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262 changes: 258 additions & 4 deletions lab-dw-aggregating.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -115,6 +115,35 @@
"Hint: You can use melt to unpivot the data and create a table that shows the customer response rate (those who responded \"Yes\") by marketing channel."
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "509e3e33",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Unnamed: 0', 'Customer', 'State', 'Customer Lifetime Value',\n",
" 'Response', 'Coverage', 'Education', 'Effective To Date',\n",
" 'EmploymentStatus', 'Gender', 'Income', 'Location Code',\n",
" 'Marital Status', 'Monthly Premium Auto', 'Months Since Last Claim',\n",
" 'Months Since Policy Inception', 'Number of Open Complaints',\n",
" 'Number of Policies', 'Policy Type', 'Policy', 'Renew Offer Type',\n",
" 'Sales Channel', 'Total Claim Amount', 'Vehicle Class', 'Vehicle Size',\n",
" 'Vehicle Type'],\n",
" dtype='object')"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "markdown",
"id": "e4378d94-48fb-4850-a802-b1bc8f427b2d",
Expand All @@ -132,18 +161,243 @@
"metadata": {
"id": "449513f4-0459-46a0-a18d-9398d974c9ad"
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Unnamed: 0 Customer State Customer Lifetime Value Response \\\n",
"3 3 XL78013 Oregon 22332.439460 Yes \n",
"8 8 FM55990 California 5989.773931 Yes \n",
"15 15 CW49887 California 4626.801093 Yes \n",
"19 19 NJ54277 California 3746.751625 Yes \n",
"27 27 MQ68407 Oregon 4376.363592 Yes \n",
"... ... ... ... ... ... \n",
"10844 10844 FM31768 Arizona 5979.724161 Yes \n",
"10852 10852 KZ80424 Washington 8382.478392 Yes \n",
"10872 10872 XT67997 California 5979.724161 Yes \n",
"10887 10887 BY78730 Oregon 8879.790017 Yes \n",
"10897 10897 MM70762 Arizona 9075.768214 Yes \n",
"\n",
" Coverage Education Effective To Date EmploymentStatus \\\n",
"3 Extended College 1/11/11 Employed \n",
"8 Premium College 1/19/11 Employed \n",
"15 Basic Master 1/16/11 Employed \n",
"19 Extended College 2/26/11 Employed \n",
"27 Premium Bachelor 2/28/11 Employed \n",
"... ... ... ... ... \n",
"10844 Extended High School or Below 2/7/11 Employed \n",
"10852 Basic Bachelor 1/27/11 Employed \n",
"10872 Extended High School or Below 2/7/11 Employed \n",
"10887 Basic High School or Below 2/3/11 Employed \n",
"10897 Basic Master 1/26/11 Employed \n",
"\n",
" Gender ... Number of Open Complaints Number of Policies \\\n",
"3 M ... 0.0 2 \n",
"8 M ... 0.0 1 \n",
"15 F ... 0.0 1 \n",
"19 F ... 1.0 1 \n",
"27 F ... 0.0 1 \n",
"... ... ... ... ... \n",
"10844 F ... 0.0 3 \n",
"10852 M ... 0.0 2 \n",
"10872 F ... 0.0 3 \n",
"10887 F ... 0.0 7 \n",
"10897 M ... 0.0 8 \n",
"\n",
" Policy Type Policy Renew Offer Type Sales Channel \\\n",
"3 Corporate Auto Corporate L3 Offer2 Branch \n",
"8 Personal Auto Personal L1 Offer2 Branch \n",
"15 Special Auto Special L1 Offer2 Branch \n",
"19 Personal Auto Personal L2 Offer2 Call Center \n",
"27 Personal Auto Personal L3 Offer2 Agent \n",
"... ... ... ... ... \n",
"10844 Personal Auto Personal L1 Offer2 Agent \n",
"10852 Personal Auto Personal L2 Offer2 Call Center \n",
"10872 Personal Auto Personal L3 Offer2 Agent \n",
"10887 Special Auto Special L2 Offer1 Agent \n",
"10897 Personal Auto Personal L1 Offer1 Agent \n",
"\n",
" Total Claim Amount Vehicle Class Vehicle Size Vehicle Type \n",
"3 484.013411 Four-Door Car Medsize A \n",
"8 739.200000 Sports Car Medsize NaN \n",
"15 547.200000 SUV Medsize NaN \n",
"19 19.575683 Two-Door Car Large A \n",
"27 60.036683 Four-Door Car Medsize NaN \n",
"... ... ... ... ... \n",
"10844 547.200000 Four-Door Car Medsize NaN \n",
"10852 791.878042 NaN NaN A \n",
"10872 547.200000 Four-Door Car Medsize NaN \n",
"10887 528.200860 SUV Small A \n",
"10897 158.077504 Sports Car Medsize A \n",
"\n",
"[1399 rows x 26 columns]\n"
]
},
{
"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>Education</th>\n",
" <th>Gender</th>\n",
" <th>Max CLV</th>\n",
" <th>Min CLV</th>\n",
" <th>Median CLV</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Bachelor</td>\n",
" <td>F</td>\n",
" <td>73225.95652</td>\n",
" <td>1904.000852</td>\n",
" <td>5640.505303</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Bachelor</td>\n",
