RiverBorn_Solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team RiverBorn Solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Induwara\\AppData\\Local\\Temp\\ipykernel_10148\\3915550238.py:1: DtypeWarning: Columns (1,2) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  train_data = pd.read_csv(\"customer_dataset.csv\")\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>customer_id</th>\n",
       "      <th>average_monthly_spend</th>\n",
       "      <th>average_monthly_visit_frequency</th>\n",
       "      <th>average_monthly_basket_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>790</td>\n",
       "      <td>1.11</td>\n",
       "      <td>2.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>176875</td>\n",
       "      <td>15.74</td>\n",
       "      <td>33.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6812</td>\n",
       "      <td>2.11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>38542</td>\n",
       "      <td>7.82</td>\n",
       "      <td>10.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>48712</td>\n",
       "      <td>7.51</td>\n",
       "      <td>10.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id average_monthly_spend average_monthly_visit_frequency  \\\n",
       "0            1                   790                            1.11   \n",
       "1            2                176875                           15.74   \n",
       "2            3                  6812                            2.11   \n",
       "3            4                 38542                            7.82   \n",
       "4            5                 48712                            7.51   \n",
       "\n",
       "   average_monthly_basket_size  \n",
       "0                         2.84  \n",
       "1                        33.83  \n",
       "2                          NaN  \n",
       "3                        10.73  \n",
       "4                        10.04  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = pd.read_csv(\"customer_dataset.csv\")\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set customer ID as Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "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>average_monthly_spend</th>\n",
       "      <th>average_monthly_visit_frequency</th>\n",
       "      <th>average_monthly_basket_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>790</td>\n",
       "      <td>1.11</td>\n",
       "      <td>2.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176875</td>\n",
       "      <td>15.74</td>\n",
       "      <td>33.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6812</td>\n",
       "      <td>2.11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38542</td>\n",
       "      <td>7.82</td>\n",
       "      <td>10.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>48712</td>\n",
       "      <td>7.51</td>\n",
       "      <td>10.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  average_monthly_spend average_monthly_visit_frequency  \\\n",
       "1                   790                            1.11   \n",
       "2                176875                           15.74   \n",
       "3                  6812                            2.11   \n",
       "4                 38542                            7.82   \n",
       "5                 48712                            7.51   \n",
       "\n",
       "   average_monthly_basket_size  \n",
       "1                         2.84  \n",
       "2                        33.83  \n",
       "3                          NaN  \n",
       "4                        10.73  \n",
       "5                        10.04  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.set_index('customer_id',inplace=True)\n",
    "train_data.index.name = None\n",
    "\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ensure that the data types of each column are appropriate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "average_monthly_spend               object\n",
       "average_monthly_visit_frequency     object\n",
       "average_monthly_basket_size        float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "average_monthly_spend              float64\n",
       "average_monthly_visit_frequency    float64\n",
       "average_monthly_basket_size        float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['average_monthly_spend'] = pd.to_numeric(train_data['average_monthly_spend'], errors='coerce')\n",
    "train_data['average_monthly_visit_frequency'] = pd.to_numeric(train_data['average_monthly_visit_frequency'], errors='coerce')\n",
    "\n",
    "train_data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dealing with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "average_monthly_spend              9387\n",
       "average_monthly_visit_frequency    9333\n",
       "average_monthly_basket_size        9286\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(570000, 3)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replace missing values with mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['average_monthly_spend'].fillna(train_data['average_monthly_spend'].mean(), inplace=True)\n",
    "train_data['average_monthly_visit_frequency'].fillna(train_data['average_monthly_visit_frequency'].mean(), inplace=True)\n",
    "train_data['average_monthly_basket_size'].fillna(train_data['average_monthly_basket_size'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "average_monthly_spend              0\n",
       "average_monthly_visit_frequency    0\n",
       "average_monthly_basket_size        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dealing with Duplicate Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(559849, 3)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.drop_duplicates(inplace=True)\n",
    "\n",
