{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unsupervised Learning: Clustering: \n",
    "\n",
    "- K-Means Clustering\n",
    "- Agglomerative Custering\n",
    "- DBScan Clustering\n",
    "\n",
    "\n",
    "```Python\n",
    "The Business Problem:\n",
    "\n",
    "A software company wants to analyze customer behavior and identify patterns in their user data to optimize their product offerings and improve customer retention. The company has a dataset containing information about 10,000 customers, including their subscription type, usage metrics (number of logins, session duration, feature usage), customer lifetime, and revenue generated.\n",
    "\n",
    "The business problem focuses on customer segmentation (clustering) for a software product, utilizing clustering techniques like Agglomerative Clustering and K-Means. The segmentation is based on diverse customer attributes, including subscription type, usage metrics (number of logins, session duration, feature usage), customer lifetime, and revenue generated.\n",
    "```\n",
    "\n",
    "#### By clustering customers based on these behavioral and financial factors, the company aims to derive actionable insights that will support more:\n",
    "\n",
    "- `Targeted Marketing:` Enables personalized campaigns for different user segments.\n",
    "- `Pricing Strategies:` Helps refine pricing tiers based on usage and revenue patterns.\n",
    "- `Resource Allocation:` Identifies high-value customers for focused engagement.\n",
    "- `Retention:` Detects low-engagement users for proactive retention efforts.\n",
    "\n",
    "#### <u> Dataset Description: </u>\n",
    "\n",
    "`User ID:` A unique identifier for each user (numeric).\n",
    "\n",
    "`Subscription Type:` The type of subscription a user has, which could be one of the following categories: \n",
    "> - Free: No payment required, limited access to features. \n",
    "> - Basic: Paid subscription with standard access to features. \n",
    "> - Pro: Premium subscription with full access to all features. \n",
    "\n",
    "`Number of Logins:` The total number of times a user has logged into the software product.\n",
    "\n",
    "`Avg Session Duration (mins):` The average duration (in minutes) of each session a user spends in the software product.\n",
    "\n",
    "`Feature Usage Count:` The number of distinct features a user actively uses during their time with the product.\n",
    "\n",
    "`Customer Lifetime (months):` The total duration (in months) the user has been a customer.\n",
    "\n",
    "`Revenue Generated ($):` The total revenue generated by the user for the business, in USD.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment to run on google-colab\n",
    "# %pip install scipy tabulate numpy scikit-learn matplotlib seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder, RobustScaler\n",
    "from sklearn.cluster import KMeans, AgglomerativeClustering, DBSCAN\n",
    "from sklearn.metrics import silhouette_score, silhouette_samples, adjusted_rand_score, calinski_harabasz_score\n",
    "from sklearn.metrics.cluster import contingency_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import NearestNeighbors\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from tabulate import tabulate\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Debrup Banerjee\\AppData\\Local\\Temp\\ipykernel_18744\\1352561546.py:6: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  df.colums = df.columns.str.strip()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>User ID</th>\n",
       "      <th>Subscription Type</th>\n",
       "      <th>Number of Logins</th>\n",
       "      <th>Avg Session Duration (mins)</th>\n",
       "      <th>Feature Usage Count</th>\n",
       "      <th>Customer Lifetime (months)</th>\n",
       "      <th>Revenue Generated ($)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2561</td>\n",
       "      <td>Free</td>\n",
       "      <td>14</td>\n",
       "      <td>22.178507</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>100.471472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2562</td>\n",
       "      <td>Pro</td>\n",
       "      <td>17</td>\n",
       "      <td>31.015401</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>160.723959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2563</td>\n",
       "      <td>Pro</td>\n",
       "      <td>18</td>\n",
       "      <td>32.819273</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>175.226528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2564</td>\n",
       "      <td>Pro</td>\n",
       "      <td>12</td>\n",
       "      <td>28.140540</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>118.400847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2565</td>\n",
       "      <td>Free</td>\n",
       "      <td>6</td>\n",
       "      <td>19.938184</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>57.314870</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User ID Subscription Type  Number of Logins  Avg Session Duration (mins)  \\\n",
       "0     2561              Free                14                    22.178507   \n",
       "1     2562               Pro                17                    31.015401   \n",
       "2     2563               Pro                18                    32.819273   \n",
       "3     2564               Pro                12                    28.140540   \n",
       "4     2565              Free                 6                    19.938184   \n",
       "\n",
       "   Feature Usage Count  Customer Lifetime (months)  Revenue Generated ($)  \n",
       "0                    4                          14             100.471472  \n",
       "1                    7                          16             160.723959  \n",
       "2                    7                          17             175.226528  \n",
       "3                    4                          11             118.400847  \n",
       "4                    3                           6              57.314870  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the necessary libraries and the dataset\n",
    "import pandas as pd\n",
    "\n",
    "# Load the dataset\n",
    "df = pd.read_csv('./datasets/software_product_user_data.csv')  # Update the path to your uploaded dataset\n",
    "df.colums = df.columns.str.strip()\n",
    "# Display the first few rows to verify the dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset Description:\n",
    "\n",
    "`User ID`:  A unique identifier for each user/customer (numeric).\n",
    "\n",
    "`Subscription Type:` The type of subscription a user has, which could be one of the following categories: \n",
    "> - Free: No payment required, limited access to features. \n",
    "> - Basic: Paid subscription with standard access to features. \n",
    "> - Pro: Premium subscription with full access to all features. \n",
    "\n",
    "`Number of Logins:` The total number of times a user has logged into the software product.\n",
    "\n",
    "`Avg Session Duration (mins):` The average duration (in minutes) of each session a user spends in the software product.\n",
    "\n",
    "`Feature Usage Count:` The number of distinct features a user actively uses during their time with the product.\n",
    "\n",
    "`Customer Lifetime (months):` The total duration (in months) the user has been a customer.\n",
    "\n",
    "`Revenue Generated ($):` The total revenue generated by the user for the business, in USD.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number of Logins</th>\n",
       "      <th>Avg Session Duration (mins)</th>\n",
       "      <th>Feature Usage Count</th>\n",
       "      <th>Customer Lifetime (months)</th>\n",
       "      <th>Revenue Generated ($)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5005.000000</td>\n",
       "      <td>5005.000000</td>\n",
       "      <td>5005.000000</td>\n",
       "      <td>5005.000000</td>\n",
       "      <td>5005.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17.692907</td>\n",
       "      <td>35.259296</td>\n",
       "      <td>6.574825</td>\n",
       "      <td>17.160639</td>\n",
       "      <td>176.454685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>24.068252</td>\n",
       "      <td>18.326090</td>\n",
       "      <td>3.651353</td>\n",
       "      <td>8.464895</td>\n",
       "      <td>86.126830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.004903</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>50.161396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>22.430058</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>112.686096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>34.889065</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>174.701908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>47.461554</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>238.849057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>848.000000</td>\n",
       "      <td>499.468365</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>196.000000</td>\n",
       "      <td>1961.691771</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Number of Logins  Avg Session Duration (mins)  Feature Usage Count  \\\n",
       "count       5005.000000                  5005.000000          5005.000000   \n",
       "mean          17.692907                    35.259296             6.574825   \n",
       "std           24.068252                    18.326090             3.651353   \n",
       "min            5.000000                    10.004903             2.000000   \n",
       "25%           11.000000                    22.430058             4.000000   \n",
       "50%           17.000000                    34.889065             6.000000   \n",
       "75%           23.000000                    47.461554             9.000000   \n",
       "max          848.000000                   499.468365            98.000000   \n",
       "\n",
       "       Customer Lifetime (months)  Revenue Generated ($)  \n",
       "count                 5005.000000            5005.000000  \n",
       "mean                    17.160639             176.454685  \n",
       "std                      8.464895              86.126830  \n",
       "min                      5.000000              50.161396  \n",
       "25%                     11.000000             112.686096  \n",
       "50%                     17.000000             174.701908  \n",
       "75%                     23.000000             238.849057  \n",
       "max                    196.000000            1961.691771  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Subscription Type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Subscription Type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Free</th>\n",
       "      <td>1707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pro</th>\n",
       "      <td>1658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Basic</th>\n",
       "      <td>1640</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Subscription Type\n",
       "Subscription Type                   \n",
       "Free                            1707\n",
       "Pro                             1658\n",
       "Basic                           1640"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Check if the 'User ID' column exists, and drop it if present\n",
    "if 'User ID' in df.columns:\n",
    "    df.drop(columns=['User ID'], axis=1, inplace=True)\n",
    "\n",
    "# Generate summary statistics for numerical columns\n",
    "numerical_stats = df.describe()\n",
    "\n",
    "# Generate frequency counts for categorical columns (if any)\n",
    "categorical_columns = df.select_dtypes(include=['object']).columns\n",
    "categorical_stats = {}\n",
    "for column in categorical_columns:\n",
    "    categorical_stats[column] = df[column].value_counts()\n",
    "\n",
    "# Create a DataFrame for categorical stats\n",
    "categorical_stats_df = pd.concat([stats.rename(column) for column, stats in categorical_stats.items()], axis=1)\n",
    "\n",
    "# Display the numerical stats DataFrame\n",
    "display(numerical_stats)\n",
    "\n",
    "# Display the categorical stats DataFrame\n",
    "display(categorical_stats_df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```Python\n",
    "Overall Summary:\n",
    "The dataset reflects a wide variation in user engagement, with most users showing moderate levels of logins, session duration, feature usage, and revenue generation. However, there are some outliers who exhibit much higher engagement and spending patterns.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### <u> Formulae for Scalers</u>:\n",
    "\n",
    "## 1. StandardScaler\n",
    "\n",
    "The formula for the **StandardScaler** is as follows:\n",
    "\n",
    "```math\n",
    "z = \\frac{x - \\mu}{\\sigma}\n",
    "```\n",
    "Where:\n",
    " - \\( x \\) is the original data point. \n",
    " - \\( \\mu \\) is the mean of the data.\n",
    " - \\( \\sigma \\) is the standard deviation of the data.\n",
    "\n",
    "## 2. RobustScaler\n",
    "\n",
    "The formula for the **RobustScaler** is as follows:\n",
    "```math\n",
