diff --git a/Customer Personality Analysis/Dataset/marketing_campaign.csv b/Customer Personality Analysis/Dataset/marketing_campaign.csv
new file mode 100644
index 000000000..8e84a342f
--- /dev/null
+++ b/Customer Personality Analysis/Dataset/marketing_campaign.csv
@@ -0,0 +1,2241 @@
+ID Year_Birth Education Marital_Status Income Kidhome Teenhome Dt_Customer Recency MntWines MntFruits MntMeatProducts MntFishProducts MntSweetProducts MntGoldProds NumDealsPurchases NumWebPurchases NumCatalogPurchases NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Z_CostContact Z_Revenue Response
+5524 1957 Graduation Single 58138 0 0 04-09-2012 58 635 88 546 172 88 88 3 8 10 4 7 0 0 0 0 0 0 3 11 1
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+6177 1985 PhD Married 33454 1 0 08-05-2013 32 76 10 56 3 1 23 2 4 0 4 8 0 0 0 0 0 0 3 11 0
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+2125 1959 Graduation Divorced 63033 0 0 15-11-2013 82 194 61 480 225 112 30 1 3 4 8 2 0 0 0 0 0 0 3 11 0
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+5710 1970 Graduation Together 40548 0 1 10-10-2012 31 110 0 5 2 0 3 2 2 1 4 5 0 1 0 0 0 0 3 11 0
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+4339 1970 PhD Married 67353 0 1 31-12-2013 37 702 17 151 0 8 35 5 5 6 12 2 0 0 0 0 0 0 3 11 0
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+8430 1957 Graduation Together 21994 0 1 24-12-2012 4 9 0 6 3 1 3 1 0 0 3 5 0 0 0 0 0 0 3 11 0
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+6312 1959 Graduation Married 65031 0 1 17-03-2013 29 258 107 291 84 37 86 4 10 3 13 7 0 0 0 0 0 0 3 11 0
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+9215 1980 PhD Married 43974 1 0 12-12-2012 19 378 0 101 0 0 72 5 6 4 6 7 0 0 0 0 0 0 3 11 0
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diff --git a/Customer Personality Analysis/Model/Customer personality Analysis.ipynb b/Customer Personality Analysis/Model/Customer personality Analysis.ipynb
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--- /dev/null
+++ b/Customer Personality Analysis/Model/Customer personality Analysis.ipynb
@@ -0,0 +1,4386 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "id": "eac0fe1c-8ad3-465c-a68b-50daf0600837",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from sklearn.metrics.cluster import v_measure_score, completeness_score"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "6bcead0d-40ac-415f-93ec-9af31827beec",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data = pd.read_csv(\"marketing_campaign.csv\", delimiter=\"\\t\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "01c56618-709f-4bce-824d-619149ebc36c",
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+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
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+ "Year_Birth 1957 1954 1965 1984 \n",
+ "Education Graduation Graduation Graduation Graduation \n",
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+ "Income 58138.0 46344.0 71613.0 26646.0 \n",
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+ " 3 | \n",
+ " 11 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " 6 | \n",
+ " 965 | \n",
+ " 1971 | \n",
+ " Graduation | \n",
+ " Divorced | \n",
+ " 55635.0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 13-11-2012 | \n",
+ " 34 | \n",
+ " 235 | \n",
+ " ... | \n",
+ " 6 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 11 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " 7 | \n",
+ " 6177 | \n",
+ " 1985 | \n",
+ " PhD | \n",
+ " Married | \n",
+ " 33454.0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 08-05-2013 | \n",
+ " 32 | \n",
+ " 76 | \n",
+ " ... | \n",
+ " 8 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 11 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " 8 | \n",
+ " 4855 | \n",
+ " 1974 | \n",
+ " PhD | \n",
+ " Together | \n",
+ " 30351.0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 06-06-2013 | \n",
+ " 19 | \n",
+ " 14 | \n",
+ " ... | \n",
+ " 9 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 11 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " 9 | \n",
+ " 5899 | \n",
+ " 1950 | \n",
+ " PhD | \n",
+ " Together | \n",
+ " 5648.0 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " 13-03-2014 | \n",
+ " 68 | \n",
+ " 28 | \n",
+ " ... | \n",
+ " 20 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 11 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
10 rows × 29 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " ID Year_Birth Education Marital_Status Income Kidhome Teenhome \\\n",
+ "0 5524 1957 Graduation Single 58138.0 0 0 \n",
+ "1 2174 1954 Graduation Single 46344.0 1 1 \n",
+ "2 4141 1965 Graduation Together 71613.0 0 0 \n",
+ "3 6182 1984 Graduation Together 26646.0 1 0 \n",
+ "4 5324 1981 PhD Married 58293.0 1 0 \n",
+ "5 7446 1967 Master Together 62513.0 0 1 \n",
+ "6 965 1971 Graduation Divorced 55635.0 0 1 \n",
+ "7 6177 1985 PhD Married 33454.0 1 0 \n",
+ "8 4855 1974 PhD Together 30351.0 1 0 \n",
+ "9 5899 1950 PhD Together 5648.0 1 1 \n",
+ "\n",
+ " Dt_Customer Recency MntWines ... NumWebVisitsMonth AcceptedCmp3 \\\n",
+ "0 04-09-2012 58 635 ... 7 0 \n",
+ "1 08-03-2014 38 11 ... 5 0 \n",
+ "2 21-08-2013 26 426 ... 4 0 \n",
+ "3 10-02-2014 26 11 ... 6 0 \n",
+ "4 19-01-2014 94 173 ... 5 0 \n",
+ "5 09-09-2013 16 520 ... 6 0 \n",
+ "6 13-11-2012 34 235 ... 6 0 \n",
+ "7 08-05-2013 32 76 ... 8 0 \n",
+ "8 06-06-2013 19 14 ... 9 0 \n",
+ "9 13-03-2014 68 28 ... 20 1 \n",
+ "\n",
+ " AcceptedCmp4 AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain \\\n",
+ "0 0 0 0 0 0 \n",
+ "1 0 0 0 0 0 \n",
+ "2 0 0 0 0 0 \n",
+ "3 0 0 0 0 0 \n",
+ "4 0 0 0 0 0 \n",
+ "5 0 0 0 0 0 \n",
+ "6 0 0 0 0 0 \n",
+ "7 0 0 0 0 0 \n",
+ "8 0 0 0 0 0 \n",
+ "9 0 0 0 0 0 \n",
+ "\n",
+ " Z_CostContact Z_Revenue Response \n",
+ "0 3 11 1 \n",
+ "1 3 11 0 \n",
+ "2 3 11 0 \n",
+ "3 3 11 0 \n",
+ "4 3 11 0 \n",
+ "5 3 11 0 \n",
+ "6 3 11 0 \n",
+ "7 3 11 0 \n",
+ "8 3 11 1 \n",
+ "9 3 11 0 \n",
+ "\n",
+ "[10 rows x 29 columns]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.head(10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cd0c065d-c895-492d-bb99-2f9eeeb9af6d",
+ "metadata": {},
+ "source": [
+ "## EDA"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "6726ffd3-761f-47fa-be46-294ee2b128a0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "RangeIndex: 2240 entries, 0 to 2239\n",
+ "Data columns (total 29 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 ID 2240 non-null int64 \n",
+ " 1 Year_Birth 2240 non-null int64 \n",
+ " 2 Education 2240 non-null object \n",
+ " 3 Marital_Status 2240 non-null object \n",
+ " 4 Income 2216 non-null float64\n",
+ " 5 Kidhome 2240 non-null int64 \n",
+ " 6 Teenhome 2240 non-null int64 \n",
