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174 changes: 172 additions & 2 deletions lab-dw-aggregating.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -36,6 +36,68 @@
" - have a response \"Yes\" to the last marketing campaign."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "f4cb4513",
"metadata": {},
"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",
" coverage education effective_to_date employmentstatus gender ... \\\n",
"3 Extended College 1/11/11 Employed M ... \n",
"8 Premium College 1/19/11 Employed M ... \n",
"15 Basic Master 1/16/11 Employed F ... \n",
"19 Extended College 2/26/11 Employed F ... \n",
"27 Premium Bachelor 2/28/11 Employed F ... \n",
"\n",
" number_of_open_complaints number_of_policies policy_type \\\n",
"3 0.0 2 Corporate Auto \n",
"8 0.0 1 Personal Auto \n",
"15 0.0 1 Special Auto \n",
"19 1.0 1 Personal Auto \n",
"27 0.0 1 Personal Auto \n",
"\n",
" policy renew_offer_type sales_channel total_claim_amount \\\n",
"3 Corporate L3 Offer2 Branch 484.013411 \n",
"8 Personal L1 Offer2 Branch 739.200000 \n",
"15 Special L1 Offer2 Branch 547.200000 \n",
"19 Personal L2 Offer2 Call Center 19.575683 \n",
"27 Personal L3 Offer2 Agent 60.036683 \n",
"\n",
" vehicle_class vehicle_size vehicle_type \n",
"3 Four-Door Car Medsize A \n",
"8 Sports Car Medsize NaN \n",
"15 SUV Medsize NaN \n",
"19 Two-Door Car Large A \n",
"27 Four-Door Car Medsize NaN \n",
"\n",
"[5 rows x 26 columns]\n"
]
}
],
"source": [
"import pandas as pd\n",
"\n",
"url = \"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis.csv\"\n",
"df = pd.read_csv(url)\n",
"\n",
"df.columns = df.columns.str.strip().str.lower().str.replace(' ', '_')\n",
"\n",
"filtered_df = df[(df['total_claim_amount'] < 1000) & (df['response'] == 'Yes')]\n",
"\n",
"print(filtered_df.head())\n"
]
},
{
"cell_type": "markdown",
"id": "b9be383e-5165-436e-80c8-57d4c757c8c3",
Expand All @@ -48,6 +110,55 @@
" - compare these insights to `total_claim_amount` patterns, and discuss which segments appear most profitable or low-risk for the company."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "207fe97f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" customer_lifetime_value monthly_premium_auto \\\n",
"policy_type gender \n",
"Corporate Auto F 7712.63 94.30 \n",
" M 7944.47 92.19 \n",
"Personal Auto F 8339.79 99.00 \n",
" M 7448.38 91.09 \n",
"Special Auto F 7691.58 92.31 \n",
" M 8247.09 86.34 \n",
"\n",
" total_claim_amount \n",
"policy_type gender \n",
"Corporate Auto F 433.74 \n",
" M 408.58 \n",
"Personal Auto F 452.97 \n",
" M 457.01 \n",
"Special Auto F 453.28 \n",
" M 429.53 \n"
]
}
],
"source": [
"import pandas as pd\n",
"\n",
"url = \"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis.csv\"\n",
"df = pd.read_csv(url)\n",
"\n",
"df.columns = df.columns.str.strip().str.lower().str.replace(' ', '_')\n",
"\n",
"df_yes = df[df['response'] == 'Yes']\n",
"\n",
"avg_metrics = df_yes.pivot_table(\n",
" index=['policy_type', 'gender'],\n",
" values=['monthly_premium_auto', 'customer_lifetime_value', 'total_claim_amount'],\n",
" aggfunc='mean'\n",
").round(2)\n",
"\n",
"print(avg_metrics)\n"
]
},
{
"cell_type": "markdown",
"id": "7050f4ac-53c5-4193-a3c0-8699b87196f0",
Expand All @@ -58,6 +169,34 @@
"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."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "6a039528",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"state\n",
"California 3552\n",
"Oregon 2909\n",
"Arizona 1937\n",
"Nevada 993\n",
"Washington 888\n",
"Name: count, dtype: int64\n"
]
}
],
"source": [
"state_counts = df['state'].value_counts()\n",
"\n",
"states_over_500 = state_counts[state_counts > 500]\n",
"\n",
"print(states_over_500)\n"
]
},
{
"cell_type": "markdown",
"id": "b60a4443-a1a7-4bbf-b78e-9ccdf9895e0d",
Expand All @@ -68,6 +207,37 @@
"4. Find the maximum, minimum, and median customer lifetime value by education level and gender. Write your conclusions."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "cf21916b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" max min median\n",
"education gender \n",
"Bachelor F 73225.96 1904.00 5640.51\n",
" M 67907.27 1898.01 5548.03\n",
"College F 61850.19 1898.68 5623.61\n",
" M 61134.68 1918.12 6005.85\n",
"Doctor F 44856.11 2395.57 5332.46\n",
" M 32677.34 2267.60 5577.67\n",
"High School or Below F 55277.45 2144.92 6039.55\n",
" M 83325.38 1940.98 6286.73\n",
"Master F 51016.07 2417.78 5729.86\n",
" M 50568.26 2272.31 5579.10\n"
]
}
],
"source": [
"clv_stats = df.groupby(['education', 'gender'])['customer_lifetime_value'].agg(['max', 'min', 'median']).round(2)\n",
"\n",
"print(clv_stats)\n"
]
},
{
"cell_type": "markdown",
"id": "b42999f9-311f-481e-ae63-40a5577072c5",
Expand Down Expand Up @@ -143,7 +313,7 @@
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "base",
"language": "python",
"name": "python3"
},
Expand All @@ -157,7 +327,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
"version": "3.12.2"
}
},
"nbformat": 4,
Expand Down