    "z = \\frac{x - Q_2}{Q_3 - Q_1}\n",
    "```\n",
    "Where:\n",
    "- \\( x \\) is the original data point.\n",
    "- \\( Q_2 \\) is the median of the data (also known as the 50th percentile).\n",
    "- \\( Q_1 \\) is the first quartile (25th percentile).\n",
    "- \\( Q_3 \\) is the third quartile (75th percentile).\n",
    "- The difference \\( Q_3 - Q_1 \\) is known as the **interquartile range (IQR)**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Original_Sample_Feature</th>\n",
       "      <th>Sample_Feature_Robust</th>\n",
       "      <th>Sample_Feature_Standard</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>-0.888889</td>\n",
       "      <td>-0.624002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.777778</td>\n",
       "      <td>-0.597335</td>\n",
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       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.410668</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>-0.357335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Original_Sample_Feature  Sample_Feature_Robust  Sample_Feature_Standard\n",
       "0                        2              -0.888889                -0.624002\n",
       "1                        3              -0.777778                -0.597335\n",
       "2                       10               0.000000                -0.410668\n",
       "3                       12               0.222222                -0.357335\n",
       "4                      100              10.000000                 1.989340"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import RobustScaler, StandardScaler\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Sample data (including outliers)\n",
    "data = {\n",
    "         'Sample_Feature': [2, 3, 10, 12, 100],  # Notice the outlier (10000)\n",
    "}\n",
    "\n",
    "# Create a DataFrame\n",
    "df1 = pd.DataFrame(data)\n",
    "\n",
    "# Initialize RobustScaler and StandardScaler\n",
    "scaler_robust = RobustScaler()\n",
    "scaler_standard = StandardScaler()\n",
    "\n",
    "# Apply the RobustScaler to the numerical columns\n",
    "scaled_data_robust = scaler_robust.fit_transform(df1)\n",
    "# Apply the StandardScaler to the numerical columns\n",
    "scaled_data_std = scaler_standard.fit_transform(df1)\n",
    "\n",
    "# Create separate DataFrames for the scaled data\n",
    "scaled_df_robust = pd.DataFrame(scaled_data_robust, columns=df1.columns)\n",
    "scaled_df_std = pd.DataFrame(scaled_data_std, columns=df1.columns)\n",
    "\n",
    "# Concatenate original, robust-scaled, and standard-scaled DataFrames side by side\n",
    "final_df = pd.concat([df1.add_prefix('Original_'), \n",
    "                      scaled_df_robust.add_suffix('_Robust'), \n",
    "                      scaled_df_std.add_suffix('_Standard')], axis=1)\n",
    "\n",
    "# Display the final DataFrame\n",
    "final_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```Python\n",
    "Key Points:\n",
    "RobustScaler:\n",
    "-------------\n",
    "As seen in the 'Sample_Feature_Robust' column, the non-outlier values (2, 3, 10, 12) are scaled to a reasonable range between -0.89 and 0.22, whereas the outlier (100) is assigned a value of 10.0. The other values remain spread out and less compressed.\n",
    "\n",
    "StandardScaler:\n",
    "---------------\n",
    "In the 'Sample_Feature_Standard' column, all values are scaled closer to each other. The non-outlier values (2, 3, 10, 12) are squashed into a narrow range between -0.62 and -0.36, while the outlier (100) gets scaled to a higher value (1.99).\n",
    "\n",
    "RobustScaler maintains the relative spread of non-outlier values while still handling outliers, making it more suitable when your data contains outliers.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        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>Number of Logins_Robust</th>\n",
       "      <th>Avg Session Duration (mins)_Robust</th>\n",
       "      <th>Feature Usage Count_Robust</th>\n",
       "      <th>Customer Lifetime (months)_Robust</th>\n",
       "      <th>Revenue Generated ($)_Robust</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.250000</td>\n",
       "      <td>-0.507783</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.250000</td>\n",
       "      <td>-0.588369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.154752</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.083333</td>\n",
       "      <td>-0.110793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.083333</td>\n",
       "      <td>-0.082688</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.416667</td>\n",
       "      <td>-0.269601</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-0.446257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.916667</td>\n",
       "      <td>-0.597283</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>-0.916667</td>\n",
       "      <td>-0.930440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Number of Logins_Robust  Avg Session Duration (mins)_Robust  \\\n",
       "0                -0.250000                           -0.507783   \n",
       "1                 0.000000                           -0.154752   \n",
       "2                 0.083333                           -0.082688   \n",
       "3                -0.416667                           -0.269601   \n",
       "4                -0.916667                           -0.597283   \n",
       "\n",
       "   Feature Usage Count_Robust  Customer Lifetime (months)_Robust  \\\n",
       "0                        -0.4                          -0.250000   \n",
       "1                         0.2                          -0.083333   \n",
       "2                         0.2                           0.000000   \n",
       "3                        -0.4                          -0.500000   \n",
       "4                        -0.6                          -0.916667   \n",
       "\n",
       "   Revenue Generated ($)_Robust  \n",
       "0                     -0.588369  \n",
       "1                     -0.110793  \n",
       "2                      0.004158  \n",
       "3                     -0.446257  \n",
       "4                     -0.930440  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply Robust and Standard Scaling...\n",
    "# Identify the numerical columns to scale\n",
    "numerical_features = ['Number of Logins', 'Avg Session Duration (mins)', \n",
    "                      'Feature Usage Count', 'Customer Lifetime (months)', \n",
    "                      'Revenue Generated ($)']\n",
    "\n",
    "# Initialize the RobustScaler and StandardScaler\n",
    "robust_scaler = RobustScaler()\n",
    "standard_scaler = StandardScaler()\n",
    "\n",
    "# Apply the RobustScaler and StandardScaler to the numerical columns\n",
    "df_scaled_robust = pd.DataFrame(robust_scaler.fit_transform(df[numerical_features]), columns=numerical_features)\n",
    "df_scaled_standard = pd.DataFrame(standard_scaler.fit_transform(df[numerical_features]), columns=numerical_features)\n",
    "\n",
    "# Add suffixes to distinguish scaled columns\n",
    "df_scaled_robust = df_scaled_robust.add_suffix('_Robust')\n",
    "df_scaled_standard = df_scaled_standard.add_suffix('_Standard')\n",
    "\n",
    "# Concatenate original, robust-scaled, and standard-scaled DataFrames side by side\n",
    "df_combined = pd.concat([df, df_scaled_robust, df_scaled_standard], axis=1)\n",
    "df_scaled_robust.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Original Revenue Generated</th>\n",
       "      <th>Robust Scaled Revenue Generated</th>\n",
       "      <th>Standard Scaled Revenue Generated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>176.454685</td>\n",
       "      <td>0.013893</td>\n",
       "      <td>1.675206e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>86.126830</td>\n",
       "      <td>0.682663</td>\n",
       "      <td>1.000100e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>174.701908</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.035315e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mode</th>\n",
       "      <td>50.161396</td>\n",
       "      <td>-0.987140</td>\n",
       "      <td>-1.466511e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1961.691771</td>\n",
       "      <td>14.164140</td>\n",
       "      <td>2.073007e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>50.161396</td>\n",
       "      <td>-0.987140</td>\n",
       "      <td>-1.466511e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IQR</th>\n",
       "      <td>126.162962</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.464997e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q3+(1.5*IQR)</th>\n",
       "      <td>428.093499</td>\n",
       "      <td>2.008447</td>\n",
       "      <td>2.922016e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q1-(1.5*IQR)</th>\n",
       "      <td>-76.558347</td>\n",
       "      <td>-1.991553</td>\n",
       "      <td>-2.937973e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Original Revenue Generated  Robust Scaled Revenue Generated  \\\n",
       "mean                          176.454685                         0.013893   \n",
       "std                            86.126830                         0.682663   \n",
       "median                        174.701908                         0.000000   \n",
       "mode                           50.161396                        -0.987140   \n",
       "max                          1961.691771                        14.164140   \n",
       "min                            50.161396                        -0.987140   \n",
       "IQR                           126.162962                         1.000000   \n",
       "Q3+(1.5*IQR)                  428.093499                         2.008447   \n",
       "Q1-(1.5*IQR)                  -76.558347                        -1.991553   \n",
       "\n",
       "              Standard Scaled Revenue Generated  \n",
       "mean                               1.675206e-16  \n",
       "std                                1.000100e+00  \n",
       "median                            -2.035315e-02  \n",
       "mode                              -1.466511e+00  \n",
       "max                                2.073007e+01  \n",
       "min                               -1.466511e+00  \n",
       "IQR                                1.464997e+00  \n",
       "Q3+(1.5*IQR)                       2.922016e+00  \n",
       "Q1-(1.5*IQR)                      -2.937973e+00  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate required statistics for the 'Revenue Generated ($)' columns\n",
    "\n",
    "# Define a function to calculate IQR, Q1-IQR, Q3+IQR\n",
    "def calculate_iqr_stats(column):\n",
    "    Q1 = column.quantile(0.25)\n",
    "    Q3 = column.quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    \n",
    "    # Handle mode as a single value or list of modes\n",
    "    mode_values = column.mode()  # mode() returns a Series\n",
    "    mode_result = mode_values.iloc[0] if not mode_values.empty else None  # Take the first mode if available\n",
    "    \n",
    "    return {\n",
    "        'mean'        : column.mean(),\n",
    "        'std'         : column.std(),\n",
    "        'median'      : column.median(),\n",
    "        'mode'        : mode_result, \n",
    "        'max'         : column.max(),\n",
    "        'min'         : column.min(),\n",
    "        'IQR'         : IQR,\n",
    "        'Q3+(1.5*IQR)': Q3 + (1.5 * IQR),\n",
    "        'Q1-(1.5*IQR)': Q1 - (1.5 * IQR)\n",
    "    }\n",
    "\n",
    "# Apply the function to the required columns\n",
    "stats_original = calculate_iqr_stats(df_revenue_scaled['Revenue Generated ($)'])\n",
    "stats_robust = calculate_iqr_stats(df_revenue_scaled['Revenue Generated ($)_Robust'])\n",
    "stats_standard = calculate_iqr_stats(df_revenue_scaled['Revenue Generated ($)_Standard'])\n",
    "\n",
    "# Create a DataFrame to summarize the statistics\n",
    "df_stats = pd.DataFrame({\n",
    "    'Original Revenue Generated': stats_original,\n",
    "    'Robust Scaled Revenue Generated': stats_robust,\n",
    "    'Standard Scaled Revenue Generated': stats_standard\n",
    "})\n",
    "\n",
    "df_stats\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```Python\n",
    "'RobustScaler 'effectively handles outliers by maintaining a more balanced range for revenue, whereas 'StandardScaler' compresses the bulk of values while amplifying the effect of extreme outliers.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outliers Analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot for detecting outliers\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(16, 10))\n",
    "\n",
    "# Create individual enhanced boxplots for each numerical feature\n",
    "for i, column in enumerate(df_scaled_robust, 1):\n",
    "    plt.subplot(2, 3, i)\n",
    "    sns.boxplot(\n",
    "        x=df_scaled_robust[column], \n",
    "        color=\"lightblue\", \n",
    "        flierprops=dict(markerfacecolor='r', marker='o', markersize=5)  # Red outlier points\n",
    "    )\n",
    "    plt.title(f\"Boxplot for {column}\", fontsize=14, fontweight='bold')\n",