+ " 7 Dt_Customer 2240 non-null object \n",
+ " 8 Recency 2240 non-null int64 \n",
+ " 9 MntWines 2240 non-null int64 \n",
+ " 10 MntFruits 2240 non-null int64 \n",
+ " 11 MntMeatProducts 2240 non-null int64 \n",
+ " 12 MntFishProducts 2240 non-null int64 \n",
+ " 13 MntSweetProducts 2240 non-null int64 \n",
+ " 14 MntGoldProds 2240 non-null int64 \n",
+ " 15 NumDealsPurchases 2240 non-null int64 \n",
+ " 16 NumWebPurchases 2240 non-null int64 \n",
+ " 17 NumCatalogPurchases 2240 non-null int64 \n",
+ " 18 NumStorePurchases 2240 non-null int64 \n",
+ " 19 NumWebVisitsMonth 2240 non-null int64 \n",
+ " 20 AcceptedCmp3 2240 non-null int64 \n",
+ " 21 AcceptedCmp4 2240 non-null int64 \n",
+ " 22 AcceptedCmp5 2240 non-null int64 \n",
+ " 23 AcceptedCmp1 2240 non-null int64 \n",
+ " 24 AcceptedCmp2 2240 non-null int64 \n",
+ " 25 Complain 2240 non-null int64 \n",
+ " 26 Z_CostContact 2240 non-null int64 \n",
+ " 27 Z_Revenue 2240 non-null int64 \n",
+ " 28 Response 2240 non-null int64 \n",
+ "dtypes: float64(1), int64(25), object(3)\n",
+ "memory usage: 507.6+ KB\n"
+ ]
+ }
+ ],
+ "source": [
+ "data.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "2e25511f-fab1-4005-8d2f-a8d482b162a1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " ID | \n",
+ " Year_Birth | \n",
+ " Income | \n",
+ " Kidhome | \n",
+ " Teenhome | \n",
+ " Recency | \n",
+ " MntWines | \n",
+ " MntFruits | \n",
+ " MntMeatProducts | \n",
+ " MntFishProducts | \n",
+ " ... | \n",
+ " NumWebVisitsMonth | \n",
+ " AcceptedCmp3 | \n",
+ " AcceptedCmp4 | \n",
+ " AcceptedCmp5 | \n",
+ " AcceptedCmp1 | \n",
+ " AcceptedCmp2 | \n",
+ " Complain | \n",
+ " Z_CostContact | \n",
+ " Z_Revenue | \n",
+ " Response | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " count | \n",
+ " 2240.000000 | \n",
+ " 2240.000000 | \n",
+ " 2216.000000 | \n",
+ " 2240.000000 | \n",
+ " 2240.000000 | \n",
+ " 2240.000000 | \n",
+ " 2240.000000 | \n",
+ " 2240.000000 | \n",
+ " 2240.000000 | \n",
+ " 2240.000000 | \n",
+ " ... | \n",
+ " 2240.000000 | \n",
+ " 2240.000000 | \n",
+ " 2240.000000 | \n",
+ " 2240.000000 | \n",
+ " 2240.000000 | \n",
+ " 2240.000000 | \n",
+ " 2240.000000 | \n",
+ " 2240.0 | \n",
+ " 2240.0 | \n",
+ " 2240.000000 | \n",
+ "
\n",
+ " \n",
+ " mean | \n",
+ " 5592.159821 | \n",
+ " 1968.805804 | \n",
+ " 52247.251354 | \n",
+ " 0.444196 | \n",
+ " 0.506250 | \n",
+ " 49.109375 | \n",
+ " 303.935714 | \n",
+ " 26.302232 | \n",
+ " 166.950000 | \n",
+ " 37.525446 | \n",
+ " ... | \n",
+ " 5.316518 | \n",
+ " 0.072768 | \n",
+ " 0.074554 | \n",
+ " 0.072768 | \n",
+ " 0.064286 | \n",
+ " 0.013393 | \n",
+ " 0.009375 | \n",
+ " 3.0 | \n",
+ " 11.0 | \n",
+ " 0.149107 | \n",
+ "
\n",
+ " \n",
+ " std | \n",
+ " 3246.662198 | \n",
+ " 11.984069 | \n",
+ " 25173.076661 | \n",
+ " 0.538398 | \n",
+ " 0.544538 | \n",
+ " 28.962453 | \n",
+ " 336.597393 | \n",
+ " 39.773434 | \n",
+ " 225.715373 | \n",
+ " 54.628979 | \n",
+ " ... | \n",
+ " 2.426645 | \n",
+ " 0.259813 | \n",
+ " 0.262728 | \n",
+ " 0.259813 | \n",
+ " 0.245316 | \n",
+ " 0.114976 | \n",
+ " 0.096391 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.356274 | \n",
+ "
\n",
+ " \n",
+ " min | \n",
+ " 0.000000 | \n",
+ " 1893.000000 | \n",
+ " 1730.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " ... | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 3.0 | \n",
+ " 11.0 | \n",
+ " 0.000000 | \n",
+ "
\n",
+ " \n",
+ " 25% | \n",
+ " 2828.250000 | \n",
+ " 1959.000000 | \n",
+ " 35303.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 24.000000 | \n",
+ " 23.750000 | \n",
+ " 1.000000 | \n",
+ " 16.000000 | \n",
+ " 3.000000 | \n",
+ " ... | \n",
+ " 3.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 3.0 | \n",
+ " 11.0 | \n",
+ " 0.000000 | \n",
+ "
\n",
+ " \n",
+ " 50% | \n",
+ " 5458.500000 | \n",
+ " 1970.000000 | \n",
+ " 51381.500000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 49.000000 | \n",
+ " 173.500000 | \n",
+ " 8.000000 | \n",
+ " 67.000000 | \n",
+ " 12.000000 | \n",
+ " ... | \n",
+ " 6.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 3.0 | \n",
+ " 11.0 | \n",
+ " 0.000000 | \n",
+ "
\n",
+ " \n",
+ " 75% | \n",
+ " 8427.750000 | \n",
+ " 1977.000000 | \n",
+ " 68522.000000 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ " 74.000000 | \n",
+ " 504.250000 | \n",
+ " 33.000000 | \n",
+ " 232.000000 | \n",
+ " 50.000000 | \n",
+ " ... | \n",
+ " 7.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 3.0 | \n",
+ " 11.0 | \n",
+ " 0.000000 | \n",
+ "
\n",
+ " \n",
+ " max | \n",
+ " 11191.000000 | \n",
+ " 1996.000000 | \n",
+ " 666666.000000 | \n",
+ " 2.000000 | \n",
+ " 2.000000 | \n",
+ " 99.000000 | \n",
+ " 1493.000000 | \n",
+ " 199.000000 | \n",
+ " 1725.000000 | \n",
+ " 259.000000 | \n",
+ " ... | \n",
+ " 20.000000 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ " 3.0 | \n",
+ " 11.0 | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
8 rows × 26 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " ID Year_Birth Income Kidhome Teenhome \\\n",
+ "count 2240.000000 2240.000000 2216.000000 2240.000000 2240.000000 \n",
+ "mean 5592.159821 1968.805804 52247.251354 0.444196 0.506250 \n",
+ "std 3246.662198 11.984069 25173.076661 0.538398 0.544538 \n",
+ "min 0.000000 1893.000000 1730.000000 0.000000 0.000000 \n",
+ "25% 2828.250000 1959.000000 35303.000000 0.000000 0.000000 \n",
+ "50% 5458.500000 1970.000000 51381.500000 0.000000 0.000000 \n",
+ "75% 8427.750000 1977.000000 68522.000000 1.000000 1.000000 \n",
+ "max 11191.000000 1996.000000 666666.000000 2.000000 2.000000 \n",
+ "\n",
+ " Recency MntWines MntFruits MntMeatProducts \\\n",
+ "count 2240.000000 2240.000000 2240.000000 2240.000000 \n",
+ "mean 49.109375 303.935714 26.302232 166.950000 \n",
+ "std 28.962453 336.597393 39.773434 225.715373 \n",
+ "min 0.000000 0.000000 0.000000 0.000000 \n",
+ "25% 24.000000 23.750000 1.000000 16.000000 \n",
+ "50% 49.000000 173.500000 8.000000 67.000000 \n",
+ "75% 74.000000 504.250000 33.000000 232.000000 \n",
+ "max 99.000000 1493.000000 199.000000 1725.000000 \n",
+ "\n",
+ " MntFishProducts ... NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 \\\n",
+ "count 2240.000000 ... 2240.000000 2240.000000 2240.000000 \n",
+ "mean 37.525446 ... 5.316518 0.072768 0.074554 \n",
+ "std 54.628979 ... 2.426645 0.259813 0.262728 \n",
+ "min 0.000000 ... 0.000000 0.000000 0.000000 \n",
+ "25% 3.000000 ... 3.000000 0.000000 0.000000 \n",
+ "50% 12.000000 ... 6.000000 0.000000 0.000000 \n",
+ "75% 50.000000 ... 7.000000 0.000000 0.000000 \n",
+ "max 259.000000 ... 20.000000 1.000000 1.000000 \n",
+ "\n",
+ " AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Z_CostContact \\\n",
+ "count 2240.000000 2240.000000 2240.000000 2240.000000 2240.0 \n",
+ "mean 0.072768 0.064286 0.013393 0.009375 3.0 \n",
