{ "cells": [ { "cell_type": "markdown", "id": "1da8b92c", "metadata": {}, "source": [ "# Performing Psychographic Segmentation Based on Customer Behaviour" ] }, { "cell_type": "markdown", "id": "900f1f40", "metadata": { "papermill": { "duration": 0.021263, "end_time": "2022-08-28T13:50:04.131480", "exception": false, "start_time": "2022-08-28T13:50:04.110217", "status": "completed" }, "tags": [] }, "source": [ "\n" ] }, { "cell_type": "markdown", "id": "2f870b78", "metadata": { "papermill": { "duration": 0.021887, "end_time": "2022-08-28T13:50:04.174852", "exception": false, "start_time": "2022-08-28T13:50:04.152965", "status": "completed" }, "tags": [] }, "source": [ "## Importing Libraries and Dataset" ] }, { "cell_type": "code", "execution_count": 1, "id": "1fbdee6d", "metadata": { "execution": { "iopub.execute_input": "2022-08-28T13:50:04.222093Z", "iopub.status.busy": "2022-08-28T13:50:04.220859Z", "iopub.status.idle": "2022-08-28T13:50:07.209795Z", "shell.execute_reply": "2022-08-28T13:50:07.208214Z" }, "papermill": { "duration": 3.016161, "end_time": "2022-08-28T13:50:07.212927", "exception": false, "start_time": "2022-08-28T13:50:04.196766", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd \n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import plotly.express as px\n", "import sklearn\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.metrics import silhouette_score\n", "from sklearn.metrics import silhouette_samples\n", "from scipy.cluster.hierarchy import dendrogram\n", "from sklearn.decomposition import PCA\n", "from sklearn.cluster import KMeans, AgglomerativeClustering, SpectralClustering, DBSCAN\n", "from datetime import datetime\n", "import warnings\n", "import sys\n", "import os" ] }, { "cell_type": "code", "execution_count": 2, "id": "db39198d", "metadata": { "execution": { "iopub.execute_input": "2022-08-28T13:50:07.260113Z", "iopub.status.busy": "2022-08-28T13:50:07.259192Z", "iopub.status.idle": "2022-08-28T13:50:07.297684Z", "shell.execute_reply": "2022-08-28T13:50:07.296601Z" }, "papermill": { "duration": 0.065015, "end_time": "2022-08-28T13:50:07.300452", "exception": false, "start_time": "2022-08-28T13:50:07.235437", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "df = pd.read_csv('marketing_campaign.csv')" ] }, { "cell_type": "code", "execution_count": 3, "id": "d4a47db8", "metadata": { "execution": { "iopub.execute_input": "2022-08-28T13:50:07.347340Z", "iopub.status.busy": "2022-08-28T13:50:07.346437Z", "iopub.status.idle": "2022-08-28T13:50:07.387384Z", "shell.execute_reply": "2022-08-28T13:50:07.385894Z" }, "papermill": { "duration": 0.068972, "end_time": "2022-08-28T13:50:07.391082", "exception": false, "start_time": "2022-08-28T13:50:07.322110", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
\n", " | ID | \n", "Year_Birth | \n", "Education | \n", "Marital_Status | \n", "Income | \n", "Kidhome | \n", "Teenhome | \n", "Dt_Customer | \n", "Recency | \n", "MntWines | \n", "... | \n", "NumWebVisitsMonth | \n", "AcceptedCmp3 | \n", "AcceptedCmp4 | \n", "AcceptedCmp5 | \n", "AcceptedCmp1 | \n", "AcceptedCmp2 | \n", "Complain | \n", "Z_CostContact | \n", "Z_Revenue | \n", "Response | \n", "
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | \n", "5524 | \n", "1957 | \n", "Graduation | \n", "Single | \n", "58138.0 | \n", "0 | \n", "0 | \n", "4/9/2012 | \n", "58 | \n", "635 | \n", "... | \n", "7 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "3 | \n", "11 | \n", "1 | \n", "
1 | \n", "2174 | \n", "1954 | \n", "Graduation | \n", "Single | \n", "46344.0 | \n", "1 | \n", "1 | \n", "8/3/2014 | \n", "38 | \n", "11 | \n", "... | \n", "5 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "3 | \n", "11 | \n", "0 | \n", "
2 | \n", "4141 | \n", "1965 | \n", "Graduation | \n", "Together | \n", "71613.0 | \n", "0 | \n", "0 | \n", "21-08-2013 | \n", "26 | \n", "426 | \n", "... | \n", "4 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "3 | \n", "11 | \n", "0 | \n", "
3 | \n", "6182 | \n", "1984 | \n", "Graduation | \n", "Together | \n", "26646.0 | \n", "1 | \n", "0 | \n", "10/2/2014 | \n", "26 | \n", "11 | \n", "... | \n", "6 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "3 | \n", "11 | \n", "0 | \n", "
4 | \n", "5324 | \n", "1981 | \n", "PhD | \n", "Married | \n", "58293.0 | \n", "1 | \n", "0 | \n", "19-01-2014 | \n", "94 | \n", "173 | \n", "... | \n", "5 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "3 | \n", "11 | \n", "0 | \n", "
... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
95 | \n", "7516 | \n", "1983 | \n", "Graduation | \n", "Married | \n", "30096.0 | \n", "1 | \n", "0 | \n", "22-05-2014 | \n", "30 | \n", "5 | \n", "... | \n", "6 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "3 | \n", "11 | \n", "0 | \n", "
96 | \n", "7247 | \n", "1960 | \n", "Graduation | \n", "Widow | \n", "47916.0 | \n", "0 | \n", "1 | \n", "22-11-2012 | \n", "72 | \n", "505 | \n", "... | \n", "6 | \n", "0 | \n", "1 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "3 | \n", "11 | \n", "0 | \n", "
97 | \n", "11100 | \n", "1972 | \n", "Graduation | \n", "Divorced | \n", "51813.0 | \n", "1 | \n", "1 | \n", "11/4/2013 | \n", "37 | \n", "51 | \n", "... | \n", "7 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "3 | \n", "11 | \n", "0 | \n", "
98 | \n", "4646 | \n", "1951 | \n", "2n Cycle | \n", "Married | \n", "78497.0 | \n", "0 | \n", "0 | \n", "1/12/2013 | \n", "44 | \n", "207 | \n", "... | \n", "2 | \n", "0 | \n", "0 | \n", "0 | \n", "1 | \n", "0 | \n", "0 | \n", "3 | \n", "11 | \n", "0 | \n", "
99 | \n", "3037 | \n", "1983 | \n", "PhD | \n", "Married | \n", "50150.0 | \n", "0 | \n", "0 | \n", "20-06-2013 | \n", "32 | \n", "135 | \n", "... | \n", "5 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "3 | \n", "11 | \n", "0 | \n", "
100 rows × 29 columns
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