    "    plt.xlabel(column, fontsize=12)\n",
    "    plt.ylabel('Values', fontsize=12)\n",
    "    \n",
    "# Adjust the layout for better visual aesthetics\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers removed: 5\n",
      "Filtered dataset shape: (5000, 6)\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>Subscription Type</th>\n",
       "      <th>Number of Logins</th>\n",
       "      <th>Avg Session Duration (mins)</th>\n",
       "      <th>Feature Usage Count</th>\n",
       "      <th>Customer Lifetime (months)</th>\n",
       "      <th>Revenue Generated ($)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Free</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1704</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16.970000</td>\n",
       "      <td>34.920808</td>\n",
       "      <td>6.506000</td>\n",
       "      <td>17.021800</td>\n",
       "      <td>175.044288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.195683</td>\n",
       "      <td>14.439130</td>\n",
       "      <td>2.876877</td>\n",
       "      <td>7.193141</td>\n",
       "      <td>72.484345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.004903</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>50.161396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>22.412517</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>112.527295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>34.870373</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>174.653483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>47.405136</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>238.549643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>59.987484</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>299.943424</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Subscription Type  Number of Logins  Avg Session Duration (mins)  \\\n",
       "count               5000       5000.000000                  5000.000000   \n",
       "unique                 3               NaN                          NaN   \n",
       "top                 Free               NaN                          NaN   \n",
       "freq                1704               NaN                          NaN   \n",
       "mean                 NaN         16.970000                    34.920808   \n",
       "std                  NaN          7.195683                    14.439130   \n",
       "min                  NaN          5.000000                    10.004903   \n",
       "25%                  NaN         11.000000                    22.412517   \n",
       "50%                  NaN         17.000000                    34.870373   \n",
       "75%                  NaN         23.000000                    47.405136   \n",
       "max                  NaN         29.000000                    59.987484   \n",
       "\n",
       "        Feature Usage Count  Customer Lifetime (months)  Revenue Generated ($)  \n",
       "count           5000.000000                 5000.000000            5000.000000  \n",
       "unique                  NaN                         NaN                    NaN  \n",
       "top                     NaN                         NaN                    NaN  \n",
       "freq                    NaN                         NaN                    NaN  \n",
       "mean               6.506000                   17.021800             175.044288  \n",
       "std                2.876877                    7.193141              72.484345  \n",
       "min                2.000000                    5.000000              50.161396  \n",
       "25%                4.000000                   11.000000             112.527295  \n",
       "50%                6.000000                   17.000000             174.653483  \n",
       "75%                9.000000                   23.000000             238.549643  \n",
       "max               11.000000                   29.000000             299.943424  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removal of outliers:\n",
    "from scipy import stats\n",
    "\n",
    "# Calculate Z-scores\n",
    "z_scores = stats.zscore(df[numerical_features])\n",
    "\n",
    "# Create a boolean mask for outliers (Z-score > 3 or < -3)\n",
    "outliers_mask = (z_scores > 3) | (z_scores < -3)\n",
    "\n",
    "# Identify outliers\n",
    "outliers = df[outliers_mask.any(axis=1)]\n",
    "\n",
    "# Removing outliers from the dataset\n",
    "filtered_dataset = df[~outliers_mask.any(axis=1)]\n",
    "\n",
    "# Display the filtered dataset\n",
    "print(f\"Number of outliers removed: {len(outliers)}\")\n",
    "print(f\"Filtered dataset shape: {filtered_dataset.shape}\")\n",
    "\n",
    "filtered_dataset.describe(include='all')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YjBo1yvbFxcXFxZrfu+++a7uKSJJ55JFHzNSpU83UqVPNZ599luZ6XrZsmZk6daot2W/ZsqU1/fbt2821a9dsJ/zd3NxMZGSkee6555KdIPnwww+teSf98irJ1K9f3zz33HNm2LBh5rXXXksztkmTJtmmXbRoUZrtE8vKl+qvv/7aGubl5WWGDBliXnzxRfP444+b++67z/j6+lqFjfTW29SpU40xJtvWnaurqxkwYIAZNmyY8fDwsI1zd3c3gwcPTnYl2uTJk635HThwwHh7e1vjqlWrZp577jkzZswY23urSpUqti/oiddjwrJ69eplJkyYYHr06GH++OOPNLdDdhU2MvI+jY2NNdWqVbN9sX3iiSdMVFSUadiwoW3b7t+/3xiT8c9PRgsbCZ/bJ554IsUrA/38/Mzw4cNNly5dbMMTTk4aY8zatWuNq6urNe7uu+8248ePN8OHD7ed/GrZsmW66zJB4mJESvuElPa3Xbt2NaNHj7ZdAZnw16tXLzNs2DDbCZhBgwZZ88vKviyldenq6mp69+5tRo0aZet7QECAuXLlijHGmKlTp5pHHnnENt2YMWOsbZjwS5OU+ti8eXPz/PPPmxo1atiGJ/wyyxhjChcubA1v0aKFeeGFF8zo0aPNww8/bMqUKZNqYaNNmzbWdBMmTMjwtoKzkKuQqxiTcmHj6tWrZuPGjbZ13LFjR9t0MTExtn1MSEiIefrpp824ceOSHc/OnDljjLn+y53EFxksXLjQmt+ff/5pXFxcku3LsnJ8TOm9k5F9sjFZL2wkfu/7+/ubQYMGmYkTJ9r2py4uLmbNmjXpbpPDhw/bltOhQ4d0p0lq8uTJtuV26dLFTJw40fTt29eWi02aNMmaJmm+IMncdddd5rnnnjONGjXK9mN/0qKhj4+PGThwoImKijIPPvigOXv2rDl//rwpV66c6dGjhxk5cqR58cUXTVRUlOnevbvtOD5v3jwrlqlTp5py5cpZ4+rWrWvLcWNiYrK8zaZOnWqL+c477zTPP/+86dixY7J1l9HCRlRUlDVNmzZtUmwTFBRktfnjjz+s93dGHDlyxBZXar+4AQAAAIUNY0zGTxa8+OKLtulOnDhhu1opPDzcNj48PNz2pffEiRPWuLi4ONO0aVNrvJ+fny3pTvylzZjkX/g++eQTa1x0dLTtS0/Pnj2tcamdLHjttdds8/v666+tcUePHrXFk/gLWlonXTMq6U/ZE1u0aJFt/ol/qn/x4kXbtDVr1rTGJf3y2qlTJxMXF5fhmAYPHmybPr2T6Ill5Uv1F198YQ1r3bp1snlevnw52S0M0lpvxmTfunvhhRescd27d7eNSyiiGGPM3XffbVvfCYYNG2YNr1ixou0qwX/++cf2mUntll6SzOLFi1NY26nLrsJGgrTW95IlS2yf7cS3IYiNjbWdvB42bJg1LiOfn8wUNubMmWONS3ortQULFhhjjImPjzfFixe3hj/11FPWNIm/2Ddt2tT2mfnll19s89uyZUu66zM2NtZ2omvt2rXJ2iTdTolvszB69GjbuCFDhljjunXrZg2vXbu2NTyr+7Kk6zLxVZ6LFy+2jdu6dWuq8Se+RUZqbTp27GjdQuTkyZO2z8Drr79uTZf4ivuUfiWT+CRgYokLWyndLgZ5A7kKuUrSuFL7a9OmjTl58qRtuunTp1vj8+fPbxt//vx5ExwcbI2fPn26NS4yMtIa3rlzZ2v4yy+/bA2vUqWKNTyrx8es7pOzkoNt2bLFNvz777+3TXfffffZ9t/pSXq8fPrpp23j33777RS3U0LeERcXZysojB071jZ94nVdsGBB6z2T9H1er1496zZPV69etRWysuPYn/i95+bmZjZt2pTqOjl69KhZsmSJmTFjhnnllVfM1KlTbUW/vn372tqntR2Nyfo2S1yALF++vO1XTIlvqZeZ/UTiX6am9quLtm3bWm28vb2tgvD06dPN+vXr0/3cJ95X8ktMAACA1N0eT3jLpK5du2rq1KmaMmWKevXqJXf3689YHzNmjCZMmGC1++WXXxQXF2e9joiIsM0n8eu4uDj98ssv1mtXV1d9/PHHKlCggCTp/PnzkiQfHx/NnTtX+fLlSzU+Dw8Pde3a1XpdpkwZNW7c2HqdkYcSrl+/3vp/cHCw2rZta70uXLiw7XXitjkt6bJ69+5t/d/b29v2IOKtW7fq4sWLKc5nzJgxt/QDDO+66y7rQdgrVqxQtWrV1L17d40bN06LFy/W1atXVaJEiUzNM7vW3cMPP2z9v0yZMrZxiecRGhpq/f/06dPW/9euXWv9f8+ePfL29paLi4tcXFxUvHhx22dm3bp1KcZQvXp1dejQIcVxt4LEfYyLi1PFihWtPrq7u2vbtm3W+NT6eKPc3d2T7QcSeHh4qGPHjpIkFxcXlS1b1hqX2rZavXq13NzcrH7Uq1fPtryM9OPkyZO2h2Qm7N/ScqPvt+zYl7m5uWnQoEHW60qVKtnGJ15eVjz66KNycXGRdH2dFCpUKMV533PPPdb/q1evrnbt2unJJ5/Ue++9p3379qlcuXIpzr9gwYLW/48fP35DscI5yFXIVVJSvnx5TZgwIdn+N/H+/vTp0ypYsKC1v/fz87PtOxLv7/v06WP9/6uvvtK5c+ckSXPnzk2xTXYcH3N6n5w4Rklq1qyZFaOLi4u+/vrrdGNMS8L+PqN2796tEydOWK8nTJhgi+fpp5+2xp08eVJ79uxJcT79+/eXh4eHpOufv5w89rdt21a1a9dONvzSpUvq06ePihUrpg4dOmjw4MEaMWKERo4cqe3bt1vt/v777xTnm5qsbLPz589r9+7d1vDOnTtbubdkzz8yI/FnJbU85/XXX7fy+EuXLunw4cOSpCeeeEINGjRQ6dKlNWfOnFSXkXi+HNcBAABSd+ue+c1Fbdq00YgRI/TMM8/oo48+0rPPPmuNmzhxopWcnjp1yjZdkSJF0nyd9ItYyZIl9eCDD9qGtWjRItkXuKQKFiwoNze3VJd15syZNKeX7LEnjTPpsBv9ApkZiePy8/OTr69vqnEZY1Lta+XKlTO13KRFhF27dmVq+sQxJXblypUU25UsWVKzZ8+2TnDu3LlTn332mSZMmKCOHTuqePHi+uyzzzK17Oxad8WLF7f+n/SkVeJxCSfRJCk+Pj7FONKT2pe1zG6/my07+nijChcubNsGibdV4cKFbfuInNxWNyo7329Z3ZcVKVJEXl5e1uvEJz6SLi8rkhZsEs8/8bzffvtt3X333ZKun7z6+uuvNX36dA0cOFAVKlRQ165dU4wl6X4HtwdyldsvV0mqXLlymjp1qh5//HEFBARIkvbt26d7771XO3fuTDXm9CTe3zdp0kTly5eXJF2+fFlffPGFdu3apc2bN0u6vm9OXNjJjuNKVvfJGc3BsvvYlzSHTHwyXZIaNGigqVOnavjw4TccT1oxZfRYk5N52ujRozV79ux0j5upbZvUZCXmpJ+7woUL216ntE/JLuXLl9eePXs0c+ZMde3aVUWLFrWNP3z4sCIiIvTTTz+lOD3HdQAAgIxxT78JEl+9FBsbq19//VUlSpRIdpXO0aNH03ydP39+2+sffvhBs2fPtg1bunSpFi9enOwkQmInT55UXFyc7YRB4mUFBQWl1R1J9iuBksaZdFjSuHNS4rjOnz+vCxcu2E4YJI7LxcUl1b4mPcmQnubNm9tOCs2ePTvNbZBY4qstL126ZBu3d+/eVKfr1q2bOnfurF9++UXbtm3T3r17tWrVKm3evFnnz59Xv379dP/998vPzy9DcWTXuku42i8liU8uZySOatWqKTIyMtW21atXT3F4ZrffzZa4j15eXpo4cWKqbQMDA3MkhhvdTtL1fhw7dkyS1Lhx4zR/JdOwYcMMzc/FxcX6Qp6RE43Z+X7L6r4saQyZvdo2PRmdf6lSpbR+/Xrt27dPv/zyi/bu3att27ZpyZIlio2N1fz589WmTRvb1dGS/YRPcHBwtsYO5yBXyfu5SlKlSpXSiBEjJF3/hVuTJk0UHx+vCxcuaOjQofruu+9SjLlYsWJ66qmn0pxvYpGRkXruueckXf+lxoEDB6xxbdu2tZ0gzo7jY2b2yVnJwZJ+JiZMmCBvb+9Ul5Ge4sWLq3LlytZFMStWrNCRI0esk9k1a9ZUzZo1tX37dr366qvpxhMREZFqfiQlL2AkyOh6y45jf2rv3Xnz5ln/r1GjhubOnatKlSrJ3d1d4eHhWrBgQarLSktWtlnS91dCnxOktE/JiNR+dZmUj4+P+vbtq759+2r8+PGKiorSr7/+qqioKP3vf/9TfHy85syZY/u1Zkrz5bgOAACQOgobGfDrr7/aXifc0qFevXpyc3OzXn/44Ye67777rHYffvih9X83NzfbSYfTp0+rV69e1hVNVapU0R9//CHp+k/J69WrZ7taObFr165p3rx56tGjhyTp4MGDWrNmjTW+Tp066fapYcOGmj9/vqTrVzYtW7bMuqXDsWPHtGzZMlvbBEm/NKV2e4WsSvoF6qOPPtKjjz4q6foX1oSYpetfFH18fLJlufXr19fdd9+tn3/+WZK0ZMkSvfzyy7af/yfYtGmT/vnnH7Vv316S/eTM5s2bdfXqVeXLl0+HDx+2vQcSO3XqlM6dO6eQkBA1atRIjRo1knT9fZHw5e3ixYvavXu3tT0Tr/uU1nturbuU4ki4lcm///6r7t27J7uaMTY2Vl9++aXq16+fIzHktMTr+vLly6pWrZrtligJNmzYYLtiMqc/P5nVsGFDLV68WJJ05MgRDRw40LrqN8GlS5e0YMGCDBU23N3dVbp0aR06dEiS9Ndff6lBgwbZHndiWd2XZVVObsMtW7aoRo0aKl++vHWFtCR16NBBS5culST99ttvyQobf/31l/X/1G5XhbyPXCXv5yppadSokXr16mVtz++//14//PCDwsLCrJgTr8tWrVrpjjvusM3DGKPvvvvOdus/6fqJ9rFjxyo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",
      "text/plain": [
       "<Figure size 1600x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot after outlier removal:\n",
    "numerical_columns = filtered_dataset.select_dtypes(include=['float64', 'int64']).columns\n",
    "scaler = RobustScaler()\n",
    "df_outliers_removed = pd.DataFrame(scaler.fit_transform(filtered_dataset[numerical_columns]), columns=numerical_columns)\n",
    "\n",
    "plt.figure(figsize=(16, 10))\n",
    "\n",
    "# Create individual enhanced boxplots for each numerical feature\n",
    "for i, column in enumerate(df_outliers_removed, 1):\n",
    "    plt.subplot(2, 3, i)\n",
    "    sns.boxplot(\n",
    "        x=df_outliers_removed[column], \n",
    "        color=\"lightblue\", \n",
    "        flierprops=dict(markerfacecolor='r', marker='s', markersize=5)  # Red outlier points\n",
    "    )\n",
    "    plt.title(f\"Boxplot for {column}\", fontsize=14, fontweight='bold')\n",
    "    plt.xlabel(column, fontsize=12)\n",
    "    plt.ylabel('Values', fontsize=12)\n",
    "    \n",
    "# Adjust the layout for better visual aesthetics\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Subscription Type_Basic</th>\n",
       "      <th>Subscription Type_Free</th>\n",
       "      <th>Subscription Type_Pro</th>\n",
       "      <th>Number of Logins</th>\n",
       "      <th>Avg Session Duration (mins)</th>\n",
       "      <th>Feature Usage Count</th>\n",
       "      <th>Customer Lifetime (months)</th>\n",
       "      <th>Revenue Generated ($)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.250000</td>\n",
       "      <td>-0.507825</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.250000</td>\n",
       "      <td>-0.588642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.154244</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.083333</td>\n",
       "      <td>-0.110532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>-0.082068</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.416667</td>\n",
       "      <td>-0.269273</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-0.446370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Subscription Type_Basic  Subscription Type_Free  Subscription Type_Pro  \\\n",
       "0                      0.0                     1.0                    0.0   \n",
       "1                      0.0                     0.0                    1.0   \n",
       "2                      0.0                     0.0                    1.0   \n",
       "3                      0.0                     0.0                    1.0   \n",
       "\n",
       "   Number of Logins  Avg Session Duration (mins)  Feature Usage Count  \\\n",
       "0         -0.250000                    -0.507825                 -0.4   \n",
       "1          0.000000                    -0.154244                  0.2   \n",
       "2          0.083333                    -0.082068                  0.2   \n",
       "3         -0.416667                    -0.269273                 -0.4   \n",
       "\n",
       "   Customer Lifetime (months)  Revenue Generated ($)  \n",
       "0                   -0.250000              -0.588642  \n",
       "1                   -0.083333              -0.110532  \n",
       "2                    0.000000               0.004547  \n",
       "3                   -0.500000              -0.446370  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Specify categorical and numerical features\n",