+ "std 0.259813 0.245316 0.114976 0.096391 0.0 \n",
+ "min 0.000000 0.000000 0.000000 0.000000 3.0 \n",
+ "25% 0.000000 0.000000 0.000000 0.000000 3.0 \n",
+ "50% 0.000000 0.000000 0.000000 0.000000 3.0 \n",
+ "75% 0.000000 0.000000 0.000000 0.000000 3.0 \n",
+ "max 1.000000 1.000000 1.000000 1.000000 3.0 \n",
+ "\n",
+ " Z_Revenue Response \n",
+ "count 2240.0 2240.000000 \n",
+ "mean 11.0 0.149107 \n",
+ "std 0.0 0.356274 \n",
+ "min 11.0 0.000000 \n",
+ "25% 11.0 0.000000 \n",
+ "50% 11.0 0.000000 \n",
+ "75% 11.0 0.000000 \n",
+ "max 11.0 1.000000 \n",
+ "\n",
+ "[8 rows x 26 columns]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# There are 28 columns which are of similar type\n",
+ "data.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "486dc65b-bb98-469c-9b8a-1bc1575c68cd",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2240"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "50eb9009-46a4-48fc-a8d2-973483f5b669",
+ "metadata": {},
+ "source": [
+ "## Data Engineering"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "23eeac12-0495-479d-a209-63fbdf187529",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "ID 0\n",
+ "Year_Birth 0\n",
+ "Education 0\n",
+ "Marital_Status 0\n",
+ "Income 24\n",
+ "Kidhome 0\n",
+ "Teenhome 0\n",
+ "Dt_Customer 0\n",
+ "Recency 0\n",
+ "MntWines 0\n",
+ "MntFruits 0\n",
+ "MntMeatProducts 0\n",
+ "MntFishProducts 0\n",
+ "MntSweetProducts 0\n",
+ "MntGoldProds 0\n",
+ "NumDealsPurchases 0\n",
+ "NumWebPurchases 0\n",
+ "NumCatalogPurchases 0\n",
+ "NumStorePurchases 0\n",
+ "NumWebVisitsMonth 0\n",
+ "AcceptedCmp3 0\n",
+ "AcceptedCmp4 0\n",
+ "AcceptedCmp5 0\n",
+ "AcceptedCmp1 0\n",
+ "AcceptedCmp2 0\n",
+ "Complain 0\n",
+ "Z_CostContact 0\n",
+ "Z_Revenue 0\n",
+ "Response 0\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.isna().sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "ff395ff3-1838-407b-a918-99c7fabb864e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Fill the values in Income with the median value\n",
+ "data[\"Income\"].fillna(data[\"Income\"].median(), inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "9fe90c53-dff0-4094-9566-88f7eb9e6bc8",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "ID 0\n",
+ "Year_Birth 0\n",
+ "Education 0\n",
+ "Marital_Status 0\n",
+ "Income 0\n",
+ "Kidhome 0\n",
+ "Teenhome 0\n",
+ "Dt_Customer 0\n",
+ "Recency 0\n",
+ "MntWines 0\n",
+ "MntFruits 0\n",
+ "MntMeatProducts 0\n",
+ "MntFishProducts 0\n",
+ "MntSweetProducts 0\n",
+ "MntGoldProds 0\n",
+ "NumDealsPurchases 0\n",
+ "NumWebPurchases 0\n",
+ "NumCatalogPurchases 0\n",
+ "NumStorePurchases 0\n",
+ "NumWebVisitsMonth 0\n",
+ "AcceptedCmp3 0\n",
+ "AcceptedCmp4 0\n",
+ "AcceptedCmp5 0\n",
+ "AcceptedCmp1 0\n",
+ "AcceptedCmp2 0\n",
+ "Complain 0\n",
+ "Z_CostContact 0\n",
+ "Z_Revenue 0\n",
+ "Response 0\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.isna().sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "95e61536-077b-4d6b-b854-81ba0f1b600e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Drop the ID, Z_CostContact and the Z_Revenue columns\n",
+ "data.drop([\"ID\", \"Z_CostContact\", \"Z_Revenue\"], axis=1, inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "72d80679-16b2-43ba-8223-0faaabcc9c23",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Year_Birth | \n",
+ " Education | \n",
+ " Marital_Status | \n",
+ " Income | \n",
+ " Kidhome | \n",
+ " Teenhome | \n",
+ " Dt_Customer | \n",
+ " Recency | \n",
+ " MntWines | \n",
+ " MntFruits | \n",
+ " ... | \n",
+ " NumCatalogPurchases | \n",
+ " NumStorePurchases | \n",
+ " NumWebVisitsMonth | \n",
+ " AcceptedCmp3 | \n",
+ " AcceptedCmp4 | \n",
+ " AcceptedCmp5 | \n",
+ " AcceptedCmp1 | \n",
+ " AcceptedCmp2 | \n",
+ " Complain | \n",
+ " Response | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 1957 | \n",
+ " Graduation | \n",
+ " Single | \n",
+ " 58138.0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 04-09-2012 | \n",
+ " 58 | \n",
+ " 635 | \n",
+ " 88 | \n",
+ " ... | \n",
+ " 10 | \n",
+ " 4 | \n",
+ " 7 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " 1954 | \n",
+ " Graduation | \n",
+ " Single | \n",
+ " 46344.0 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " 08-03-2014 | \n",
+ " 38 | \n",
+ " 11 | \n",
+ " 1 | \n",
+ " ... | \n",
+ " 1 | \n",
+ " 2 | \n",
+ " 5 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " 1965 | \n",
+ " Graduation | \n",
+ " Together | \n",
+ " 71613.0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 21-08-2013 | \n",
+ " 26 | \n",
+ " 426 | \n",
+ " 49 | \n",
+ " ... | \n",
+ " 2 | \n",
+ " 10 | \n",
+ " 4 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " 1984 | \n",
+ " Graduation | \n",
+ " Together | \n",
+ " 26646.0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 10-02-2014 | \n",
+ " 26 | \n",
+ " 11 | \n",
+ " 4 | \n",
+ " ... | \n",
+ " 0 | \n",
+ " 4 | \n",
+ " 6 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " 1981 | \n",
+ " PhD | \n",
+ " Married | \n",
+ " 58293.0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 19-01-2014 | \n",
+ " 94 | \n",
+ " 173 | \n",
+ " 43 | \n",
+ " ... | \n",
+ " 3 | \n",
+ " 6 | \n",
+ " 5 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
5 rows × 26 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Year_Birth Education Marital_Status Income Kidhome Teenhome \\\n",
+ "0 1957 Graduation Single 58138.0 0 0 \n",
+ "1 1954 Graduation Single 46344.0 1 1 \n",
+ "2 1965 Graduation Together 71613.0 0 0 \n",
+ "3 1984 Graduation Together 26646.0 1 0 \n",
+ "4 1981 PhD Married 58293.0 1 0 \n",
+ "\n",
+ " Dt_Customer Recency MntWines MntFruits ... NumCatalogPurchases \\\n",
+ "0 04-09-2012 58 635 88 ... 10 \n",
+ "1 08-03-2014 38 11 1 ... 1 \n",
+ "2 21-08-2013 26 426 49 ... 2 \n",
+ "3 10-02-2014 26 11 4 ... 0 \n",
+ "4 19-01-2014 94 173 43 ... 3 \n",
+ "\n",
+ " NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 \\\n",
+ "0 4 7 0 0 \n",
+ "1 2 5 0 0 \n",
+ "2 10 4 0 0 \n",
+ "3 4 6 0 0 \n",
+ "4 6 5 0 0 \n",
+ "\n",
+ " AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Response \n",
+ "0 0 0 0 0 1 \n",
+ "1 0 0 0 0 0 \n",
+ "2 0 0 0 0 0 \n",
+ "3 0 0 0 0 0 \n",
+ "4 0 0 0 0 0 \n",
+ "\n",
+ "[5 rows x 26 columns]"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "6a8a5c51-d686-4300-9619-eb7240b9a01f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Education\n",
+ "Graduation 1127\n",
+ "PhD 486\n",
+ "Master 370\n",
+ "2n Cycle 203\n",
+ "Basic 54\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data[\"Education\"].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "e7216ff6-b279-4246-9e84-521bac8a98a8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data[\"Education\"] = data[\"Education\"].replace([\"Graduation\", \"PhD\", \"Master\", \"2n Cycle\"], \"Post-Graduate\")\n",