    "train_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize distribution and outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='average_monthly_spend'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2) = plt.subplots(2,1)\n",
    "sns.histplot(train_data['average_monthly_spend'],ax=ax1)\n",
    "sns.boxplot(x=train_data['average_monthly_spend'],ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='average_monthly_visit_frequency'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2) = plt.subplots(2,1)\n",
    "sns.histplot(train_data['average_monthly_visit_frequency'],ax=ax1)\n",
    "sns.boxplot(x=train_data['average_monthly_visit_frequency'],ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='average_monthly_basket_size'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2) = plt.subplots(2,1)\n",
    "sns.histplot(train_data['average_monthly_basket_size'],ax=ax1)\n",
    "sns.boxplot(x=train_data['average_monthly_basket_size'],ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dealing with Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1 = train_data.quantile(0.25)\n",
    "Q3 = train_data.quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# Define the bounds for outliers\n",
    "lower_bound = Q1 - 1.5 * IQR\n",
    "upper_bound = Q3 + 1.5 * IQR\n",
    "\n",
    "# Identify outliers\n",
    "outliers = ((train_data < lower_bound) | (train_data > upper_bound)).any(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_cleaned = train_data[~outliers]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize cleaned data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='average_monthly_spend'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2) = plt.subplots(2,1)\n",
    "sns.histplot(train_cleaned['average_monthly_spend'],ax=ax1)\n",
    "sns.boxplot(x=train_cleaned['average_monthly_spend'],ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='average_monthly_visit_frequency'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2) = plt.subplots(2,1)\n",
    "sns.histplot(train_cleaned['average_monthly_visit_frequency'],ax=ax1)\n",
    "sns.boxplot(x=train_cleaned['average_monthly_visit_frequency'],ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='average_monthly_basket_size'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2) = plt.subplots(2,1)\n",
    "sns.histplot(train_cleaned['average_monthly_basket_size'],ax=ax1)\n",
    "sns.boxplot(x=train_cleaned['average_monthly_basket_size'],ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Transformation and Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.93560826, -1.03294832, -0.85122026],\n",
       "       [ 2.81200764,  1.71041821,  2.21403665],\n",
       "       [-0.80744207, -0.84543181,  0.18296307],\n",
       "       ...,\n",
       "       [-0.64952226, -0.68416761, -0.65735438],\n",
       "       [-0.70851872, -0.87168412, -0.82550335],\n",
       "       [-0.86250117,  0.04874606, -0.94221853]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transform = preprocessing.StandardScaler()\n",
    "train_scaled = transform.fit_transform(train_cleaned[['average_monthly_spend', 'average_monthly_visit_frequency', 'average_monthly_basket_size']])\n",
    "\n",
    "train_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clustering using KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=5, n_init=10, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=5, n_init=10, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=5, n_init=10, random_state=42)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=5, init='k-means++',random_state=42,n_init=10)\n",
    "kmeans.fit(train_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Induwara\\AppData\\Local\\Temp\\ipykernel_10148\\4272999807.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_cleaned['Cluster'] = kmeans.labels_\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>average_monthly_spend</th>\n",
       "      <th>average_monthly_visit_frequency</th>\n",
       "      <th>average_monthly_basket_size</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>790.0</td>\n",
       "      <td>1.11</td>\n",
       "      <td>2.840000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176875.0</td>\n",
       "      <td>15.74</td>\n",
       "      <td>33.830000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6812.0</td>\n",
       "      <td>2.11</td>\n",
       "      <td>13.295679</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38542.0</td>\n",
       "      <td>7.82</td>\n",
       "      <td>10.730000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>48712.0</td>\n",
       "      <td>7.51</td>\n",
       "      <td>10.040000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   average_monthly_spend  average_monthly_visit_frequency  \\\n",
       "1                  790.0                             1.11   \n",
       "2               176875.0                            15.74   \n",
       "3                 6812.0                             2.11   \n",
       "4                38542.0                             7.82   \n",
       "5                48712.0                             7.51   \n",
       "\n",
       "   average_monthly_basket_size  Cluster  \n",
       "1                     2.840000        3  \n",
       "2                    33.830000        1  \n",
       "3                    13.295679        0  \n",
       "4                    10.730000        0  \n",
       "5                    10.040000        0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_cleaned['Cluster'] = kmeans.labels_\n",
    "train_cleaned.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cluster\n",
       "0    204676\n",
       "3    196653\n",
       "1     48431\n",
       "2     40354\n",
       "4     31626\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_cleaned['Cluster'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cluster Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize cluster characteristics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize=(18, 6))\n",
    "\n",