    "categorical_features = ['Subscription Type']\n",
    "numerical_features = ['Number of Logins', 'Avg Session Duration (mins)', 'Feature Usage Count', 'Customer Lifetime (months)', 'Revenue Generated ($)']\n",
    "\n",
    "# Define the preprocessing pipeline\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('cat', OneHotEncoder(drop=None), categorical_features),\n",
    "        ('num', RobustScaler(), numerical_features)\n",
    "    ]\n",
    ")\n",
    "\n",
    "\n",
    "# Fit and transform the data using the preprocessor\n",
    "transformed_data = preprocessor.fit_transform(filtered_dataset)\n",
    "# Get the feature names after transformations - otherwiae dataframe when displayed shows only 0, 1, 2, 3 etc\n",
    "cat_feature_names = preprocessor.named_transformers_['cat'].get_feature_names_out(categorical_features)\n",
    "all_feature_names = np.concatenate([cat_feature_names, numerical_features])\n",
    "# Create a DataFrame with the transformed data and feature names\n",
    "df_transformed = pd.DataFrame(transformed_data, columns=all_feature_names)\n",
    "df_transformed.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.351843</td>\n",
       "      <td>3.238329e-01</td>\n",
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       "      <th>2</th>\n",
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       "      <td>-8.881784e-16</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "          0             1             2         3         4         5  \\\n",
       "0  0.223048  7.769517e-01 -1.110223e-16  0.330297  0.322538  0.424238   \n",
       "1  0.351843  3.238329e-01  3.243243e-01 -0.615807 -0.589748 -0.490221   \n",
       "2  0.384568 -8.881784e-16  6.154321e-01  0.491615  0.479268  0.575926   \n",
       "\n",
       "          6         7  \n",
       "0  0.337485  0.328276  \n",
       "1 -0.612121 -0.586065  \n",
       "2  0.494342  0.473220  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build the k-means model for k=3 : show the outputs of the model- centroids, wss and labeled data (resulting clusters)\n",
    "# instatiate KMeans class and set the number of clusters\n",
    "km_model = KMeans(n_clusters=3, random_state=10)\n",
    "\n",
    "# call fit method with data \n",
    "km = km_model.fit_predict(df_transformed)\n",
    "\n",
    "# Coordinates of cluster centers (centroids)\n",
    "centroids = km_model.cluster_centers_\n",
    "centroids_df = pd.DataFrame(centroids)  \n",
    "centroids_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "   0\n",
       "0  1\n",
       "1  2\n",
       "2  2\n",
       "3  1\n",
       "4  1"
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     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# cluster label for each data point\n",
    "labels = km_model.labels_ \n",
    "labels_df = pd.DataFrame(labels)\n",
    "labels_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Debrup Banerjee\\AppData\\Local\\Temp\\ipykernel_18744\\3805374671.py:24: DeprecationWarning: Arrays of 2-dimensional vectors are deprecated. Use arrays of 3-dimensional vectors instead. (deprecated in NumPy 2.0)\n",
      "  distance = np.abs(np.cross(point2-point1, point1-point)) / np.linalg.norm(point2-point1)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal k value: 2\n"
     ]
    }
   ],
   "source": [
    "# Define the range of k values to try\n",
    "k_values = range(1, 11)\n",
    "inertias = []\n",
    "\n",
    "# Fit KMeans and calculate inertia for each k\n",
    "for k in k_values:\n",
    "    model = KMeans(n_clusters=k, random_state=42)\n",
    "    model.fit(df_transformed)  # Fit the model\n",
    "    inertias.append(model.inertia_)  # Append the inertia for this k\n",
    "\n",
    "# Convert to numpy array for further calculation\n",
    "inertias = np.array(inertias)\n",
    "\n",
    "# Manually find the elbow point by finding the point with the maximum distance\n",
    "# Create a straight line between the first and last points\n",
    "point1 = np.array([k_values[0], inertias[0]])  # First point (k=1, WCSS)\n",
    "point2 = np.array([k_values[-1], inertias[-1]])  # Last point (k=max, WCSS)\n",
    "\n",
    "# Calculate the distance of each point from the line connecting point1 and point2\n",
    "distances = []\n",
    "for i in range(len(k_values)):\n",
    "    point = np.array([k_values[i], inertias[i]])\n",
    "    # Compute the perpendicular distance from point to the line\n",
    "    distance = np.abs(np.cross(point2-point1, point1-point)) / np.linalg.norm(point2-point1)\n",
    "    distances.append(distance)\n",
    "\n",
    "# Find the index of the maximum distance (the elbow point)\n",
    "k_optimal = k_values[np.argmax(distances)]\n",
    "\n",
    "# Plot the elbow curve\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(k_values, inertias, 'bs-', markersize=4, label='WCSS')\n",
    "plt.axvline(x=k_optimal, color='r', linestyle='--', label=f'Optimal k = {k_optimal}')\n",
    "plt.xlabel('Number of clusters, k')\n",
    "plt.ylabel('Within-cluster Sum of Squared distances (WCSS)')\n",
    "plt.title('Elbow Method For Optimal k')\n",
    "plt.xticks(k_values)\n",
    "plt.grid(True)\n",
    "\n",
    "# Add a legend explaining the plot\n",
    "plt.legend(loc='best', title='Legend')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n",
    "\n",
    "# Print the detected optimal k value\n",
    "print(f\"Optimal k value: {k_optimal}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>Subscription Type_Basic</th>\n",
       "      <th>Subscription Type_Free</th>\n",
       "      <th>Subscription Type_Pro</th>\n",
       "      <th>Number of Logins</th>\n",
       "      <th>Avg Session Duration (mins)</th>\n",
       "      <th>Feature Usage Count</th>\n",
       "      <th>Customer Lifetime (months)</th>\n",
       "      <th>Revenue Generated ($)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.250000</td>\n",
       "      <td>-0.507825</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.250000</td>\n",
       "      <td>-0.588642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.154244</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.083333</td>\n",
       "      <td>-0.110532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>-0.082068</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004547</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Subscription Type_Basic  Subscription Type_Free  Subscription Type_Pro  \\\n",
       "0                      0.0                     1.0                    0.0   \n",
       "1                      0.0                     0.0                    1.0   \n",
       "2                      0.0                     0.0                    1.0   \n",
       "\n",
       "   Number of Logins  Avg Session Duration (mins)  Feature Usage Count  \\\n",
       "0         -0.250000                    -0.507825                 -0.4   \n",
       "1          0.000000                    -0.154244                  0.2   \n",
       "2          0.083333                    -0.082068                  0.2   \n",
       "\n",
       "   Customer Lifetime (months)  Revenue Generated ($)  \n",
       "0                   -0.250000              -0.588642  \n",
       "1                   -0.083333              -0.110532  \n",
       "2                    0.000000               0.004547  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transformed.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Subscription Type_Basic</th>\n",
       "      <th>Subscription Type_Free</th>\n",
       "      <th>Subscription Type_Pro</th>\n",
       "      <th>Number of Logins</th>\n",
       "      <th>Avg Session Duration (mins)</th>\n",
       "      <th>Feature Usage Count</th>\n",
       "      <th>Customer Lifetime (months)</th>\n",
       "      <th>Revenue Generated ($)</th>\n",
       "      <th>KMeans_Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.250000</td>\n",
       "      <td>-0.507825</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.250000</td>\n",
       "      <td>-0.588642</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.154244</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.083333</td>\n",
       "      <td>-0.110532</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>-0.082068</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004547</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Subscription Type_Basic  Subscription Type_Free  Subscription Type_Pro  \\\n",
       "0                      0.0                     1.0                    0.0   \n",
       "1                      0.0                     0.0                    1.0   \n",
       "2                      0.0                     0.0                    1.0   \n",
       "\n",
       "   Number of Logins  Avg Session Duration (mins)  Feature Usage Count  \\\n",
       "0         -0.250000                    -0.507825                 -0.4   \n",
       "1          0.000000                    -0.154244                  0.2   \n",
       "2          0.083333                    -0.082068                  0.2   \n",
       "\n",
       "   Customer Lifetime (months)  Revenue Generated ($)  KMeans_Cluster  \n",
       "0                   -0.250000              -0.588642               1  \n",
       "1                   -0.083333              -0.110532               0  \n",
       "2                    0.000000               0.004547               0  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting K-Means with optimal k(k_optimal is already assigned)\n",
    "\n",
    "categorical_features = ['Subscription Type_Basic', 'Subscription Type_Free', 'Subscription Type_Pro']\n",
    "numerical_features = ['Number of Logins', 'Avg Session Duration (mins)', 'Feature Usage Count', 'Customer Lifetime (months)', 'Revenue Generated ($)']\n",
    "\n",
    "# Define the preprocessing pipeline\n",
    "preprocessor_encoded_version = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('cat', OneHotEncoder(drop=None), categorical_features),\n",
    "        ('num', RobustScaler(), numerical_features)\n",
    "    ]\n",
    ")\n",
    "\n",
    "kmeans_pipeline_no_outliers = Pipeline(steps=[('preprocessor', preprocessor_encoded_version),\n",
    "                                  ('cluster', KMeans(n_clusters=k_optimal, random_state=42, init='k-means++'))])\n",
    "\n",
    "# Partition the df dataframe into train and test partitions ...\n",
    "\n",
    "# Fit and predict clusters\n",
    "df_transformed['KMeans_Cluster'] = kmeans_pipeline_no_outliers.fit_predict(df_transformed) # Fit the data to the visual\n",
    "df_transformed.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>0.688430</td>\n",
       "      <td>0.311570</td>\n",
       "      <td>0.648850</td>\n",
       "      <td>0.351150</td>\n",
       "      <td>0.662720</td>\n",
       "      <td>0.337280</td>\n",
       "      <td>0.420078</td>\n",
       "      <td>0.409729</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.648728</td>\n",
       "      <td>0.351272</td>\n",
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       "      <td>0.325832</td>\n",
       "      <td>0.677104</td>\n",
       "      <td>0.322896</td>\n",
       "      <td>-0.613625</td>\n",
       "      <td>-0.587607</td>\n",
       "      <td>-0.487867</td>\n",
       "      <td>-0.610038</td>\n",
       "      <td>-0.583906</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         0         1         2         3         4         5         6   \\\n",
       "0  0.688430  0.311570  0.648850  0.351150  0.662720  0.337280  0.420078   \n",
       "1  0.648728  0.351272  0.674168  0.325832  0.677104  0.322896 -0.613625   \n",
       "\n",
       "         7         8         9         10  \n",
       "0  0.409729  0.508525  0.424899  0.409002  \n",
       "1 -0.587607 -0.487867 -0.610038 -0.583906  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting centroids to a dataframe\n",
    "centroids = kmeans_pipeline_no_outliers.named_steps['cluster'].cluster_centers_\n",
    "centroids_df = pd.DataFrame(centroids)  \n",
    "centroids_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cluster</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2958</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cluster  Count\n",
       "0        0   2958\n",
       "1        1   2042"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Output the cluster counts\n",
    "# Get cluster labels from the k-means model\n",
    "labels = kmeans_pipeline_no_outliers.named_steps['cluster'].labels_\n",
    "\n",
    "# Add cluster labels to the transformed DataFrame\n",
    "#df_transformed['Cluster'] = labels\n",
    "\n",
    "# Perform value counts for clusters\n",
    "cluster_counts = df_transformed['KMeans_Cluster'].value_counts()\n",
    "\n",
    "# Convert the cluster counts to a DataFrame\n",
    "cluster_counts_df = cluster_counts.reset_index()\n",
    "\n",
    "# Rename the columns for clarity\n",
    "cluster_counts_df.columns = ['Cluster', 'Count']\n",
    "\n",
    "# Display the resulting DataFrame\n",
    "cluster_counts_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Number of Logins</th>\n",