+ "data[\"Education\"] = data[\"Education\"].replace(\"Basic\", \"Under-Graduate\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "a229a7f2-1e6b-4b30-aa4e-7ead14adf769",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Education\n",
+ "Post-Graduate 2186\n",
+ "Under-Graduate 54\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data[\"Education\"].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "4ea69211-b170-4c18-9e7f-ebac73b979b5",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Unique Values in the Education columns is Education\n",
+ "Post-Graduate 2186\n",
+ "Under-Graduate 54\n",
+ "Name: count, dtype: int64\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "print(f\"Unique Values in the Education columns is {data.Education.value_counts()}\")\n",
+ "print(\"\\n\")\n",
+ "\n",
+ "# Plotting the graph for the unique values\n",
+ "fig, ax = plt.subplots(figsize=(10, 10))\n",
+ "# Get the counts of unique values in the \"Education\" column\n",
+ "counts = data[\"Education\"].value_counts()\n",
+ "# Create a bar plot\n",
+ "ax.bar(counts.index, counts.values, color=\"turquoise\", edgecolor=\"black\", linewidth=3)\n",
+ "plt.title(\"Frequency Of Each Category in the Education Variable \\n\",fontsize=24)\n",
+ "fig.savefig(\"Documents/Customer Personality Analysis/\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "5034ec82-3ecd-492c-87fd-a6f2a8dc887a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Year_Birth | \n",
+ " Education | \n",
+ " Marital_Status | \n",
+ " Income | \n",
+ " Kidhome | \n",
+ " Teenhome | \n",
+ " Dt_Customer | \n",
+ " Recency | \n",
+ " MntWines | \n",
+ " MntFruits | \n",
+ " ... | \n",
+ " NumCatalogPurchases | \n",
+ " NumStorePurchases | \n",
+ " NumWebVisitsMonth | \n",
+ " AcceptedCmp3 | \n",
+ " AcceptedCmp4 | \n",
+ " AcceptedCmp5 | \n",
+ " AcceptedCmp1 | \n",
+ " AcceptedCmp2 | \n",
+ " Complain | \n",
+ " Response | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 1957 | \n",
+ " Post-Graduate | \n",
+ " Single | \n",
+ " 58138.0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 04-09-2012 | \n",
+ " 58 | \n",
+ " 635 | \n",
+ " 88 | \n",
+ " ... | \n",
+ " 10 | \n",
+ " 4 | \n",
+ " 7 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " 1954 | \n",
+ " Post-Graduate | \n",
+ " Single | \n",
+ " 46344.0 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " 08-03-2014 | \n",
+ " 38 | \n",
+ " 11 | \n",
+ " 1 | \n",
+ " ... | \n",
+ " 1 | \n",
+ " 2 | \n",
+ " 5 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " 1965 | \n",
+ " Post-Graduate | \n",
+ " Together | \n",
+ " 71613.0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 21-08-2013 | \n",
+ " 26 | \n",
+ " 426 | \n",
+ " 49 | \n",
+ " ... | \n",
+ " 2 | \n",
+ " 10 | \n",
+ " 4 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " 1984 | \n",
+ " Post-Graduate | \n",
+ " Together | \n",
+ " 26646.0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 10-02-2014 | \n",
+ " 26 | \n",
+ " 11 | \n",
+ " 4 | \n",
+ " ... | \n",
+ " 0 | \n",
+ " 4 | \n",
+ " 6 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " 1981 | \n",
+ " Post-Graduate | \n",
+ " Married | \n",
+ " 58293.0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 19-01-2014 | \n",
+ " 94 | \n",
+ " 173 | \n",
+ " 43 | \n",
+ " ... | \n",
+ " 3 | \n",
+ " 6 | \n",
+ " 5 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
5 rows × 26 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Year_Birth Education Marital_Status Income Kidhome Teenhome \\\n",
+ "0 1957 Post-Graduate Single 58138.0 0 0 \n",
+ "1 1954 Post-Graduate Single 46344.0 1 1 \n",
+ "2 1965 Post-Graduate Together 71613.0 0 0 \n",
+ "3 1984 Post-Graduate Together 26646.0 1 0 \n",
+ "4 1981 Post-Graduate Married 58293.0 1 0 \n",
+ "\n",
+ " Dt_Customer Recency MntWines MntFruits ... NumCatalogPurchases \\\n",
+ "0 04-09-2012 58 635 88 ... 10 \n",
+ "1 08-03-2014 38 11 1 ... 1 \n",
+ "2 21-08-2013 26 426 49 ... 2 \n",
+ "3 10-02-2014 26 11 4 ... 0 \n",
+ "4 19-01-2014 94 173 43 ... 3 \n",
+ "\n",
+ " NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 \\\n",
+ "0 4 7 0 0 \n",
+ "1 2 5 0 0 \n",
+ "2 10 4 0 0 \n",
+ "3 4 6 0 0 \n",
+ "4 6 5 0 0 \n",
+ "\n",
+ " AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Response \n",
+ "0 0 0 0 0 1 \n",
+ "1 0 0 0 0 0 \n",
+ "2 0 0 0 0 0 \n",
+ "3 0 0 0 0 0 \n",
+ "4 0 0 0 0 0 \n",
+ "\n",
+ "[5 rows x 26 columns]"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "1fa90a0f-eea9-4e8a-9305-caa51b747ce0",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Year_Birth | \n",
+ " Education | \n",
+ " Marital_Status | \n",
+ " Income | \n",
+ " Kidhome | \n",
+ " Teenhome | \n",
+ " Dt_Customer | \n",
+ " Recency | \n",
+ " MntWines | \n",
+ " MntFruits | \n",
+ " ... | \n",
+ " NumStorePurchases | \n",
+ " NumWebVisitsMonth | \n",
+ " AcceptedCmp3 | \n",
+ " AcceptedCmp4 | \n",
+ " AcceptedCmp5 | \n",
+ " AcceptedCmp1 | \n",
+ " AcceptedCmp2 | \n",
+ " Complain | \n",
+ " Response | \n",
+ " Kids | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 1957 | \n",
+ " Post-Graduate | \n",
+ " Single | \n",
+ " 58138.0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 04-09-2012 | \n",
+ " 58 | \n",
+ " 635 | \n",
+ " 88 | \n",
+ " ... | \n",
+ " 4 | \n",
+ " 7 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " 1954 | \n",
+ " Post-Graduate | \n",
+ " Single | \n",
+ " 46344.0 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " 08-03-2014 | \n",
+ " 38 | \n",
+ " 11 | \n",
+ " 1 | \n",
+ " ... | \n",
+ " 2 | \n",
+ " 5 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " 1965 | \n",
+ " Post-Graduate | \n",
+ " Together | \n",
+ " 71613.0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 21-08-2013 | \n",
+ " 26 | \n",
+ " 426 | \n",
+ " 49 | \n",
+ " ... | \n",
+ " 10 | \n",
+ " 4 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " 1984 | \n",
+ " Post-Graduate | \n",
+ " Together | \n",
+ " 26646.0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 10-02-2014 | \n",
+ " 26 | \n",
+ " 11 | \n",
+ " 4 | \n",
+ " ... | \n",
+ " 4 | \n",
+ " 6 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " 1981 | \n",
+ " Post-Graduate | \n",
+ " Married | \n",
+ " 58293.0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 19-01-2014 | \n",
+ " 94 | \n",
+ " 173 | \n",
+ " 43 | \n",
+ " ... | \n",
+ " 6 | \n",
+ " 5 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
5 rows × 27 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Year_Birth Education Marital_Status Income Kidhome Teenhome \\\n",
+ "0 1957 Post-Graduate Single 58138.0 0 0 \n",
+ "1 1954 Post-Graduate Single 46344.0 1 1 \n",
+ "2 1965 Post-Graduate Together 71613.0 0 0 \n",
+ "3 1984 Post-Graduate Together 26646.0 1 0 \n",