    "sns.barplot(data=train_cleaned, x='Cluster', y='average_monthly_spend', ax=axs[0])\n",
    "axs[0].set_title('Monthly Spend')\n",
    "\n",
    "sns.barplot(data=train_cleaned, x='Cluster', y='average_monthly_visit_frequency', ax=axs[1])\n",
    "axs[1].set_title('Monthly Visit')\n",
    "\n",
    "sns.barplot(data=train_cleaned, x='Cluster', y='average_monthly_basket_size', ax=axs[2])\n",
    "axs[2].set_title('Monthly Basket Size')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " |          |  monthly spend  |     monthly visit  |       monthly basket size   |        category\n",
    " | --- | --- | --- | --- | --- |\n",
    " |Cluster 0 | average         |   average          |     average                 |      Medium\n",
    " |Cluster 1 | very high       |    high            |      high                   |       Premium\n",
    " |Cluster 2 | high            |    very high       |      average                |       Frequent\n",
    " |Cluster 3 | low             |    low             |      low                    |       Low\n",
    " |Cluster 4 | high            |    average         |      very high              |       Loyal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assigning Categories to Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cluster\n",
       "Medium      204676\n",
       "Low         196653\n",
       "Premium      48431\n",
       "Frequent     40354\n",
       "Loyal        31626\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clust_dict = {0:'Medium',1:'Premium',2:'Frequent',3:'Low',4:'Loyal'}\n",
    "\n",
    "train_cleaned.loc[:,'Cluster'] = train_cleaned['Cluster'].map(clust_dict)\n",
    "\n",
    "train_cleaned['Cluster'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intervention Strategies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_rules(cluster):\n",
    "    cluster_data = train_cleaned[train_cleaned['Cluster'] == cluster]\n",
    "    \n",
    "    column_list = ['average_monthly_spend','average_monthly_visit_frequency','average_monthly_basket_size']\n",
    "    column_names = ['spend','visit frequency','basket size']\n",
    "\n",
    "    print(cluster + \" customers\")\n",
    "    for column, cname in zip(column_list, column_names):\n",
    "        cluster_avg_value = cluster_data[column].mean()\n",
    "        percentile_rank_cluster = stats.percentileofscore(train_cleaned[column], cluster_avg_value)\n",
    "        \n",
    "        \n",
    "        lower_percentile_cluster = int(percentile_rank_cluster // 5) * 5\n",
    "        upper_percentile_cluster = lower_percentile_cluster + 5\n",
    "        \n",
    "        print(\"Monthly {} between {} - {}th percentile.\".format(cname, lower_percentile_cluster, upper_percentile_cluster))\n",
    "    print(\" \")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Premium customers\n",
      "Monthly spend between 95 - 100th percentile.\n",
      "Monthly visit frequency between 85 - 90th percentile.\n",
      "Monthly basket size between 85 - 90th percentile.\n",
      " \n",
      "Frequent customers\n",
      "Monthly spend between 80 - 85th percentile.\n",
      "Monthly visit frequency between 95 - 100th percentile.\n",
      "Monthly basket size between 80 - 85th percentile.\n",
      " \n",
      "Loyal customers\n",
      "Monthly spend between 80 - 85th percentile.\n",
      "Monthly visit frequency between 80 - 85th percentile.\n",
      "Monthly basket size between 95 - 100th percentile.\n",
      " \n"
     ]
    }
   ],
   "source": [
    "clusters = ['Premium','Frequent','Loyal']\n",
    "for cluster in clusters:\n",
    "    generate_rules(cluster)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Customer Segment: Premium\n",
    "- **Assumptions:** Premium customers spend a lot each month, so they probably earn a good income and aren't too worried about higher prices. They come to the store often and buy a lot each time.\n",
    "- **Promotion Goals:** We want to make these customers even more loyal to our brand by showing them why our expensive, high-quality products are worth it.\n",
    "- **Customer preferences:** Premium customers like feeling special and getting things that are unique or personalized. They want the best quality.\n",
    "\n",
    "- **Strategy:** Offer exclusive deals on our top-tier products, like limited edition items or personalized services. Focus on making them feel valued and appreciated for choosing our brand.\n",
    "\n",
    "### Customer Segment: Frequent\n",
    "- **Assumptions:** These customers come to the store a lot, so they probably live nearby and find it easy to get here. They don't spend as much as premium customers, but they still buy quite a bit.\n",
    "- **Promotion Goals:** We want to encourage these customers to spend more money when they come in, even if it's just a little extra each time.\n",
    "- **Customer preferences:** Since they're here often, they probably like buying everyday things they need regularly.\n",
    "\n",
    "- **Strategy:** Give them special deals or rewards for spending a bit more each visit, like discounts on larger quantities or bundled items. Make it convenient for them to get what they need quickly and easily.\n",
    "\n",
    "### Customer Segment: Loyal\n",
    "- **Assumptions:** Loyal customers don't come in as often as the others, but when they do, they buy a lot. They really like our brand and stick with us.\n",
    "- **Promotion Goals:** We want these customers to come in more often, so we need to give them extra reasons to visit us regularly.\n",
    "- **Customer preferences:** They want good deals and lots of products for their money.\n",
    "\n",
    "- **Strategy:** Offer them special discounts or promotions that change regularly, so they feel like they're getting a good deal every time they visit. Show them why it's worth coming back to us again and again.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}