       "      <th>Avg Session Duration (mins)</th>\n",
       "      <th>Feature Usage Count</th>\n",
       "      <th>Customer Lifetime (months)</th>\n",
       "      <th>Revenue Generated ($)</th>\n",
       "      <th>KMeans_Cluster</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.419766</td>\n",
       "      <td>0.409378</td>\n",
       "      <td>0.508249</td>\n",
       "      <td>0.424499</td>\n",
       "      <td>0.408691</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.614185</td>\n",
       "      <td>-0.588075</td>\n",
       "      <td>-0.488443</td>\n",
       "      <td>-0.610472</td>\n",
       "      <td>-0.584429</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Number of Logins  Avg Session Duration (mins)  Feature Usage Count  \\\n",
       "Cluster                                                                       \n",
       "0                0.419766                     0.409378             0.508249   \n",
       "1               -0.614185                    -0.588075            -0.488443   \n",
       "\n",
       "         Customer Lifetime (months)  Revenue Generated ($)  KMeans_Cluster  \\\n",
       "Cluster                                                                      \n",
       "0                          0.424499               0.408691             0.0   \n",
       "1                         -0.610472              -0.584429             1.0   \n",
       "\n",
       "         Cluster  \n",
       "Cluster           \n",
       "0            0.0  \n",
       "1            1.0  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add cluster labels to the original data (before clustering)\n",
    "df_transformed2 = df_transformed.copy()  # Ensure you're not modifying the original DataFrame\n",
    "df_transformed2['Cluster'] = labels\n",
    "\n",
    "# Separate numerical and categorical columns\n",
    "numerical_columns = df_transformed2.select_dtypes(include=['number']).columns\n",
    "categorical_columns = df_transformed2.select_dtypes(include=['object', 'category']).columns\n",
    "\n",
    "# Calculate the mean for numerical columns by cluster\n",
    "cluster_summary_numerical = df_transformed2.groupby('Cluster')[numerical_columns].mean()\n",
    "\n",
    "# Calculate the mode for categorical columns by cluster\n",
    "# Modify lambda to return the first mode, or NaN if there's no mode\n",
    "def get_mode_or_nan(x):\n",
    "    modes = x.mode()\n",
    "    return modes[0] if not modes.empty else np.nan\n",
    "\n",
    "cluster_summary_categorical = df_transformed2.groupby('Cluster')[categorical_columns].agg(get_mode_or_nan)\n",
    "\n",
    "# Combine both numerical and categorical summaries\n",
    "cluster_summary = pd.concat([cluster_summary_numerical, cluster_summary_categorical], axis=1)\n",
    "\n",
    "# Display the final summary\n",
    "cluster_summary_subset = cluster_summary.iloc[:, 3:]\n",
    "cluster_summary_subset\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```Python\n",
    "Key Insights:\n",
    "Cluster 0  - high-engagement customers who log in more frequently, use features more extensively, and generate more revenue.\n",
    "Cluster 1  - low-engagement customers, with less frequent logins, shorter session durations, and lower overall revenue.\n",
    "\n",
    "In terms of business actions:\n",
    "Cluster 0: Focus on retaining and upselling these users, as they are likely your most valuable customers.\n",
    "Cluster 1: Consider engagement strategies or retention efforts to prevent churn, as this group shows signs of lower engagement and value.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>Subscription Type_Basic</th>\n",
       "      <th>Subscription Type_Free</th>\n",
       "      <th>Subscription Type_Pro</th>\n",
       "      <th>Number of Logins</th>\n",
       "      <th>Avg Session Duration (mins)</th>\n",
       "      <th>Feature Usage Count</th>\n",
       "      <th>Customer Lifetime (months)</th>\n",
       "      <th>Revenue Generated ($)</th>\n",
       "      <th>KMeans_Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>-0.4</td>\n",
       "      <td>-0.250000</td>\n",
       "      <td>-0.588642</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.154244</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.083333</td>\n",
       "      <td>-0.110532</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>-0.082068</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004547</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Subscription Type_Basic  Subscription Type_Free  Subscription Type_Pro  \\\n",
       "0                      0.0                     1.0                    0.0   \n",
       "1                      0.0                     0.0                    1.0   \n",
       "2                      0.0                     0.0                    1.0   \n",
       "\n",
       "   Number of Logins  Avg Session Duration (mins)  Feature Usage Count  \\\n",
       "0         -0.250000                    -0.507825                 -0.4   \n",
       "1          0.000000                    -0.154244                  0.2   \n",
       "2          0.083333                    -0.082068                  0.2   \n",
       "\n",
       "   Customer Lifetime (months)  Revenue Generated ($)  KMeans_Cluster  \n",
       "0                   -0.250000              -0.588642               1  \n",
       "1                   -0.083333              -0.110532               0  \n",
       "2                    0.000000               0.004547               0  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_transformed.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Within-cluster sum of squares (WSS): 9052.277518329895\n"
     ]
    }
   ],
   "source": [
    "# WSS values\n",
    "# Get the WSS (inertia) from the trained KMeans model\n",
    "wss = kmeans_pipeline_no_outliers.named_steps['cluster'].inertia_\n",
    "print(f\"Within-cluster sum of squares (WSS): {wss}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Subscription Type_Basic</th>\n",
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       "      <th>Subscription Type_Pro</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.083333</td>\n",
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       "      <td>0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.416667</td>\n",
       "      <td>-0.269273</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-0.446370</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.916667</td>\n",
       "      <td>-0.597464</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>-0.916667</td>\n",
       "      <td>-0.931094</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Subscription Type_Basic  Subscription Type_Free  Subscription Type_Pro  \\\n",
       "0                      0.0                     1.0                    0.0   \n",
       "1                      0.0                     0.0                    1.0   \n",
       "2                      0.0                     0.0                    1.0   \n",
       "3                      0.0                     0.0                    1.0   \n",
       "4                      0.0                     1.0                    0.0   \n",
       "\n",
       "   Number of Logins  Avg Session Duration (mins)  Feature Usage Count  \\\n",
       "0         -0.250000                    -0.507825                 -0.4   \n",
       "1          0.000000                    -0.154244                  0.2   \n",
       "2          0.083333                    -0.082068                  0.2   \n",
       "3         -0.416667                    -0.269273                 -0.4   \n",
       "4         -0.916667                    -0.597464                 -0.6   \n",
       "\n",
       "   Customer Lifetime (months)  Revenue Generated ($)  KMeans_Cluster  \n",
       "0                   -0.250000              -0.588642               1  \n",
       "1                   -0.083333              -0.110532               0  \n",
       "2                    0.000000               0.004547               0  \n",
       "3                   -0.500000              -0.446370               1  \n",
       "4                   -0.916667              -0.931094               1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transformed.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette Score for K-Means (k=2): 0.47554742758485435\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette Score for K-Means (k=3): 0.3613191927391265\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "from sklearn.metrics import silhouette_score, silhouette_samples\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "if k_optimal is not None:\n",
    "    if k_optimal == 1: \n",
    "        k_start=k_optimal+1\n",
    "        \n",
    "    if k_optimal == 2: \n",
    "        k_start=k_optimal\n",
    "    k_end=k_start+2\n",
    "\n",
    "# Loop through different values of k and generate silhouette plots, starting from k=2\n",
    "for k in range(k_start, k_end):\n",
    "    # Fit K-Means with k clusters\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "    kmeans.fit(df_transformed)\n",
    "    cluster_labels = kmeans.labels_\n",
    "    \n",
    "    # Silhouette score for K-Means clustering\n",
    "    silhouette_avg = silhouette_score(df_transformed, cluster_labels)\n",
    "    print(f'Silhouette Score for K-Means (k={k}): {silhouette_avg}')\n",
    "    \n",
    "    # Silhouette plot for K-Means clustering\n",
    "    fig, ax1 = plt.subplots(1, 1)\n",
    "    fig.set_size_inches(10, 6)\n",
    "\n",
    "    # Silhouette values\n",
    "    sample_silhouette_values = silhouette_samples(df_transformed, cluster_labels)\n",
    "\n",
    "    y_lower = 10\n",
    "    for i in range(k):\n",
    "        # Aggregate the silhouette scores for samples in each cluster\n",
    "        ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]\n",
    "        ith_cluster_silhouette_values.sort()\n",
    "\n",
    "        size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
    "        y_upper = y_lower + size_cluster_i\n",
    "\n",
    "        # Adjusting the color map to improve contrast between clusters\n",
    "        color = cm.nipy_spectral(float(i) / k)\n",
    "        ax1.fill_betweenx(np.arange(y_lower, y_upper), 0, ith_cluster_silhouette_values, facecolor=color, edgecolor=color, alpha=0.7)\n",
    "\n",
    "        # Label the silhouette plots with cluster numbers at the middle\n",
    "        ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i), fontsize=12)\n",
    "\n",
    "        # Compute new y_lower for next plot\n",
    "        y_lower = y_upper + 10  # 10 for the 0 samples gap between clusters\n",
    "\n",
    "    ax1.set_title(f\"Silhouette plot for K-Means clustering with k={k}\", fontsize=14, fontweight='bold')\n",
    "    ax1.set_xlabel(\"Silhouette coefficient values\", fontsize=12)\n",
    "    ax1.set_ylabel(\"Cluster label\", fontsize=12)\n",
    "\n",
    "    # The vertical line for average silhouette score of all values\n",
    "    ax1.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\", label=f\"Average silhouette = {silhouette_avg:.2f}\")\n",
    "\n",
    "    # Setting fewer x-ticks and y-ticks\n",
    "    ax1.set_yticks(np.arange(0, df_transformed.shape[0], 200))  # Y-ticks every 200 samples\n",
    "    ax1.set_xticks(np.arange(-0.1, 1.1, 0.2))  # X-ticks with larger intervals (every 0.2)\n",
    "\n",
    "    plt.legend(loc='upper right')  # Add legend for average silhouette score\n",
    "    plt.grid(True)  # Add gridlines for clarity\n",
    "    plt.tight_layout()  # Improve spacing between plots\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### K-means Clustering after dropping the categorical feature: \n",
    "```Python\n",
    "'Subscription Type_Basic','Subscription Type_Free' and 'Subscription Type_Pro'\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers removed: 5\n",
      "Filtered dataset shape: (5000, 6)\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>Subscription Type</th>\n",
       "      <th>Number of Logins</th>\n",
       "      <th>Avg Session Duration (mins)</th>\n",
       "      <th>Feature Usage Count</th>\n",
       "      <th>Customer Lifetime (months)</th>\n",
       "      <th>Revenue Generated ($)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Free</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1704</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16.970000</td>\n",
       "      <td>34.920808</td>\n",
       "      <td>6.506000</td>\n",
       "      <td>17.021800</td>\n",
       "      <td>175.044288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.195683</td>\n",
       "      <td>14.439130</td>\n",
       "      <td>2.876877</td>\n",
       "      <td>7.193141</td>\n",
       "      <td>72.484345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.004903</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>50.161396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>22.412517</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>112.527295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>34.870373</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>174.653483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>47.405136</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>238.549643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>59.987484</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>299.943424</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Subscription Type  Number of Logins  Avg Session Duration (mins)  \\\n",