+ "4 1981 Post-Graduate Married 58293.0 1 0 \n",
+ "\n",
+ " Dt_Customer Recency MntWines MntFruits ... NumStorePurchases \\\n",
+ "0 04-09-2012 58 635 88 ... 4 \n",
+ "1 08-03-2014 38 11 1 ... 2 \n",
+ "2 21-08-2013 26 426 49 ... 10 \n",
+ "3 10-02-2014 26 11 4 ... 4 \n",
+ "4 19-01-2014 94 173 43 ... 6 \n",
+ "\n",
+ " NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 AcceptedCmp5 AcceptedCmp1 \\\n",
+ "0 7 0 0 0 0 \n",
+ "1 5 0 0 0 0 \n",
+ "2 4 0 0 0 0 \n",
+ "3 6 0 0 0 0 \n",
+ "4 5 0 0 0 0 \n",
+ "\n",
+ " AcceptedCmp2 Complain Response Kids \n",
+ "0 0 0 1 0 \n",
+ "1 0 0 0 2 \n",
+ "2 0 0 0 0 \n",
+ "3 0 0 0 1 \n",
+ "4 0 0 0 1 \n",
+ "\n",
+ "[5 rows x 27 columns]"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Changing the kidhome and teenhome\n",
+ "data[\"Kids\"] = data[\"Kidhome\"] + data[\"Teenhome\"]\n",
+ "data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "711370de-7e1a-4424-be06-5e10add6d01a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plotting the graph for the value counts of kids\n",
+ "fig, ax = plt.subplots(figsize=(5, 5))\n",
+ "counts = data.Kids.value_counts()\n",
+ "ax.bar(counts.index, counts.values, color=\"Orange\", edgecolor=\"grey\", linewidth=2)\n",
+ "plt.title(\"Value Counts for Kids \\n\", fontsize=10)\n",
+ "plt.xlabel(\"Kids\")\n",
+ "plt.ylabel(\"Value Counts\")\n",
+ "fig.savefig(\"Documents/Customer Personality Analysis/Images\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "66faafba-dcb7-4c7e-b0e0-25bc231ee64d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Marital_Status\n",
+ "Married 864\n",
+ "Together 580\n",
+ "Single 480\n",
+ "Divorced 232\n",
+ "Widow 77\n",
+ "Alone 3\n",
+ "Absurd 2\n",
+ "YOLO 2\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data[\"Marital_Status\"].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "f00be6f5-fe88-4ac2-9a7d-d6ba9bf88703",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Reduce the dimensonality\n",
+ "data[\"Marital_Status\"] = data[\"Marital_Status\"].replace([\"Married\", \"Together\"], \"In-Relationship\")\n",
+ "data[\"Marital_Status\"] = data[\"Marital_Status\"].replace([\"Single\", \"Divorced\", \"Widow\", \"Alone\", \"Absurd\", \"YOLO\"], \"Single\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "d2d395c6-49a9-4ee3-b486-9ab152b41c38",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Year_Birth | \n",
+ " Education | \n",
+ " Marital_Status | \n",
+ " Income | \n",
+ " Kidhome | \n",
+ " Teenhome | \n",
+ " Dt_Customer | \n",
+ " Recency | \n",
+ " MntWines | \n",
+ " MntFruits | \n",
+ " ... | \n",
+ " NumStorePurchases | \n",
+ " NumWebVisitsMonth | \n",
+ " AcceptedCmp3 | \n",
+ " AcceptedCmp4 | \n",
+ " AcceptedCmp5 | \n",
+ " AcceptedCmp1 | \n",
+ " AcceptedCmp2 | \n",
+ " Complain | \n",
+ " Response | \n",
+ " Kids | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 1957 | \n",
+ " Post-Graduate | \n",
+ " Single | \n",
+ " 58138.0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 04-09-2012 | \n",
+ " 58 | \n",
+ " 635 | \n",
+ " 88 | \n",
+ " ... | \n",
+ " 4 | \n",
+ " 7 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " 1954 | \n",
+ " Post-Graduate | \n",
+ " Single | \n",
+ " 46344.0 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " 08-03-2014 | \n",
+ " 38 | \n",
+ " 11 | \n",
+ " 1 | \n",
+ " ... | \n",
+ " 2 | \n",
+ " 5 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " 1965 | \n",
+ " Post-Graduate | \n",
+ " In-Relationship | \n",
+ " 71613.0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 21-08-2013 | \n",
+ " 26 | \n",
+ " 426 | \n",
+ " 49 | \n",
+ " ... | \n",
+ " 10 | \n",
+ " 4 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " 1984 | \n",
+ " Post-Graduate | \n",
+ " In-Relationship | \n",
+ " 26646.0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 10-02-2014 | \n",
+ " 26 | \n",
+ " 11 | \n",
+ " 4 | \n",
+ " ... | \n",
+ " 4 | \n",
+ " 6 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " 1981 | \n",
+ " Post-Graduate | \n",
+ " In-Relationship | \n",
+ " 58293.0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 19-01-2014 | \n",
+ " 94 | \n",
+ " 173 | \n",
+ " 43 | \n",
+ " ... | \n",
+ " 6 | \n",
+ " 5 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
5 rows × 27 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Year_Birth Education Marital_Status Income Kidhome Teenhome \\\n",
+ "0 1957 Post-Graduate Single 58138.0 0 0 \n",
+ "1 1954 Post-Graduate Single 46344.0 1 1 \n",
+ "2 1965 Post-Graduate In-Relationship 71613.0 0 0 \n",
+ "3 1984 Post-Graduate In-Relationship 26646.0 1 0 \n",
+ "4 1981 Post-Graduate In-Relationship 58293.0 1 0 \n",
+ "\n",
+ " Dt_Customer Recency MntWines MntFruits ... NumStorePurchases \\\n",
+ "0 04-09-2012 58 635 88 ... 4 \n",
+ "1 08-03-2014 38 11 1 ... 2 \n",
+ "2 21-08-2013 26 426 49 ... 10 \n",
+ "3 10-02-2014 26 11 4 ... 4 \n",
+ "4 19-01-2014 94 173 43 ... 6 \n",
+ "\n",
+ " NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 AcceptedCmp5 AcceptedCmp1 \\\n",
+ "0 7 0 0 0 0 \n",
+ "1 5 0 0 0 0 \n",
+ "2 4 0 0 0 0 \n",
+ "3 6 0 0 0 0 \n",
+ "4 5 0 0 0 0 \n",
+ "\n",
+ " AcceptedCmp2 Complain Response Kids \n",
+ "0 0 0 1 0 \n",
+ "1 0 0 0 2 \n",
+ "2 0 0 0 0 \n",
+ "3 0 0 0 1 \n",
+ "4 0 0 0 1 \n",
+ "\n",
+ "[5 rows x 27 columns]"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a98044db-3266-47f2-8f7d-1280c11459be",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig, ax = plt.subplots(figsize=(5, 5))\n",
+ "counts = data.Marital_Status.value_counts()\n",
+ "ax.bar(counts.index, counts.values, color=\"gold\", edgecolor=\"black\", linewidth=2)\n",
+ "plt.title(\"Frequency Of Each Category in the Marital_Status Variable \\n\",fontsize=10)\n",
+ "fig.savefig(\"Documents/Customer Personality Analysis/Images\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "e5f3550e-4a31-4c27-8af1-4d495d732932",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/tmp/ipykernel_4198/1351278601.py:2: UserWarning: \n",
+ "\n",
+ "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+ "\n",
+ "Please adapt your code to use either `displot` (a figure-level function with\n",
+ "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+ "\n",
+ "For a guide to updating your code to use the new functions, please see\n",
+ "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+ "\n",
+ " sns.distplot(data[\"Income\"],color = 'turquoise')\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(figsize=(8, 8))\n",
+ "sns.distplot(data[\"Income\"],color = 'turquoise')\n",
+ "plt.show()\n",
+ "data[\"Income\"].plot.box(figsize=(8,8),color = 'turquoise')\n",
+ "plt.show()\n",
