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212 changes: 210 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 +36,77 @@
" - have a response \"Yes\" to the last marketing campaign."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "3c78618b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" total_claim_amount response\n",
"0 292.800000 No\n",
"1 744.924331 No\n",
"2 480.000000 No\n",
"3 484.013411 Yes\n",
"4 707.925645 No\n"
]
}
],
"source": [
"import pandas as pd\n",
"\n",
"# Load the dataset from the URL\n",
"url = \"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis.csv\"\n",
"df = pd.read_csv(url)\n",
"\n",
"# Standardize column names\n",
"df.columns = df.columns.str.lower().str.replace(' ', '_')\n",
"\n",
"print(df[['total_claim_amount', 'response']].head())"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "e9b16011",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Filtered DataFrame (low claim + said Yes):\n",
"(1399, 26)\n",
" customer total_claim_amount response gender education\n",
"0 XL78013 484.013411 Yes M College\n",
"1 FM55990 739.200000 Yes M College\n",
"2 CW49887 547.200000 Yes F Master\n",
"3 NJ54277 19.575683 Yes F College\n",
"4 MQ68407 60.036683 Yes F Bachelor\n"
]
}
],
"source": [
"# Clean the 'response' column (remove extra spaces and standardize)\n",
"df['response'] = df['response'].astype(str).str.strip().str.title()\n",
"\n",
"# Create the filtered DataFrame\n",
"low_claim_yes_response = df[\n",
" (df['total_claim_amount'] < 1000) &\n",
" (df['response'] == 'Yes')\n",
"].copy()\n",
"\n",
"# Reset index (optional)\n",
"low_claim_yes_response.reset_index(drop=True, inplace=True)\n",
"\n",
"print(\"Filtered DataFrame (low claim + said Yes):\")\n",
"print(low_claim_yes_response.shape)\n",
"print(low_claim_yes_response[['customer', 'total_claim_amount', 'response', 'gender', 'education']].head()) "
]
},
{
"cell_type": "markdown",
"id": "b9be383e-5165-436e-80c8-57d4c757c8c3",
Expand All @@ -48,6 +119,55 @@
" - 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": 9,
"id": "c347fa94",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Simple Analysis by Policy Type:\n",
"\n",
"\n",
" avg_premium avg_claims customer_count profit\n",
"policy_type \n",
"Corporate Auto 93.29 421.74 323 -328.45\n",
"Personal Auto 95.06 454.98 1076 -359.92\n",
"Special Auto 89.46 441.94 67 -352.48\n",
"\n",
"Which policy type is most profitable?\n",
"\n",
"\n",
"Most profitable: Corporate Auto ($-328.45 profit per customer)\n"
]
}
],
"source": [
"yes_customers = df[df['response'] == 'Yes']\n",
"\n",
"# Simple group by policy type only (easier to understand)\n",
"simple_analysis = yes_customers.groupby('policy_type').agg({\n",
" 'monthly_premium_auto': 'mean',\n",
" 'total_claim_amount': 'mean',\n",
" 'customer': 'count'\n",
"}).round(2)\n",
"\n",
"simple_analysis.columns = ['avg_premium', 'avg_claims', 'customer_count']\n",
"simple_analysis['profit'] = simple_analysis['avg_premium'] - simple_analysis['avg_claims']\n",
"\n",
"print(\"Simple Analysis by Policy Type:\")\n",
"print(\"\\n\")\n",
"print(simple_analysis)\n",
"\n",
"print(\"\\nWhich policy type is most profitable?\") \n",
"print(\"\\n\")\n",
"most_profitable = simple_analysis['profit'].idxmax()\n",
"print(f\"Most profitable: {most_profitable} (${simple_analysis.loc[most_profitable, 'profit']:.2f} profit per customer)\")"
]
},
{
"cell_type": "markdown",
"id": "7050f4ac-53c5-4193-a3c0-8699b87196f0",
Expand All @@ -58,6 +178,60 @@
"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": 8,
"id": "afa91674",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"All states and their customer counts:\n",
"\n",
"\n",
" state customer_count\n",
"0 California 3552\n",
"1 Oregon 2909\n",
"2 Arizona 1937\n",
"3 Nevada 993\n",
"4 Washington 888\n",
"\n",
"\n",
"STATES WITH 500+ CUSTOMERS:\n",
"\n",
"\n",
" state customer_count\n",
"0 California 3552\n",
"1 Oregon 2909\n",
"2 Arizona 1937\n",
"3 Nevada 993\n",
"4 Washington 888\n",
"\n",
"There are 5 states with more than 500 customers\n"
]
}
],
"source": [
"state_counts = df['state'].value_counts().reset_index()\n",
"state_counts.columns = ['state', 'customer_count']\n",
"\n",
"print(\"All states and their customer counts:\")\n",
"print(\"\\n\")\n",
"print(state_counts)\n",
"\n",
"# Filter for states with more than 500 customers\n",
"popular_states = state_counts[state_counts['customer_count'] > 500]\n",
"\n",
"print(\"\\n\")\n",
"print(\"STATES WITH 500+ CUSTOMERS:\")\n",
"print(\"\\n\")\n",
"print(popular_states)\n",
"\n",
"print(f\"\\nThere are {len(popular_states)} states with more than 500 customers\")"
]
},
{
"cell_type": "markdown",
"id": "b60a4443-a1a7-4bbf-b78e-9ccdf9895e0d",
Expand All @@ -68,6 +242,40 @@
"4. Find the maximum, minimum, and median customer lifetime value by education level and gender. Write your conclusions."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "27b8ac80",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" education gender max_clv min_clv median_clv\n",
"0 Bachelor F 73225.96 1904.00 5640.51\n",
"1 Bachelor M 67907.27 1898.01 5548.03\n",
"2 College F 61850.19 1898.68 5623.61\n",
"3 College M 61134.68 1918.12 6005.85\n",
"4 Doctor F 44856.11 2395.57 5332.46\n",
"5 Doctor M 32677.34 2267.60 5577.67\n",
"6 High School or Below F 55277.45 2144.92 6039.55\n",
"7 High School or Below M 83325.38 1940.98 6286.73\n",
"8 Master F 51016.07 2417.78 5729.86\n",
"9 Master M 50568.26 2272.31 5579.10\n"
]
}
],
"source": [
"clv_stats = df.groupby(['education', 'gender'])['customer_lifetime_value'].agg([\n",
" ('max_clv', 'max'),\n",
" ('min_clv', 'min'), \n",
" ('median_clv', 'median')\n",
"]).round(2).reset_index()\n",
"\n",
"print(clv_stats)"
]
},
{
"cell_type": "markdown",
"id": "b42999f9-311f-481e-ae63-40a5577072c5",
Expand Down Expand Up @@ -143,7 +351,7 @@
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "base",
"language": "python",
"name": "python3"
},
Expand All @@ -157,7 +365,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
"version": "3.13.5"
}
},
"nbformat": 4,
Expand Down