       "count               5000       5000.000000                  5000.000000   \n",
       "unique                 3               NaN                          NaN   \n",
       "top                 Free               NaN                          NaN   \n",
       "freq                1704               NaN                          NaN   \n",
       "mean                 NaN         16.970000                    34.920808   \n",
       "std                  NaN          7.195683                    14.439130   \n",
       "min                  NaN          5.000000                    10.004903   \n",
       "25%                  NaN         11.000000                    22.412517   \n",
       "50%                  NaN         17.000000                    34.870373   \n",
       "75%                  NaN         23.000000                    47.405136   \n",
       "max                  NaN         29.000000                    59.987484   \n",
       "\n",
       "        Feature Usage Count  Customer Lifetime (months)  Revenue Generated ($)  \n",
       "count           5000.000000                 5000.000000            5000.000000  \n",
       "unique                  NaN                         NaN                    NaN  \n",
       "top                     NaN                         NaN                    NaN  \n",
       "freq                    NaN                         NaN                    NaN  \n",
       "mean               6.506000                   17.021800             175.044288  \n",
       "std                2.876877                    7.193141              72.484345  \n",
       "min                2.000000                    5.000000              50.161396  \n",
       "25%                4.000000                   11.000000             112.527295  \n",
       "50%                6.000000                   17.000000             174.653483  \n",
       "75%                9.000000                   23.000000             238.549643  \n",
       "max               11.000000                   29.000000             299.943424  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove outliers:\n",
    "# Calculate Z-scores\n",
    "z_scores = stats.zscore(df[numerical_features])\n",
    "\n",
    "# Create a boolean mask for outliers (Z-score > 3 or < -3)\n",
    "outliers_mask = (z_scores > 3) | (z_scores < -3)\n",
    "\n",
    "# Identify outliers\n",
    "outliers = df[outliers_mask.any(axis=1)]\n",
    "\n",
    "# Removing outliers from the dataset\n",
    "filtered_dataset = df[~outliers_mask.any(axis=1)]\n",
    "\n",
    "# Display the filtered dataset\n",
    "print(f\"Number of outliers removed: {len(outliers)}\")\n",
    "print(f\"Filtered dataset shape: {filtered_dataset.shape}\")\n",
    "filtered_dataset.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Show boxplot after outlier removal\n",
    "# Select only numerical columns from the filtered dataset\n",
    "numerical_columns = filtered_dataset.select_dtypes(include=['float64', 'int64']).columns\n",
    "# Scale the numerical columns using RobustScaler for better visual comparison\n",
    "scaler = RobustScaler()\n",
    "df_outlier_removed = pd.DataFrame(scaler.fit_transform(filtered_dataset[numerical_columns]), columns=numerical_columns)\n",
    "\n",
    "\n",
    "# Display the boxplots:\n",
    "plt.figure(figsize=(16, 10))\n",
    "\n",
    "# Create individual enhanced boxplots for each numerical feature\n",
    "for i, column in enumerate(numerical_columns, 1):\n",
    "    plt.subplot(2, 3, i)\n",
    "    sns.boxplot(\n",
    "        x=df_outlier_removed[column], \n",
    "        color=\"lightblue\", \n",
    "        flierprops=dict(markerfacecolor='r', marker='o', markersize=5)  # Red outlier points\n",
    "    )\n",
    "    plt.title(f\"Boxplot for {column}\", fontsize=14, fontweight='bold')\n",
    "    plt.xlabel(column, fontsize=12)\n",
    "    plt.ylabel('Values', fontsize=12)\n",
    "\n",
    "# Adjust the layout for better visual aesthetics\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Number of Logins</th>\n",
       "      <th>Avg Session Duration (mins)</th>\n",
       "      <th>Feature Usage Count</th>\n",
       "      <th>Customer Lifetime (months)</th>\n",
       "      <th>Revenue Generated ($)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.250000</td>\n",
       "      <td>-0.507825</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.250000</td>\n",
       "      <td>-0.588642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.154244</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.083333</td>\n",
       "      <td>-0.110532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.083333</td>\n",
       "      <td>-0.082068</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.416667</td>\n",
       "      <td>-0.269273</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-0.446370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.916667</td>\n",
       "      <td>-0.597464</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>-0.916667</td>\n",
       "      <td>-0.931094</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Number of Logins  Avg Session Duration (mins)  Feature Usage Count  \\\n",
       "0         -0.250000                    -0.507825                 -0.4   \n",
       "1          0.000000                    -0.154244                  0.2   \n",
       "2          0.083333                    -0.082068                  0.2   \n",
       "3         -0.416667                    -0.269273                 -0.4   \n",
       "4         -0.916667                    -0.597464                 -0.6   \n",
       "\n",
       "   Customer Lifetime (months)  Revenue Generated ($)  \n",
       "0                   -0.250000              -0.588642  \n",
       "1                   -0.083333              -0.110532  \n",
       "2                    0.000000               0.004547  \n",
       "3                   -0.500000              -0.446370  \n",
       "4                   -0.916667              -0.931094  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the preprocessing pipeline\n",
    "preprocessor_numeric = ColumnTransformer(\n",
    "    transformers=[\n",
    "         ('num', RobustScaler(), numerical_features)\n",
    "    ]\n",
    ")\n",
    "\n",
    "\n",
    "# Fit and transform the data using the preprocessor\n",
    "transformed_data = preprocessor_numeric.fit_transform(filtered_dataset)\n",
    "# Convert transformed data back to a DataFrame for easier manipulation\n",
    "df_transformed = pd.DataFrame(transformed_data, columns=numerical_features)\n",
    "#df_transformed.drop(['Cluster'], axis=1)\n",
    "df_transformed.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Debrup Banerjee\\AppData\\Local\\Temp\\ipykernel_18744\\3805374671.py:24: DeprecationWarning: Arrays of 2-dimensional vectors are deprecated. Use arrays of 3-dimensional vectors instead. (deprecated in NumPy 2.0)\n",
      "  distance = np.abs(np.cross(point2-point1, point1-point)) / np.linalg.norm(point2-point1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal k value: 3\n"
     ]
    }
   ],
   "source": [
    "# Define the range of k values to try\n",
    "k_values = range(1, 11)\n",
    "inertias = []\n",
    "\n",
    "# Fit KMeans and calculate inertia for each k\n",
    "for k in k_values:\n",
    "    model = KMeans(n_clusters=k, random_state=42)\n",
    "    model.fit(df_transformed)  # Fit the model\n",
    "    inertias.append(model.inertia_)  # Append the inertia for this k\n",
    "\n",
    "# Convert to numpy array for further calculation\n",
    "inertias = np.array(inertias)\n",
    "\n",
    "# Manually find the elbow point by finding the point with the maximum distance\n",
    "# Create a straight line between the first and last points\n",
    "point1 = np.array([k_values[0], inertias[0]])  # First point (k=1, WCSS)\n",
    "point2 = np.array([k_values[-1], inertias[-1]])  # Last point (k=max, WCSS)\n",
    "\n",
    "# Calculate the distance of each point from the line connecting point1 and point2\n",
    "distances = []\n",
    "for i in range(len(k_values)):\n",
    "    point = np.array([k_values[i], inertias[i]])\n",
    "    # Compute the perpendicular distance from point to the line\n",
    "    distance = np.abs(np.cross(point2-point1, point1-point)) / np.linalg.norm(point2-point1)\n",
    "    distances.append(distance)\n",
    "\n",
    "# Find the index of the maximum distance (the elbow point)\n",
    "k_optimal = k_values[np.argmax(distances)]\n",
    "\n",
    "# Plot the elbow curve\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(k_values, inertias, 'bs-', markersize=4, label='WCSS')\n",
    "plt.axvline(x=k_optimal, color='r', linestyle='--', label=f'Optimal k = {k_optimal}')\n",
    "plt.xlabel('Number of clusters, k')\n",
    "plt.ylabel('Within-cluster Sum of Squared distances (WCSS)')\n",
    "plt.title('Elbow Method For Optimal k')\n",
    "plt.xticks(k_values)\n",
    "plt.grid(True)\n",
    "\n",
    "# Add a legend explaining the plot\n",
    "plt.legend(loc='best', title='Legend')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n",
    "\n",
    "# Print the detected optimal k value\n",
    "print(f\"Optimal k value: {k_optimal}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Partition the df_transformed dataframe into train and test partitions\n",
    "X_train, X_test = train_test_split(df_transformed, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Number of Logins</th>\n",
       "      <th>Avg Session Duration (mins)</th>\n",
       "      <th>Feature Usage Count</th>\n",
       "      <th>Customer Lifetime (months)</th>\n",
       "      <th>Revenue Generated ($)</th>\n",
       "      <th>KMeans_Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.250000</td>\n",
       "      <td>-0.507825</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.250000</td>\n",
       "      <td>-0.588642</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.154244</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.083333</td>\n",
       "      <td>-0.110532</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.083333</td>\n",
       "      <td>-0.082068</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004547</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.416667</td>\n",
       "      <td>-0.269273</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-0.446370</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.916667</td>\n",
       "      <td>-0.597464</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>-0.916667</td>\n",
       "      <td>-0.931094</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Number of Logins  Avg Session Duration (mins)  Feature Usage Count  \\\n",
       "0         -0.250000                    -0.507825                 -0.4   \n",
       "1          0.000000                    -0.154244                  0.2   \n",
       "2          0.083333                    -0.082068                  0.2   \n",
       "3         -0.416667                    -0.269273                 -0.4   \n",
       "4         -0.916667                    -0.597464                 -0.6   \n",
       "\n",
       "   Customer Lifetime (months)  Revenue Generated ($)  KMeans_Cluster  \n",
       "0                   -0.250000              -0.588642               2  \n",
       "1                   -0.083333              -0.110532               2  \n",
       "2                    0.000000               0.004547               2  \n",
       "3                   -0.500000              -0.446370               2  \n",
       "4                   -0.916667              -0.931094               1  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting K-Means with optimal k (k_optimal is already assigned)\n",
    "kmeans_pipeline = Pipeline(steps=[('preprocessor', preprocessor_numeric),\n",
    "                                  ('cluster', KMeans(n_clusters=k_optimal, random_state=42))])\n",
    "\n",
    "# Fit the pipeline on the entire transformed dataset (if you want clusters for the whole dataset)\n",
    "df_transformed['KMeans_Cluster'] = kmeans_pipeline.fit_predict(df_transformed)\n",
    "\n",
    "# Display the DataFrame with the new cluster labels\n",
    "df_transformed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Cluster</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cluster  Count\n",
       "0        0   2000\n",
       "1        2   1980\n",
       "2        1   1020"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Output the cluster counts\n",
    "# Get cluster labels from the k-means model\n",
    "labels = kmeans_pipeline.named_steps['cluster'].labels_\n",
    "\n",
    "# Add cluster labels to the transformed DataFrame\n",
    "df_transformed['Cluster'] = labels\n",
    "\n",
    "# Perform value counts for clusters\n",