+ "fig.savefig(\"Documents/Customer Personality Analysis/Images\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "c0ea3f06-f2e4-4af5-bf97-9350dfae25ec",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data[\"Expenses\"] = data[\"MntFishProducts\"] + data[\"MntFruits\"] + data[\"MntGoldProds\"] + data[\"MntMeatProducts\"] + data[\"MntSweetProducts\"] + data[\"MntWines\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "90ae6b88-a704-4561-8d45-08f9f4d96c7d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Year_Birth | \n",
+ " Education | \n",
+ " Marital_Status | \n",
+ " Income | \n",
+ " Kidhome | \n",
+ " Teenhome | \n",
+ " Dt_Customer | \n",
+ " Recency | \n",
+ " MntWines | \n",
+ " MntFruits | \n",
+ " ... | \n",
+ " NumWebVisitsMonth | \n",
+ " AcceptedCmp3 | \n",
+ " AcceptedCmp4 | \n",
+ " AcceptedCmp5 | \n",
+ " AcceptedCmp1 | \n",
+ " AcceptedCmp2 | \n",
+ " Complain | \n",
+ " Response | \n",
+ " Kids | \n",
+ " Expenses | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 1957 | \n",
+ " Post-Graduate | \n",
+ " Single | \n",
+ " 58138.0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 04-09-2012 | \n",
+ " 58 | \n",
+ " 635 | \n",
+ " 88 | \n",
+ " ... | \n",
+ " 7 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 1617 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " 1954 | \n",
+ " Post-Graduate | \n",
+ " Single | \n",
+ " 46344.0 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " 08-03-2014 | \n",
+ " 38 | \n",
+ " 11 | \n",
+ " 1 | \n",
+ " ... | \n",
+ " 5 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 2 | \n",
+ " 27 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " 1965 | \n",
+ " Post-Graduate | \n",
+ " In-Relationship | \n",
+ " 71613.0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 21-08-2013 | \n",
+ " 26 | \n",
+ " 426 | \n",
+ " 49 | \n",
+ " ... | \n",
+ " 4 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 776 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " 1984 | \n",
+ " Post-Graduate | \n",
+ " In-Relationship | \n",
+ " 26646.0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 10-02-2014 | \n",
+ " 26 | \n",
+ " 11 | \n",
+ " 4 | \n",
+ " ... | \n",
+ " 6 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 53 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " 1981 | \n",
+ " Post-Graduate | \n",
+ " In-Relationship | \n",
+ " 58293.0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 19-01-2014 | \n",
+ " 94 | \n",
+ " 173 | \n",
+ " 43 | \n",
+ " ... | \n",
+ " 5 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 422 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
5 rows × 28 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Year_Birth Education Marital_Status Income Kidhome Teenhome \\\n",
+ "0 1957 Post-Graduate Single 58138.0 0 0 \n",
+ "1 1954 Post-Graduate Single 46344.0 1 1 \n",
+ "2 1965 Post-Graduate In-Relationship 71613.0 0 0 \n",
+ "3 1984 Post-Graduate In-Relationship 26646.0 1 0 \n",
+ "4 1981 Post-Graduate In-Relationship 58293.0 1 0 \n",
+ "\n",
+ " Dt_Customer Recency MntWines MntFruits ... NumWebVisitsMonth \\\n",
+ "0 04-09-2012 58 635 88 ... 7 \n",
+ "1 08-03-2014 38 11 1 ... 5 \n",
+ "2 21-08-2013 26 426 49 ... 4 \n",
+ "3 10-02-2014 26 11 4 ... 6 \n",
+ "4 19-01-2014 94 173 43 ... 5 \n",
+ "\n",
+ " AcceptedCmp3 AcceptedCmp4 AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 \\\n",
+ "0 0 0 0 0 0 \n",
+ "1 0 0 0 0 0 \n",
+ "2 0 0 0 0 0 \n",
+ "3 0 0 0 0 0 \n",
+ "4 0 0 0 0 0 \n",
+ "\n",
+ " Complain Response Kids Expenses \n",
+ "0 0 1 0 1617 \n",
+ "1 0 0 2 27 \n",
+ "2 0 0 0 776 \n",
+ "3 0 0 1 53 \n",
+ "4 0 0 1 422 \n",
+ "\n",
+ "[5 rows x 28 columns]"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "0230acc1-8fcc-4ad1-a8d6-bf1fb71f3673",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Expenses\n",
+ "46 19\n",
+ "22 18\n",
+ "57 16\n",
+ "44 15\n",
+ "55 15\n",
+ " ..\n",
+ "1345 1\n",
+ "764 1\n",
+ "368 1\n",
+ "292 1\n",
+ "843 1\n",
+ "Name: count, Length: 1054, dtype: int64"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.Expenses.value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "abe9573c-2085-4164-a702-8b253f4a9a7c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 8))\n",
+ "\n",
+ "# Generate the histogram\n",
+ "sns.histplot(data[\"Expenses\"], color='turquoise', ax=ax1)\n",
+ "\n",
+ "# Generate the box plot\n",
+ "sns.boxplot(data[\"Expenses\"], color='turquoise', ax=ax2)\n",
+ "\n",
+ "# Display the figure\n",
+ "plt.show()\n",
+ "\n",
+ "# Save the figure\n",
+ "fig.savefig(\"Documents/Customer Personality Analysis/Images/figure1.png\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "0e77a140-a226-49b4-8072-f8b587dfffb8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data['TotalAcceptedCmp'] = data['AcceptedCmp1'] + data['AcceptedCmp2'] + data['AcceptedCmp3'] + data['AcceptedCmp4'] + data['AcceptedCmp5']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "727c759f-d084-425b-9d46-38899934a782",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([25, 6, 21, 8, 19, 22, 10, 2, 4, 16, 15, 5, 26, 9, 13, 12, 43,\n",
+ " 17, 20, 14, 27, 11, 18, 28, 7, 24, 29, 23, 32, 30, 37, 31, 33, 35,\n",
+ " 39, 1, 34, 0, 44])"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data['NumTotalPurchases'] = data['NumWebPurchases'] + data['NumCatalogPurchases'] + data['NumStorePurchases'] + data['NumDealsPurchases']\n",
+ "data['NumTotalPurchases'].unique()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "fb9a6729-eb98-40bd-a5cf-cc6dce67ea5d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Year_Birth | \n",
+ " Education | \n",
+ " Marital_Status | \n",
+ " Income | \n",
+ " Dt_Customer | \n",
+ " Recency | \n",
+ " Complain | \n",
+ " Response | \n",
+ " Kids | \n",
+ " Expenses | \n",
+ " TotalAcceptedCmp | \n",
+ " NumTotalPurchases | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
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\n",
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\n",
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\n",
+ " \n",
+ " 1410 | \n",
+ " 1977 | \n",
+ " Post-Graduate | \n",
+ " In-Relationship | \n",
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\n",
+ " \n",
+ " 1668 | \n",
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+ "
\n",
+ " \n",
+ " 326 | \n",
+ " 1976 | \n",
+ " Post-Graduate | \n",
+ " Single | \n",
+ " 14849.0 | \n",
+ " 02-07-2013 | \n",
+ " 39 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 65 | \n",
+ " 0 | \n",
+ " 9 | \n",
+ "
\n",
+ " \n",
+ " 299 | \n",
+ " 1965 | \n",
+ " Post-Graduate | \n",
+ " Single | \n",
+ " 57912.0 | \n",
+ " 17-03-2014 | \n",
+ " 34 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 916 | \n",
+ " 1 | \n",
+ " 28 | \n",
+ "
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+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " Year_Birth Education Marital_Status Income Dt_Customer \\\n",
