa      70225  34.007264\n","##############################################\n","Count of sales by source          PRICE\n","SOURCE        \n","android   2974\n","ios       2026\n","##############################################\n","Average amount of sales by country              PRICE\n","COUNTRY           \n","bra      34.327540\n","can      33.608696\n","deu      34.032967\n","fra      33.587459\n","tur      34.787140\n","usa      34.007264\n","##############################################\n","Average amount of sales by source              PRICE\n","SOURCE            \n","android  34.174849\n","ios      34.069102\n","##############################################\n","Average amount of sales by source and country                      PRICE\n","SOURCE  COUNTRY           \n","android bra      34.387029\n","        can      33.330709\n","        deu      33.869888\n","        fra      34.312500\n","        tur      36.229437\n","        usa      33.760357\n","ios     bra      34.222222\n","        can      33.951456\n","        deu      34.268817\n","        fra      32.776224\n","        tur      33.272727\n","        usa      34.371703\n"]}]},{"cell_type":"markdown","source":["### Average PRICE in COUNTRY, SOURCE, SEX, AGE groupby"],"metadata":{"id":"M7RXeKVqpVU7"}},{"cell_type":"code","source":["df.groupby([\"COUNTRY\",\"SOURCE\",\"SEX\",\"AGE\"]).agg({'PRICE':'mean'}).head(5)"],"metadata":{"id":"Cz41K1QGfyPT","colab":{"base_uri":"https://localhost:8080/","height":237},"executionInfo":{"status":"ok","timestamp":1667228440788,"user_tz":-180,"elapsed":137,"user":{"displayName":"tohid yousefi","userId":"09164737776488206070"}},"outputId":"c4be64ea-15ce-478a-b600-5070fe31fb50"},"execution_count":34,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                PRICE\n","COUNTRY SOURCE  SEX    AGE           \n","bra     android female 15   38.714286\n","                       16   35.944444\n","                       17   35.666667\n","                       18   32.255814\n","                       19   35.206897"],"text/html":["\n","  <div id=\"df-9ccbbb53-7979-4528-8b52-dfadd70c6bd4\">\n","    <div class=\"colab-df-container\">\n","      <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></th>\n","      <th></th>\n","      <th></th>\n","      <th>PRICE</th>\n","    </tr>\n","    <tr>\n","      <th>COUNTRY</th>\n","      <th>SOURCE</th>\n","      <th>SEX</th>\n","      <th>AGE</th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th rowspan=\"5\" valign=\"top\">bra</th>\n","      <th rowspan=\"5\" valign=\"top\">android</th>\n","      <th rowspan=\"5\" valign=\"top\">female</th>\n","      <th>15</th>\n","      <td>38.714286</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>35.944444</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>35.666667</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>32.255814</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>35.206897</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9ccbbb53-7979-4528-8b52-dfadd70c6bd4')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":34}]},{"cell_type":"markdown","source":["### To better see the output in the previous question, apply the sort_values method in descending order of PRICE. and Save the Output as agg_df."],"metadata":{"id":"Qm1NBBYsshYm"}},{"cell_type":"code","source":["agg_df = df.groupby([\"COUNTRY\",\"SOURCE\",\"SEX\",\"AGE\"]).agg({'PRICE':'mean'}).sort_values(by='PRICE',ascending=False)\n","agg_df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":237},"id":"0CTD_PozqE4C","executionInfo":{"status":"ok","timestamp":1667228440790,"user_tz":-180,"elapsed":133,"user":{"displayName":"tohid yousefi","userId":"09164737776488206070"}},"outputId":"9543cc5c-823e-4f6e-be6b-2297513e405f"},"execution_count":35,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                            PRICE\n","COUNTRY SOURCE  SEX    AGE       \n","bra     android male   46    59.0\n","usa     android male   36    59.0\n","fra     android female 24    59.0\n","usa     ios     male   32    54.0\n","deu     android female 36    49.0"],"text/html":["\n","  <div id=\"df-caa8cda3-57f2-47a8-b19f-f58e65cae8aa\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        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<th>24</th>\n","      <td>59.0</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <th>ios</th>\n","      <th>male</th>\n","      <th>32</th>\n","      <td>54.0</td>\n","    </tr>\n","    <tr>\n","      <th>deu</th>\n","      <th>android</th>\n","      <th>female</th>\n","      <th>36</th>\n","      <td>49.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-caa8cda3-57f2-47a8-b19f-f58e65cae8aa')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":35}]},{"cell_type":"markdown","source":["### Converted the names in the index to variable names"],"metadata":{"id":"zM2fP61ts3ZK"}},{"cell_type":"code","source":["agg_df = agg_df.reset_index()\n","agg_df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"OsY0PZnbvyFm","executionInfo":{"status":"ok","timestamp":1667228440791,"user_tz":-180,"elapsed":129,"user":{"displayName":"tohid yousefi","userId":"09164737776488206070"}},"outputId":"72350cc7-8cd3-4ce8-855c-c11398b6d341"},"execution_count":36,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    COUNTRY   SOURCE     SEX  AGE  PRICE\n","0       bra  android    male   46   59.0\n","1       usa  android    male   36   59.0\n","2       fra  android  female   24   59.0\n","3       usa      ios    male   32   54.0\n","4       deu  android  female   36   49.0\n","..      ...      ...     ...  ...    ...\n","343     usa      ios  female   38   19.0\n","344     usa      ios  female   30   19.0\n","345     can  android  female   27   19.0\n","346     fra  android    male   18   19.0\n","347     deu  android    male   26    9.0\n","\n","[348 rows x 5 columns]"],"text/html":["\n","  <div id=\"df-a56ff3cb-300e-4164-b088-b372ec3508a5\">\n","    <div class=\"colab-df-container\">\n","      <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>COUNTRY</th>\n","      <th>SOURCE</th>\n","      <th>SEX</th>\n","      <th>AGE</th>\n","      <th>PRICE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>bra</td>\n","      <td>android</td>\n","      <td>male</td>\n","      <td>46</td>\n","      <td>59.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>usa</td>\n","      <td>android</td>\n","      <td>male</td>\n","      <td>36</td>\n","      <td>59.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>fra</td>\n","      <td>android</td>\n","      <td>female</td>\n","      <td>24</td>\n","      <td>59.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>usa</td>\n","      <td>ios</td>\n","      <td>male</td>\n","      <td>32</td>\n","      <td>54.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>deu</td>\n","      <td>android</td>\n","      <td>female</td>\n","      <td>36</td>\n","      <td>49.0</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>343</th>\n","      <td>usa</td>\n","      <td>ios</td>\n","      <td>female</td>\n","      <td>38</td>\n","      <td>19.0</td>\n","    </tr>\n","    <tr>\n","      <th>344</th>\n","      <td>usa</td>\n","      <td>ios</td>\n","      <td>female</td>\n","      <td>30</td>\n","      <td>19.0</td>\n","    </tr>\n","    <tr>\n","      <th>345</th>\n","      <td>can</td>\n","      <td>android</td>\n","      <td>female</td>\n","      <td>27</td>\n","      <td>19.0</td>\n","    </tr>\n","    <tr>\n","      <th>346</th>\n","      <td>fra</td>\n","      <td>android</td>\n","      <td>male</td>\n","      <td>18</td>\n","      <td>19.0</td>\n","    </tr>\n","    <tr>\n","      <th>347</th>\n","      <td>deu</td>\n","      <td>android</td>\n","      <td>male</td>\n","      <td>26</td>\n","      <td>9.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>348 rows × 5 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a56ff3cb-300e-4164-b088-b372ec3508a5')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       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pd.cut(agg_df[\"AGE\"],bins,labels=labels)\n","agg_df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"63pVCK354x2y","executionInfo":{"status":"ok","timestamp":1667228440791,"user_tz":-180,"elapsed":122,"user":{"displayName":"tohid yousefi","userId":"09164737776488206070"}},"outputId":"33edb020-7370-4ca9-ac79-8f0cdaf0b45e"},"execution_count":37,"outputs":[{"output_type":"execute_result","data":{"text/plain":["  COUNTRY   SOURCE     SEX  AGE  PRICE AGE_CAT\n","0     bra  android    male   46   59.0   41_66\n","1     usa  android    male   36   59.0   31_40\n","2     fra  android  female   24   59.0   24_30\n","3     usa      ios    male   32   54.0   31_40\n","4     deu  android  female   36   49.0   31_40"],"text/html":["\n","  <div id=\"df-75cbad05-e9d7-4f0b-b461-7509e8423da1\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    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<td>24</td>\n","      <td>59.0</td>\n","      <td>24_30</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>usa</td>\n","      <td>ios</td>\n","      <td>male</td>\n","      <td>32</td>\n","      <td>54.0</td>\n","      <td>31_40</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>deu</td>\n","      <td>android</td>\n","      <td>female</td>\n","      <td>36</td>\n","      <td>49.0</td>\n","      <td>31_40</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-75cbad05-e9d7-4f0b-b461-7509e8423da1')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 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agg_df.values])\n","agg_df[[\"CUSTOMERS_LEVEL_BASED\",\"PRICE\"]]"],"metadata":{"id":"L3nZeo2hC0SL","executionInfo":{"status":"ok","timestamp":1667228440792,"user_tz":-180,"elapsed":118,"user":{"displayName":"tohid yousefi","userId":"09164737776488206070"}},"colab":{"base_uri":"https://localhost:8080/","height":424},"outputId":"1dbcfb41-cde2-4990-a022-ebf4fe569f44"},"execution_count":38,"outputs":[{"output_type":"execute_result","data":{"text/plain":["        CUSTOMERS_LEVEL_BASED  PRICE\n","0      BRA_ANDROID_MALE_41_66   59.0\n","1      USA_ANDROID_MALE_31_40   59.0\n","2    FRA_ANDROID_FEMALE_24_30   59.0\n","3          USA_IOS_MALE_31_40   54.0\n","4    DEU_ANDROID_FEMALE_31_40   49.0\n","..                        ...    ...\n","343      USA_IOS_FEMALE_31_40   19.0\n","344      USA_IOS_FEMALE_24_30   19.0\n","345  CAN_ANDROID_FEMALE_24_30   19.0\n","346     FRA_ANDROID_MALE_0_18   19.0\n","347    DEU_ANDROID_MALE_24_30    9.0\n","\n","[348 rows x 2 columns]"],"text/html":["\n","  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<th>3</th>\n","      <td>USA_IOS_MALE_31_40</td>\n","      <td>54.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>DEU_ANDROID_FEMALE_31_40</td>\n","      <td>49.0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>343</th>\n","      <td>USA_IOS_FEMALE_31_40</td>\n","      <td>19.0</td>\n","    </tr>\n","    <tr>\n","      <th>344</th>\n","      <td>USA_IOS_FEMALE_24_30</td>\n","      <td>19.0</td>\n","    </tr>\n","    <tr>\n","      <th>345</th>\n","      <td>CAN_ANDROID_FEMALE_24_30</td>\n","      <td>19.0</td>\n","    </tr>\n","    <tr>\n","      <th>346</th>\n","      <td>FRA_ANDROID_MALE_0_18</td>\n","      <td>19.0</td>\n","    </tr>\n","    <tr>\n","      <th>347</th>\n","      <td>DEU_ANDROID_MALE_24_30</td>\n","      <td>9.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>348 rows × 2 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" 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agg_df.reset_index()\n","agg_df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"yqNWvzzaL2sL","executionInfo":{"status":"ok","timestamp":1667228440793,"user_tz":-180,"elapsed":112,"user":{"displayName":"tohid yousefi","userId":"09164737776488206070"}},"outputId":"a165e1db-4e4d-40ee-89a2-c5df9da42354"},"execution_count":39,"outputs":[{"output_type":"execute_result","data":{"text/plain":["        CUSTOMERS_LEVEL_BASED      PRICE\n","0    FRA_ANDROID_FEMALE_24_30  45.428571\n","1          TUR_IOS_MALE_24_30  45.000000\n","2          TUR_IOS_MALE_31_40  42.333333\n","3    TUR_ANDROID_FEMALE_31_40  41.833333\n","4      CAN_ANDROID_MALE_19_23  40.111111\n","..                        ...        ...\n","104        TUR_IOS_MALE_41_66  25.052632\n","105        TUR_IOS_MALE_19_23  24.833333\n","106     FRA_ANDROID_MALE_0_18  24.625000\n","107  CAN_ANDROID_FEMALE_24_30  19.000000\n","108    DEU_ANDROID_MALE_24_30  19.000000\n","\n","[109 rows x 2 columns]"],"text/html":["\n","  <div id=\"df-662e0425-7da7-4da1-8663-84c706b2c288\">\n","    <div class=\"colab-df-container\">\n","      <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>CUSTOMERS_LEVEL_BASED</th>\n","      <th>PRICE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>FRA_ANDROID_FEMALE_24_30</td>\n","      <td>45.428571</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>TUR_IOS_MALE_24_30</td>\n","      <td>45.000000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>TUR_IOS_MALE_31_40</td>\n","      <td>42.333333</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>TUR_ANDROID_FEMALE_31_40</td>\n","      <td>41.833333</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>CAN_ANDROID_MALE_19_23</td>\n","      <td>40.111111</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>104</th>\n","      <td>TUR_IOS_MALE_41_66</td>\n","      <td>25.052632</td>\n","    </tr>\n","    <tr>\n","      <th>105</th>\n","      <td>TUR_IOS_MALE_19_23</td>\n","      <td>24.833333</td>\n","    </tr>\n","    <tr>\n","      <th>106</th>\n","      <td>FRA_ANDROID_MALE_0_18</td>\n","      <td>24.625000</td>\n","    </tr>\n","    <tr>\n","      <th>107</th>\n","      <td>CAN_ANDROID_FEMALE_24_30</td>\n","      <td>19.000000</td>\n","    </tr>\n","    <tr>\n","      <th>108</th>\n","      <td>DEU_ANDROID_MALE_24_30</td>\n","      <td>19.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>109 rows × 2 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-662e0425-7da7-4da1-8663-84c706b2c288')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    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['mean','max','sum']})"],"metadata":{"id":"i5ITXIhCMWcN","executionInfo":{"status":"ok","timestamp":1667228440795,"user_tz":-180,"elapsed":108,"user":{"displayName":"tohid yousefi","userId":"09164737776488206070"}},"colab":{"base_uri":"https://localhost:8080/","height":237},"outputId":"bef2e6c8-36b6-4d94-f69c-9e38040016ca"},"execution_count":40,"outputs":[{"output_type":"execute_result","data":{"text/plain":["             PRICE                        \n","              mean        max          sum\n","SEGMENT                                   \n","D        29.206780  32.333333   817.789833\n","C        33.509674  34.077340   904.761209\n","B        34.999645  36.000000   944.990411\n","A        38.691234  45.428571  1044.663328"],"text/html":["\n","  <div id=\"df-c79fd196-bd6d-44f6-ab13-70336135d6d6\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    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<td>34.999645</td>\n","      <td>36.000000</td>\n","      <td>944.990411</td>\n","    </tr>\n","    <tr>\n","      <th>A</th>\n","      <td>38.691234</td>\n","      <td>45.428571</td>\n","      <td>1044.663328</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c79fd196-bd6d-44f6-ab13-70336135d6d6')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 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