    "cluster_counts = df_transformed['Cluster'].value_counts()\n",
    "\n",
    "# Convert the cluster counts to a DataFrame\n",
    "cluster_counts_df = cluster_counts.reset_index()\n",
    "\n",
    "# Rename the columns for clarity\n",
    "cluster_counts_df.columns = ['Cluster', 'Count']\n",
    "\n",
    "# Display the resulting DataFrame\n",
    "cluster_counts_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.620833</td>\n",
       "      <td>0.601904</td>\n",
       "      <td>0.704600</td>\n",
       "      <td>0.625958</td>\n",
       "      <td>0.601996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.831046</td>\n",
       "      <td>-0.792851</td>\n",
       "      <td>-0.690980</td>\n",
       "      <td>-0.820180</td>\n",
       "      <td>-0.789851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.205303</td>\n",
       "      <td>-0.194450</td>\n",
       "      <td>-0.100202</td>\n",
       "      <td>-0.205177</td>\n",
       "      <td>-0.193353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4\n",
       "0  0.620833  0.601904  0.704600  0.625958  0.601996\n",
       "1 -0.831046 -0.792851 -0.690980 -0.820180 -0.789851\n",
       "2 -0.205303 -0.194450 -0.100202 -0.205177 -0.193353"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting centroids to a dataframe\n",
    "centroids = kmeans_pipeline.named_steps['cluster'].cluster_centers_\n",
    "centroids_df = pd.DataFrame(centroids)  \n",
    "centroids_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Within-cluster sum of squares (WSS): 1134.7879783008516\n"
     ]
    }
   ],
   "source": [
    "# WSS values\n",
    "# Get the WSS (inertia) from the trained KMeans model\n",
    "wss = kmeans_pipeline.named_steps['cluster'].inertia_\n",
    "print(f\"Within-cluster sum of squares (WSS): {wss}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette Score for K-Means (k=3): 0.7548238026708775\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette Score for K-Means (k=4): 0.687120275520373\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## K-MEANS CLUSTERING: SILHOUETTE SCORES\n",
    "\n",
    "from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib.cm as cm\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# Loop through different values of k and generate silhouette plots\n",
    "for k in range(3, 5):\n",
    "    # Fit K-Means with k clusters\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "    kmeans.fit(df_transformed)\n",
    "    cluster_labels = kmeans.labels_\n",
    "    \n",
    "    # Silhouette score for K-Means clustering\n",
    "    silhouette_avg = silhouette_score(df_transformed, cluster_labels)\n",
    "    print(f'Silhouette Score for K-Means (k={k}): {silhouette_avg}')\n",
    "    \n",
    "    # Silhouette plot for K-Means clustering\n",
    "    fig, ax1 = plt.subplots(1, 1)\n",
    "    fig.set_size_inches(10, 6)\n",
    "\n",
    "    # Silhouette values\n",
    "    sample_silhouette_values = silhouette_samples(df_transformed, cluster_labels)\n",
    "\n",
    "    y_lower = 10\n",
    "    for i in range(k):\n",
    "        ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]\n",
    "        ith_cluster_silhouette_values.sort()\n",
    "\n",
    "        size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
    "        y_upper = y_lower + size_cluster_i\n",
    "\n",
    "        color = cm.nipy_spectral(float(i) / k)\n",
    "        ax1.fill_betweenx(np.arange(y_lower, y_upper), 0, ith_cluster_silhouette_values, facecolor=color, edgecolor=color, alpha=0.7)\n",
    "\n",
    "        # Label the silhouette plots with cluster numbers at the middle\n",
    "        ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
    "\n",
    "        # Compute new y_lower for next plot\n",
    "        y_lower = y_upper + 10  # 10 for the 0 samples gap between clusters\n",
    "\n",
    "    ax1.set_title(f\"Silhouette plot for K-Means clustering with k={k}\")\n",
    "    ax1.set_xlabel(\"Silhouette coefficient values\")\n",
    "    ax1.set_ylabel(\"Cluster label\")\n",
    "\n",
    "    # The vertical line for average silhouette score of all values\n",
    "    ax1.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n",
    "\n",
    "    # Setting fewer x-ticks and y-ticks\n",
    "    ax1.set_yticks(np.arange(0, df_transformed.shape[0], 200))  # Y-ticks every 200 samples\n",
    "    ax1.set_xticks(np.arange(-0.1, 1.1, 0.2))  # X-ticks with larger intervals (every 0.2)\n",
    "\n",
    "    plt.grid(True)  # Add gridlines for clarity\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_optimal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Agglomerative Clustering:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Subscription Type</th>\n",
       "      <th>Number of Logins</th>\n",
       "      <th>Avg Session Duration (mins)</th>\n",
       "      <th>Feature Usage Count</th>\n",
       "      <th>Customer Lifetime (months)</th>\n",
       "      <th>Revenue Generated ($)</th>\n",
       "      <th>Agglomerative_Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Free</td>\n",
       "      <td>14</td>\n",
       "      <td>22.178507</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>100.471472</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pro</td>\n",
       "      <td>17</td>\n",
       "      <td>31.015401</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>160.723959</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pro</td>\n",
       "      <td>18</td>\n",
       "      <td>32.819273</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>175.226528</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pro</td>\n",
       "      <td>12</td>\n",
       "      <td>28.140540</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>118.400847</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Free</td>\n",
       "      <td>6</td>\n",
       "      <td>19.938184</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>57.314870</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Subscription Type  Number of Logins  Avg Session Duration (mins)  \\\n",
       "0              Free                14                    22.178507   \n",
       "1               Pro                17                    31.015401   \n",
       "2               Pro                18                    32.819273   \n",
       "3               Pro                12                    28.140540   \n",
       "4              Free                 6                    19.938184   \n",
       "\n",
       "   Feature Usage Count  Customer Lifetime (months)  Revenue Generated ($)  \\\n",
       "0                    4                          14             100.471472   \n",
       "1                    7                          16             160.723959   \n",
       "2                    7                          17             175.226528   \n",
       "3                    4                          11             118.400847   \n",
       "4                    3                           6              57.314870   \n",
       "\n",
       "   Agglomerative_Cluster  \n",
       "0                      0  \n",
       "1                      0  \n",
       "2                      0  \n",
       "3                      0  \n",
       "4                      0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from sklearn.preprocessing import RobustScaler  # RobustScaler as preprocessor\n",
    "\n",
    "\n",
    "# Specify categorical and numerical features\n",
    "categorical_features = ['Subscription Type']\n",
    "numerical_features = ['Number of Logins', 'Avg Session Duration (mins)', 'Feature Usage Count', 'Customer Lifetime (months)', 'Revenue Generated ($)']\n",
    "\n",
    "# Define the preprocessor (RobustScaler works well for data with outliers)\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('cat', OneHotEncoder(), categorical_features),\n",
    "        ('num', RobustScaler(), numerical_features)\n",
    "    ]\n",
    ")\n",
    "\n",
    "\n",
    "# Create the clustering pipeline with preprocessing and Agglomerative Clustering\n",
    "agg_clustering_pipeline = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor),  # Preprocessing step\n",
    "    ('cluster', AgglomerativeClustering(n_clusters=k_optimal))\n",
    "])\n",
    "\n",
    "# Fit the pipeline and predict clusters on the original DataFrame\n",
    "df['Agglomerative_Cluster'] = agg_clustering_pipeline.fit_predict(df)\n",
    "\n",
    "# Drop the 'Cluster' column if it exists, without errors if not found\n",
    "df.drop(columns=['KMeans_Cluster'], errors='ignore', inplace=True)\n",
    "\n",
    "# Display the first few rows of the DataFrame with the cluster labels\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the Dendogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Subscription Type</th>\n",
       "      <th>Number of Logins</th>\n",
       "      <th>Avg Session Duration (mins)</th>\n",
       "      <th>Feature Usage Count</th>\n",
       "      <th>Customer Lifetime (months)</th>\n",
       "      <th>Revenue Generated ($)</th>\n",
       "      <th>Agglomerative_Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Free</td>\n",
       "      <td>14</td>\n",
       "      <td>22.178507</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>100.471472</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pro</td>\n",
       "      <td>17</td>\n",
       "      <td>31.015401</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>160.723959</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pro</td>\n",
       "      <td>18</td>\n",
       "      <td>32.819273</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>175.226528</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pro</td>\n",
       "      <td>12</td>\n",
       "      <td>28.140540</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>118.400847</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Free</td>\n",
       "      <td>6</td>\n",
       "      <td>19.938184</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>57.314870</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Subscription Type  Number of Logins  Avg Session Duration (mins)  \\\n",
       "0              Free                14                    22.178507   \n",
       "1               Pro                17                    31.015401   \n",
       "2               Pro                18                    32.819273   \n",
       "3               Pro                12                    28.140540   \n",
       "4              Free                 6                    19.938184   \n",
       "\n",
       "   Feature Usage Count  Customer Lifetime (months)  Revenue Generated ($)  \\\n",
       "0                    4                          14             100.471472   \n",
       "1                    7                          16             160.723959   \n",
       "2                    7                          17             175.226528   \n",
       "3                    4                          11             118.400847   \n",
       "4                    3                           6              57.314870   \n",
       "\n",
       "   Agglomerative_Cluster  \n",
       "0                      0  \n",
       "1                      0  \n",
       "2                      0  \n",
       "3                      0  \n",
       "4                      0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Assuming df is your original DataFrame\n",
    "# Generate the linkage matrix for the dendrogram\n",
    "linked = linkage(transformed_data, method='ward')\n",
    "\n",
    "# Create the figure for the dendrogram\n",
    "plt.figure(figsize=(10, 7))\n",
    "\n",
    "# Plot the dendrogram and capture the color information\n",
    "dendro = dendrogram(linked,\n",
    "                    orientation='top',\n",
    "                    distance_sort='descending',\n",
    "                    show_leaf_counts=True)\n",
    "\n",
    "# Retrieve the colors from the dendrogram\n",
    "cluster_colors = set([color for color in dendro['leaves_color_list']])\n",
    "\n",
    "# Plot the dendrogram\n",
    "plt.title(\"Dendrogram for Agglomerative Clustering\")\n",
    "plt.xlabel(\"Data Points\")\n",
    "plt.ylabel(\"Euclidean Distance\")\n",
    "\n",
    "# Adding a legend manually based on the colors used\n",
    "legend_labels = [f'Cluster {i+1}' for i in range(len(cluster_colors))]\n",
    "\n",
    "for i, color in enumerate(cluster_colors):\n",
    "    plt.scatter([], [], color=color, label=legend_labels[i])\n",
    "\n",
    "# Add the legend\n",
    "plt.legend(loc='upper right', title=\"Cluster Colors\")\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "# Display the first few rows of the DataFrame with the cluster labels\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette Score for Agglomerative Clustering (k=3): 0.7548238026708775\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette Score for Agglomerative Clustering (k=4): 0.687120275520373\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## AGGLOMERATIVE CLUSTERING : SILHOUETTE PLOTS\n",
    "\n",
    "from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib.cm as cm\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "# Transform the data using the preprocessor\n",