+ "644 1947 Post-Graduate In-Relationship 77632.0 10-03-2013 \n",
+ "1764 1986 Post-Graduate In-Relationship 21955.0 07-09-2013 \n",
+ "2007 1964 Post-Graduate Single 41638.0 13-02-2013 \n",
+ "2142 1968 Post-Graduate Single 36997.0 01-02-2013 \n",
+ "1054 1983 Post-Graduate In-Relationship 27100.0 05-04-2013 \n",
+ "1410 1977 Post-Graduate In-Relationship 69389.0 28-09-2012 \n",
+ "1668 1970 Post-Graduate In-Relationship 69084.0 19-11-2013 \n",
+ "1373 1960 Post-Graduate Single 50611.0 04-10-2012 \n",
+ "326 1976 Post-Graduate Single 14849.0 02-07-2013 \n",
+ "299 1965 Post-Graduate Single 57912.0 17-03-2014 \n",
+ "\n",
+ " Recency Complain Response Kids Expenses TotalAcceptedCmp \\\n",
+ "644 73 0 1 0 2252 2 \n",
+ "1764 81 0 0 1 55 0 \n",
+ "2007 68 0 0 1 441 1 \n",
+ "2142 72 0 0 2 94 0 \n",
+ "1054 64 0 0 1 37 0 \n",
+ "1410 17 0 0 1 797 0 \n",
+ "1668 43 0 0 1 1586 0 \n",
+ "1373 98 0 0 1 493 1 \n",
+ "326 39 0 0 1 65 0 \n",
+ "299 34 0 0 1 916 1 \n",
+ "\n",
+ " NumTotalPurchases \n",
+ "644 13 \n",
+ "1764 8 \n",
+ "2007 17 \n",
+ "2142 12 \n",
+ "1054 5 \n",
+ "1410 22 \n",
+ "1668 25 \n",
+ "1373 22 \n",
+ "326 9 \n",
+ "299 28 "
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "col_del = [\"AcceptedCmp1\" , \"AcceptedCmp2\", \"AcceptedCmp3\" , \"AcceptedCmp4\",\"AcceptedCmp5\",\"NumWebVisitsMonth\", \"NumWebPurchases\",\"NumCatalogPurchases\",\"NumStorePurchases\",\"NumDealsPurchases\" , \"Kidhome\", \"Teenhome\",\"MntWines\", \"MntFruits\", \"MntMeatProducts\", \"MntFishProducts\", \"MntSweetProducts\", \"MntGoldProds\"]\n",
+ "data=data.drop(columns=col_del,axis=1)\n",
+ "data.sample(10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "249cca73-e817-44fd-badc-b0de73e58f39",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data.drop(\"Dt_Customer\", axis=1, inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "ac11ee1f-1f1b-481a-84b6-d36d63bf2171",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data[\"Age\"] = pd.Timestamp('now').year - data[\"Year_Birth\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "683fe245-9cde-42f9-b2e2-3d84602a6cdc",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " NumTotalPurchases | \n",
+ " Age | \n",
+ "
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+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 1957 | \n",
+ " Post-Graduate | \n",
+ " Single | \n",
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+ " \n",
+ " 2 | \n",
+ " 1965 | \n",
+ " Post-Graduate | \n",
+ " In-Relationship | \n",
+ " 71613.0 | \n",
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+ " 776 | \n",
+ " 0 | \n",
+ " 21 | \n",
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+ " \n",
+ " 3 | \n",
+ " 1984 | \n",
+ " Post-Graduate | \n",
+ " In-Relationship | \n",
+ " 26646.0 | \n",
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+ " 0 | \n",
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+ " \n",
+ " 4 | \n",
+ " 1981 | \n",
+ " Post-Graduate | \n",
+ " In-Relationship | \n",
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+ ],
+ "text/plain": [
+ " Year_Birth Education Marital_Status Income Recency Complain \\\n",
+ "0 1957 Post-Graduate Single 58138.0 58 0 \n",
+ "1 1954 Post-Graduate Single 46344.0 38 0 \n",
+ "2 1965 Post-Graduate In-Relationship 71613.0 26 0 \n",
+ "3 1984 Post-Graduate In-Relationship 26646.0 26 0 \n",
+ "4 1981 Post-Graduate In-Relationship 58293.0 94 0 \n",
+ "\n",
+ " Response Kids Expenses TotalAcceptedCmp NumTotalPurchases Age \n",
+ "0 1 0 1617 0 25 67 \n",
+ "1 0 2 27 0 6 70 \n",
+ "2 0 0 776 0 21 59 \n",
+ "3 0 1 53 0 8 40 \n",
+ "4 0 1 422 0 19 43 "
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "d83d27af-2360-4add-9242-a182daf5909e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data.drop(\"Year_Birth\", axis=1, inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "0544910e-9088-48fe-8ecc-2777b08cf512",
+ "metadata": {},
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+ {
+ "data": {
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+ " Income | \n",
+ " Recency | \n",
+ " Complain | \n",
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+ " Kids | \n",
+ " Expenses | \n",
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+ " 776 | \n",
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+ " 59 | \n",
+ "
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+ " \n",
+ " 3 | \n",
+ " Post-Graduate | \n",
+ " In-Relationship | \n",
+ " 26646.0 | \n",
+ " 26 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 53 | \n",
+ " 0 | \n",
+ " 8 | \n",
+ " 40 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " Post-Graduate | \n",
+ " In-Relationship | \n",
+ " 58293.0 | \n",
+ " 94 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 422 | \n",
+ " 0 | \n",
+ " 19 | \n",
+ " 43 | \n",
+ "
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+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " Education Marital_Status Income Recency Complain Response Kids \\\n",
+ "0 Post-Graduate Single 58138.0 58 0 1 0 \n",
+ "1 Post-Graduate Single 46344.0 38 0 0 2 \n",
+ "2 Post-Graduate In-Relationship 71613.0 26 0 0 0 \n",
+ "3 Post-Graduate In-Relationship 26646.0 26 0 0 1 \n",
+ "4 Post-Graduate In-Relationship 58293.0 94 0 0 1 \n",
+ "\n",
+ " Expenses TotalAcceptedCmp NumTotalPurchases Age \n",
+ "0 1617 0 25 67 \n",
+ "1 27 0 6 70 \n",
+ "2 776 0 21 59 \n",
+ "3 53 0 8 40 \n",
+ "4 422 0 19 43 "
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "id": "d4098c31-e7f7-4a66-bab8-a4370108c14b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "RangeIndex: 2240 entries, 0 to 2239\n",
+ "Data columns (total 11 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 Education 2240 non-null object \n",
+ " 1 Marital_Status 2240 non-null object \n",
+ " 2 Income 2240 non-null float64\n",
+ " 3 Recency 2240 non-null int64 \n",
+ " 4 Complain 2240 non-null int64 \n",
+ " 5 Response 2240 non-null int64 \n",
+ " 6 Kids 2240 non-null int64 \n",
+ " 7 Expenses 2240 non-null int64 \n",
+ " 8 TotalAcceptedCmp 2240 non-null int64 \n",
+ " 9 NumTotalPurchases 2240 non-null int64 \n",
+ " 10 Age 2240 non-null int64 \n",
+ "dtypes: float64(1), int64(8), object(2)\n",
+ "memory usage: 192.6+ KB\n"
+ ]
+ }
+ ],
+ "source": [
+ "data.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "593ff975-76a3-46ef-a328-8970f509312d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Education\n",
+ "Marital_Status\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.preprocessing import LabelEncoder\n",
+ "le = LabelEncoder()\n",
+ "for label, content in data.items():\n",
+ " if pd.api.types.is_string_dtype(content):\n",
+ " print(label)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "f40f9be4-24ec-4d2f-9157-2d770ca90863",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Education\n",