" <td>M</td>\n",
" <td>67907.27050</td>\n",
" <td>1898.007675</td>\n",
" <td>5548.031892</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>College</td>\n",
" <td>F</td>\n",
" <td>61850.18803</td>\n",
" <td>1898.683686</td>\n",
" <td>5623.611187</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>College</td>\n",
" <td>M</td>\n",
" <td>61134.68307</td>\n",
" <td>1918.119700</td>\n",
" <td>6005.847375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Doctor</td>\n",
" <td>F</td>\n",
" <td>44856.11397</td>\n",
" <td>2395.570000</td>\n",
" <td>5332.462694</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Education Gender Max CLV Min CLV Median CLV\n",
"0 Bachelor F 73225.95652 1904.000852 5640.505303\n",
"1 Bachelor M 67907.27050 1898.007675 5548.031892\n",
"2 College F 61850.18803 1898.683686 5623.611187\n",
"3 College M 61134.68307 1918.119700 6005.847375\n",
"4 Doctor F 44856.11397 2395.570000 5332.462694"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# your code goes here"
"import pandas as pd\n",
"url = 'https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis.csv'\n",
"df = pd.read_csv(url)\n",
"\n",
"#new df\n",
"# 1. Create a new DataFrame that only includes customers who:\n",
"# - have a **low total_claim_amount** (e.g., below $1,000),\n",
"# - have a response \"Yes\" to the last marketing campaign.\n",
"\n",
"\n",
"df['Total Claim Amount'] = pd.to_numeric(df['Total Claim Amount'], errors='coerce')\n",
"\n",
"def filter_values(row):\n",
" if row['Response'] == 'Yes' and row['Total Claim Amount'] < 1000:\n",
" return True\n",
" return False\n",
"filtered_df = df[['Customer','Response','Total Claim Amount']]\n",
"filtered_df = df[df.apply(filter_values, axis=1)]\n",
"\n",
"print(filtered_df)\n",
"\n",
"# 2. Using the original Dataframe, analyze:\n",
"# - the average `monthly_premium` and/or customer lifetime value by `policy_type` and `gender` for customers who responded \"Yes\", and\n",
"# - compare these insights to `total_claim_amount` patterns, and discuss which segments appear most profitable or low-risk for the company.\n",
"\n",
"def filter_values_yes_only(row):\n",
" return row['Response'] == 'Yes'\n",
"\n",
"filtered_df2 = df[df.apply(filter_values_yes_only, axis=1)]\n",
"filtered_df2 = filtered_df2[['Customer','Response','Monthly Premium Auto','Customer Lifetime Value','Policy Type','Gender','Total Claim Amount']]\n",
"\n",
"grouped_stats = filtered_df2.groupby(['Policy Type', 'Gender']).agg({\n",
" 'Monthly Premium Auto': 'mean',\n",
" 'Customer Lifetime Value': 'mean',\n",
" 'Total Claim Amount': 'mean'\n",
"}).reset_index()\n",
"\n",
"grouped_stats['Avg Monthly Premium'] = grouped_stats['Monthly Premium Auto']\n",
"grouped_stats['Avg CLV'] = grouped_stats['Customer Lifetime Value']\n",
"grouped_stats['Avg Total Claim Amount'] = grouped_stats['Total Claim Amount']\n",
"\n",
"grouped_stats.head()\n",
"\n",
"# 3. Analyze the total number of customers who have policies in each state, and then filter the results to only include states where there are more than 500 customers.\n",
"filtered_df3 = df[['Customer', 'State']]\n",
"state_counts = filtered_df3['State'].value_counts().reset_index()\n",
"state_counts.columns = ['State','Customer Count']\n",
"state_counts = state_counts[state_counts['Customer Count'] > 500]\n",
"\n",
"state_counts.head()\n",
"\n",
"# 4. Find the maximum, minimum, and median customer lifetime value by education level and gender. Write your conclusions.\n",
"filtered_df4 = df[['Customer Lifetime Value', 'Education','Gender']]\n",
"grouped_stats2 = filtered_df4.groupby(['Education', 'Gender']).agg({\n",
" 'Customer Lifetime Value': ['max','min','median']\n",
"\n",
"}).reset_index()\n",
"grouped_stats2.columns = ['Education', 'Gender','Max CLV','Min CLV','Median CLV']\n",
"grouped_stats2.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d75abbb1",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "base",
"language": "python",
"name": "python3"
},
Expand All @@ -157,7 +411,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
"version": "3.12.2"
}
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"nbformat": 4,
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