    "# df_transformed = preprocessor.transform(df)\n",
    "\n",
    "# Loop through different values of k and generate silhouette plots\n",
    "for k in range(3, 5):\n",
    "    # Fit Agglomerative Clustering with k clusters\n",
    "    agg_clustering = AgglomerativeClustering(n_clusters=k)\n",
    "    cluster_labels = agg_clustering.fit_predict(df_transformed)\n",
    "    \n",
    "    # Silhouette score for Agglomerative Clustering\n",
    "    silhouette_avg = silhouette_score(df_transformed, cluster_labels)\n",
    "    print(f'Silhouette Score for Agglomerative Clustering (k={k}): {silhouette_avg}')\n",
    "    \n",
    "    # Silhouette plot for Agglomerative Clustering\n",
    "    fig, ax1 = plt.subplots(1, 1)\n",
    "    fig.set_size_inches(10, 6)\n",
    "\n",
    "    # Silhouette values\n",
    "    sample_silhouette_values = silhouette_samples(df_transformed, cluster_labels)\n",
    "\n",
    "    y_lower = 10\n",
    "    for i in range(k):\n",
    "        ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]\n",
    "        ith_cluster_silhouette_values.sort()\n",
    "\n",
    "        size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
    "        y_upper = y_lower + size_cluster_i\n",
    "\n",
    "        color = cm.nipy_spectral(float(i) / k)\n",
    "        ax1.fill_betweenx(np.arange(y_lower, y_upper), 0, ith_cluster_silhouette_values, facecolor=color, edgecolor=color, alpha=0.7)\n",
    "\n",
    "        # Label the silhouette plots with cluster numbers at the middle\n",
    "        ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
    "\n",
    "        # Compute new y_lower for next plot\n",
    "        y_lower = y_upper + 10  # 10 for the 0 samples gap between clusters\n",
    "\n",
    "    ax1.set_title(f\"Silhouette plot for Agglomerative Clustering with k={k}\")\n",
    "    ax1.set_xlabel(\"Silhouette coefficient values\")\n",
    "    ax1.set_ylabel(\"Cluster label\")\n",
    "\n",
    "    # The vertical line for average silhouette score of all values\n",
    "    ax1.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n",
    "\n",
    "    # Setting fewer x-ticks and y-ticks\n",
    "    ax1.set_yticks(np.arange(0, df_transformed.shape[0], 200))  # Y-ticks every 200 samples\n",
    "    ax1.set_xticks(np.arange(-0.1, 1.1, 0.2))  # X-ticks with larger intervals (every 0.2)\n",
    "\n",
    "    plt.grid(True)  # Add gridlines for clarity\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DBSCAN - Density-Based Spatial Clustering of Applications with Noise Clustering:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameter tuning function definition\n",
    "# Function to try different eps and min_samples combinations\n",
    "\n",
    "def tune_dbscan(X_train):\n",
    "    best_eps = None\n",
    "    best_min_samples = None\n",
    "    best_silhouette = -1  # Initialize silhouette score to the worst possible value\n",
    "    best_ch_score = -1\n",
    "    best_labels = None\n",
    "    \n",
    "    \n",
    "    # Define a range of eps and min_samples to try\n",
    "    eps_values = np.arange(0.1, 1.0, 0.1)  # You can adjust the range and step size as needed\n",
    "    min_samples_values = range(3, 10)  # Range for min_samples\n",
    "\n",
    "\n",
    "    # Loop over all combinations of eps and min_samples\n",
    "    for eps in eps_values:\n",
    "        for min_samples in min_samples_values:\n",
    "            # Apply DBSCAN\n",
    "            dbscan = DBSCAN(eps=eps, min_samples=min_samples)\n",
    "            train_labels = dbscan.fit_predict(X_train)\n",
    "            \n",
    "            # Ignore the iteration if only one cluster is found\n",
    "            if len(set(train_labels)) > 1:\n",
    "                # Calculate silhouette score and Calinski-Harabasz score\n",
    "                silhouette_avg = silhouette_score(X_train, train_labels)\n",
    "                calinski_harabasz = calinski_harabasz_score(X_train, train_labels)\n",
    "                \n",
    "                # Check if this is the best performing model\n",
    "                if silhouette_avg > best_silhouette:\n",
    "                    best_eps = eps\n",
    "                    best_min_samples = min_samples\n",
    "                    best_silhouette = silhouette_avg\n",
    "                    best_ch_score = calinski_harabasz\n",
    "                    best_labels = train_labels\n",
    "\n",
    "    print(f\"Best eps: {best_eps}, Best min_samples: {best_min_samples}\")\n",
    "    print(f\"Best Silhouette Score: {best_silhouette}\")\n",
    "    print(f\"Best Calinski-Harabasz Score: {best_ch_score}\")\n",
    "    return best_labels, best_eps, best_min_samples, train_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and testing partitions (80% train, 20% test)\n",
    "X_train, X_test = train_test_split(transformed_data, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.25      , -0.50782459, -0.4       , -0.25      , -0.58864171],\n",
       "       [ 0.        , -0.15424442,  0.2       , -0.08333333, -0.11053217],\n",
       "       [ 0.08333333, -0.08206824,  0.2       ,  0.        ,  0.00454717],\n",
       "       ...,\n",
       "       [ 0.5       ,  0.38943882,  0.4       ,  0.58333333,  0.27449885],\n",
       "       [ 0.25      ,  0.49768481,  0.6       ,  0.58333333,  0.24067684],\n",
       "       [-1.        , -0.60242015, -0.8       , -0.83333333, -0.75807881]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformed_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best eps: 0.30000000000000004, Best min_samples: 3\n",
      "Best Silhouette Score: 0.597611929136805\n",
      "Best Calinski-Harabasz Score: 10329.589505409953\n"
     ]
    }
   ],
   "source": [
    "# Apply hyperparameter tuning to DBSCAN model to find the best hyper-parameters: eps and min_samples\n",
    "best_train_labels, best_eps, best_min_samples, train_labels = tune_dbscan(X_train)\n",
    "\n",
    "# Apply the tuned model to train\n",
    "dbscan_best = DBSCAN(eps=best_eps, min_samples=best_min_samples) \n",
    "train_labels = dbscan_best.fit_predict(X_train)\n",
    "\n",
    "\n",
    "# Add cluster labels to the training and test DataFrames\n",
    "df_train = pd.DataFrame(X_train)\n",
    "df_test = pd.DataFrame(X_test)\n",
    "df_train['Cluster'] = train_labels\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_clusters = len(set(train_labels)) - (1 if -1 in train_labels else 0)  # Exclude the noise cluster if it exists\n",
    "n_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_train_labels = np.unique(train_labels)\n",
    "unique_train_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The reason `k-Nearest Neighbors (kNN)` (NearestNeighbors) is being used in this code is due to a limitation with `DBSCAN`.\n",
    "\n",
    "DBSCAN does not have a `predict()` method like K-Means. Once DBSCAN is trained on a dataset, `it cannot directly assign` cluster labels to new data points (test data) because DBSCAN does not learn explicit cluster centers like K-Means does. Instead, DBSCAN clusters are formed based on density and connectivity between data points, and there’s no mechanism to \"classify\" new data points into clusters.\n",
    "\n",
    "#### What’s happening in the code:\n",
    "In the absence of a predict() method for DBSCAN, `k-Nearest Neighbors (kNN)` is used as a workaround to label the test data:\n",
    "\n",
    "#### `kNN` is used to find the nearest neighbors of each test point from the training data.\n",
    "Each test data point is then assigned the cluster label of its nearest neighbor in the training data, which was assigned by DBSCAN.\n",
    "This is why you see NearestNeighbors used in the code. It’s a way to propagate the training labels from DBSCAN to the test data based on proximity.\n",
    "\n",
    "### NOW APPLY ON TEST DATASET:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Silhouette Score: 0.6087305032887591\n",
      "Test Calinski-Harabasz Score: 2761.613341003759\n"
     ]
    }
   ],
   "source": [
    "# Use NearestNeighbors to label the test data based on the training data\n",
    "# This approach assigns each test data point to the closest training data cluster\n",
    "nn = NearestNeighbors(n_neighbors=1)\n",
    "nn.fit(X_train)\n",
    "\n",
    "# Get the nearest neighbors for the test data\n",
    "distances, indices = nn.kneighbors(X_test)\n",
    "\n",
    "# Assign cluster labels to test data points based on the nearest neighbor in the training data\n",
    "test_labels = train_labels[indices.flatten()]\n",
    "\n",
    "# Evaluate clustering on the test partition (using the nearest neighbor-based labels)\n",
    "if len(set(test_labels)) > 1:  # Ensure there's more than one cluster\n",
    "    test_silhouette_avg = silhouette_score(X_test, test_labels)\n",
    "    print(f\"Test Silhouette Score: {test_silhouette_avg}\")\n",
    "\n",
    "    test_calinski_harabasz = calinski_harabasz_score(X_test, test_labels)\n",
    "    print(f\"Test Calinski-Harabasz Score: {test_calinski_harabasz}\")\n",
    "else:\n",
    "    print(\"Test data is assigned to a single cluster or no meaningful clusters.\")\n",
    "\n",
    "# Add cluster labels to the training and test DataFrames\n",
    "df_train = pd.DataFrame(X_train)\n",
    "df_test = pd.DataFrame(X_test)\n",
    "\n",
    "df_train['Cluster'] = train_labels\n",
    "df_test['Cluster'] = test_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Silhouette Score: 0.6087305032887591\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import the colormap library from matplotlib\n",
    "import matplotlib.cm as cm\n",
    "from matplotlib.ticker import AutoMinorLocator\n",
    "\n",
    "# Silhouette analysis (only if there are more than one cluster)\n",
    "if len(set(test_labels)) > 1:\n",
    "    silhouette_avg = silhouette_score(X_test, test_labels)\n",
    "    print(f\"Training Silhouette Score: {silhouette_avg}\")\n",
    "    \n",
    "    # Compute the silhouette scores for each sample\n",
    "    sample_silhouette_values = silhouette_samples(X_test, test_labels)\n",
    "    \n",
    "    # Create the silhouette plot\n",
    "    fig, ax = plt.subplots(1, 1)\n",
    "    fig.set_size_inches(10, 20)\n",
    "\n",
    "    y_lower = 10\n",
    "    n_clusters = len(set(test_labels)) - (1 if -1 in test_labels else 0)  # Exclude the noise cluster if it exists\n",
    "    for i in range(n_clusters):\n",
    "        # Aggregate the silhouette scores for samples in the current cluster\n",
    "        ith_cluster_silhouette_values = sample_silhouette_values[test_labels == i]\n",
    "        ith_cluster_silhouette_values.sort()\n",
    "\n",
    "        size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
    "        y_upper = y_lower + size_cluster_i\n",
    "\n",
    "        color = cm.nipy_spectral(float(i) / n_clusters)\n",
    "        ax.fill_betweenx(np.arange(y_lower, y_upper),\n",
    "                         0, ith_cluster_silhouette_values,\n",
    "                         facecolor=color, edgecolor=color, alpha=0.7)\n",
    "\n",
    "        # Label the silhouette plots with cluster numbers in the middle\n",
    "        ax.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
    "\n",
    "        # Compute the new y_lower for next plot\n",
    "        y_lower = y_upper + 10  # 10 for the 0 samples gap between silhouettes\n",
    "\n",
    "    ax.set_title(\"Silhouette Plot for DBSCAN Clustering (Training Data)\")\n",
    "    ax.set_xlabel(\"Silhouette Coefficient Values\")\n",
    "    ax.set_ylabel(\"Cluster Label\")\n",
    "\n",
    "    # The vertical line for average silhouette score of all the values\n",
    "    ax.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n",
    "\n",
    "    # Major and minor ticks for the x-axis\n",
    "    ax.set_xticks([-1, -0.5, 0, 0.5, 1])  # Major ticks for x-axis\n",
    "    ax.xaxis.set_minor_locator(AutoMinorLocator(2))  # Add minor ticks for x-axis\n",
    "\n",
    "    # Major and minor ticks for the y-axis\n",
    "    ax.yaxis.set_minor_locator(AutoMinorLocator(2))  # Add minor ticks for y-axis\n",
    "    ax.minorticks_on()  # Enable minor ticks on both axes\n",
    "\n",
    "    # Display the plot\n",
    "    plt.show()\n",
    "\n",
    "else:\n",
    "    print(\"DBSCAN found only one cluster or no meaningful clusters.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Practice Problem on Clustering: [Duration: 30 mins]\n",
    "\n",
    "Perform k-means clustering on the handwritten digits dataset provided to you. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# digits: 10; # samples: 1797; # features 64\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "from sklearn.datasets import load_digits\n",
    "\n",
    "data, labels = load_digits(return_X_y=True)\n",
    "(n_samples, n_features), n_digits = data.shape, np.unique(labels).size\n",
    "\n",
    "print(f\"# digits: {n_digits}; # samples: {n_samples}; # features {n_features}\")"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
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 },
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