+ "Marital_Status\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.preprocessing import LabelEncoder\n",
+ "le = LabelEncoder()\n",
+ "for label, content in data.items():\n",
+ " if pd.api.types.is_string_dtype(content):\n",
+ " data[label] = le.fit_transform(data[label])\n",
+ " print(label)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "id": "9f5aaedd-e094-41eb-a2bc-9876c17e68cb",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "RangeIndex: 2240 entries, 0 to 2239\n",
+ "Data columns (total 11 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 Education 2240 non-null int64 \n",
+ " 1 Marital_Status 2240 non-null int64 \n",
+ " 2 Income 2240 non-null float64\n",
+ " 3 Recency 2240 non-null int64 \n",
+ " 4 Complain 2240 non-null int64 \n",
+ " 5 Response 2240 non-null int64 \n",
+ " 6 Kids 2240 non-null int64 \n",
+ " 7 Expenses 2240 non-null int64 \n",
+ " 8 TotalAcceptedCmp 2240 non-null int64 \n",
+ " 9 NumTotalPurchases 2240 non-null int64 \n",
+ " 10 Age 2240 non-null int64 \n",
+ "dtypes: float64(1), int64(10)\n",
+ "memory usage: 192.6 KB\n"
+ ]
+ }
+ ],
+ "source": [
+ "data.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "id": "efaebbca-0e7f-4183-b302-ec455fd64d87",
+ "metadata": {},
+ "outputs": [
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+ " Education Marital_Status Income Recency Complain Response Kids \\\n",
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+ "3 0 0 26646.0 26 0 0 1 \n",
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+ "\n",
+ " Expenses TotalAcceptedCmp NumTotalPurchases Age \n",
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+ "3 53 0 8 40 \n",
+ "4 422 0 19 43 "
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "id": "06688ff5-e2b3-408c-a6c7-bc42cb231297",
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+ "17 0\n",
+ "18 0\n",
+ "19 0\n",
+ "20 0\n",
+ "21 0\n",
+ "22 0\n",
+ "23 0\n",
+ "24 0\n",
+ "25 0\n",
+ "26 0\n",
+ "27 0\n",
+ "28 0\n",
+ "29 0\n",
+ "Name: Education, dtype: int64"
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data[\"Education\"].head(30)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "id": "098ac69e-ac4d-49b0-92d1-082895fb712e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 1\n",
+ "1 1\n",
+ "2 0\n",
+ "3 0\n",
+ "4 0\n",
+ "5 0\n",
+ "6 1\n",
+ "7 0\n",
+ "8 0\n",
+ "9 0\n",
+ "10 0\n",
+ "11 0\n",
+ "12 1\n",
+ "13 1\n",
+ "14 0\n",
+ "15 1\n",
+ "16 0\n",
+ "17 0\n",
+ "18 0\n",
+ "19 1\n",
+ "Name: Marital_Status, dtype: int64"
+ ]
+ },
+ "execution_count": 45,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.Marital_Status.head(20)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "id": "2792a709-6ae6-45d5-87ca-e26fecd8196a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.preprocessing import StandardScaler\n",
+ "scaler = StandardScaler()\n",
+ "scaled_feat = scaler.fit_transform(data.values)\n",
+ "scaled_data = pd.DataFrame(scaled_feat, data.index, columns=data.columns)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "id": "11d48a5d-a0b4-4ac2-9251-e06244e93fb1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(2240, 11)"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.values.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "id": "7653649f-47e3-4e70-afaa-409697dc48cf",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Education | \n",
+ " Marital_Status | \n",
+ " Income | \n",
+ " Recency | \n",
+ " Complain | \n",
+ " Response | \n",
+ " Kids | \n",
+ " Expenses | \n",
+ " TotalAcceptedCmp | \n",
+ " NumTotalPurchases | \n",
+ " Age | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " -0.157171 | \n",
+ " 1.346874 | \n",
+ " 0.235696 | \n",
+ " 0.307039 | \n",
+ " -0.097282 | \n",
+ " 2.388846 | \n",
+ " -1.264505 | \n",
+ " 1.679417 | \n",
+ " -0.439037 | \n",
+ " 1.320826 | \n",
+ " 0.985345 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " -0.157171 | \n",
+ " 1.346874 | \n",
+ " -0.235454 | \n",
+ " -0.383664 | \n",
+ " -0.097282 | \n",
+ " -0.418612 | \n",
+ " 1.396361 | \n",
+ " -0.961275 | \n",
+ " -0.439037 | \n",
+ " -1.154596 | \n",
+ " 1.235733 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " -0.157171 | \n",
+ " -0.742460 | \n",
+ " 0.773999 | \n",
+ " -0.798086 | \n",
+ " -0.097282 | \n",
+ " -0.418612 | \n",
+ " -1.264505 | \n",
+ " 0.282673 | \n",
+ " -0.439037 | \n",
+ " 0.799685 | \n",
+ " 0.317643 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " -0.157171 | \n",
+ " -0.742460 | \n",
+ " -1.022355 | \n",
+ " -0.798086 | \n",
+ " -0.097282 | \n",
+ " -0.418612 | \n",
+ " 0.065928 | \n",
+ " -0.918094 | \n",
+ " -0.439037 | \n",
+ " -0.894025 | \n",
+ " -1.268149 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " -0.157171 | \n",
+ " -0.742460 | \n",
+ " 0.241888 | \n",
+ " 1.550305 | \n",
+ " -0.097282 | \n",
+ " -0.418612 | \n",
+ " 0.065928 | \n",
+ " -0.305254 | \n",
+ " -0.439037 | \n",
+ " 0.539114 | \n",
+ " -1.017761 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Education Marital_Status Income Recency Complain Response \\\n",
+ "0 -0.157171 1.346874 0.235696 0.307039 -0.097282 2.388846 \n",
+ "1 -0.157171 1.346874 -0.235454 -0.383664 -0.097282 -0.418612 \n",
+ "2 -0.157171 -0.742460 0.773999 -0.798086 -0.097282 -0.418612 \n",
+ "3 -0.157171 -0.742460 -1.022355 -0.798086 -0.097282 -0.418612 \n",
+ "4 -0.157171 -0.742460 0.241888 1.550305 -0.097282 -0.418612 \n",
+ "\n",
+ " Kids Expenses TotalAcceptedCmp NumTotalPurchases Age \n",
+ "0 -1.264505 1.679417 -0.439037 1.320826 0.985345 \n",
+ "1 1.396361 -0.961275 -0.439037 -1.154596 1.235733 \n",
+ "2 -1.264505 0.282673 -0.439037 0.799685 0.317643 \n",
+ "3 0.065928 -0.918094 -0.439037 -0.894025 -1.268149 \n",
+ "4 0.065928 -0.305254 -0.439037 0.539114 -1.017761 "
+ ]
+ },
+ "execution_count": 41,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "scaled_data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "id": "a263a18f-e8cc-40b5-9588-ede3d115abf1",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+ " super()._check_params_vs_input(X, default_n_init=10)\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.cluster import KMeans\n",
+ "kmeans = KMeans(n_clusters=5, random_state=42)\n",
+ "kmeans.fit(scaled_data)\n",
+ "y_pred = kmeans.predict(scaled_data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "id": "c830f57f-d624-4a94-8122-ebee64bd85c5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def evaluation(y_true, y_pred):\n",
+ " metrics = {\"V Score\": v_measure_score(y_true, y_pred),\n",
+ " \"Completeness Score\": completeness_score(y_true, y_pred)\n",
+ " }\n",
+ " return metrics"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "id": "7faff048-79ad-45b4-8761-86fbbd62f58c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "