Machine_Learning/Customer Clust - Customer Segmentation Tool/Customer Clust - Customer Segmentation Tool.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "21941ceb-179f-47d8-abdb-944260365c5c",
   "metadata": {},
   "source": [
    "# Customer Clust - Customer Segmentation Tool"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81f2d0ac-24b7-4818-ba0a-79f327ffbbd3",
   "metadata": {},
   "source": [
    "## Aim\n",
    "\n",
    "The primary aim of the Customer Clust project is to develop a robust customer segmentation tool that leverages data analytics and machine learning techniques to categorize customers into distinct groups based on their purchasing behavior, preferences, and demographic characteristics. By achieving this aim, the project seeks to:\n",
    "\n",
    "- **Enhance Marketing Strategies**: Enable businesses to tailor their marketing efforts to specific customer segments, resulting in more effective and personalized campaigns.\n",
    "- **Improve Customer Understanding**: Provide insights into customer behavior and preferences, allowing businesses to better understand their target audience.\n",
    "- **Optimize Resource Allocation**: Help businesses allocate resources more efficiently by focusing on high-value customer segments.\n",
    "- **Drive Business Growth**: Facilitate data-driven decision-making to enhance customer satisfaction, loyalty, and ultimately increase sales and profitability.\n",
    "- **Foster Data-Driven Culture**: Encourage organizations to adopt data-driven approaches for their marketing and customer relationship management efforts, leading to improved overall performance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95bd9466-b14e-443a-804e-6198c5fd6d51",
   "metadata": {},
   "source": [
    "## Brief Explanation\n",
    "\n",
    "The Customer Clust project aims to develop a customer segmentation tool that leverages data analytics and machine learning to categorize customers based on their purchasing behavior and demographic characteristics. The primary objective is to enhance marketing strategies by allowing businesses to tailor their campaigns for specific customer segments, leading to more effective and personalized outreach. Additionally, the project seeks to improve customer understanding by providing valuable insights into preferences and behaviors, which can help organizations better connect with their target audience. \n",
    "\n",
    "By optimizing resource allocation and focusing on high-value segments, businesses can drive growth through data-driven decision-making that enhances customer satisfaction and loyalty. Ultimately, Customer Clust aspires to foster a data-driven culture within organizations, empowering them with actionable insights that significantly impact their success in the marketplace."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b925ca53-87b0-4f14-90ab-26342ee81955",
   "metadata": {},
   "source": [
    "## 1. **Install Required Libraries**: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e20fc9d1-8485-4ce5-be30-f54f64fd1a76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\users\\asus\\anaconda3\\lib\\site-packages (2.1.4)\n",
      "Requirement already satisfied: numpy in c:\\users\\asus\\anaconda3\\lib\\site-packages (1.26.4)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\asus\\anaconda3\\lib\\site-packages (3.8.0)\n",
      "Requirement already satisfied: seaborn in c:\\users\\asus\\anaconda3\\lib\\site-packages (0.12.2)\n",
      "Requirement already satisfied: scikit-learn in c:\\users\\asus\\anaconda3\\lib\\site-packages (1.2.2)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from pandas) (2023.3.post1)\n",
      "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from pandas) (2023.3)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (4.25.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (23.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (10.2.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (3.0.9)\n",
      "Requirement already satisfied: scipy>=1.3.2 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from scikit-learn) (1.11.4)\n",
      "Requirement already satisfied: joblib>=1.1.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from scikit-learn) (1.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from scikit-learn) (2.2.0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pandas numpy matplotlib seaborn scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "418a026e-efa3-42bd-800d-81b52304be5f",
   "metadata": {},
   "source": [
    "## 2.**Data Collection**: \n",
    "   - Collect data from various sources, such as sales records, customer feedback, and demographic information. A sample dataset can be found on platforms like Kaggle.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b18800ed-b32d-4469-8457-fecafd7a88c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   CustomerID   Genre  Age  Annual Income (k$)  Spending Score (1-100)\n",
      "0           1    Male   19                  15                      39\n",
      "1           2    Male   21                  15                      81\n",
      "2           3  Female   20                  16                       6\n",
      "3           4  Female   23                  16                      77\n",
      "4           5  Female   31                  17                      40\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load Data\n",
    "data = pd.read_csv(r'C:\\Users\\ASUS\\Downloads\\Mall_Customers.csv')\n",
    "\n",
    "# Display the first few rows of the dataset\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07306a92-3734-4596-b4f5-9feea059b992",
   "metadata": {},
   "source": [
    "## 3.**Data Preprocessing**: \n",
    "   - Clean and preprocess the data to handle missing values, outliers, and categorical variables.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2808e997-f9b0-49a9-9150-aa080f4ceb86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CustomerID                0\n",
      "Genre                     0\n",
      "Age                       0\n",
      "Annual Income (k$)        0\n",
      "Spending Score (1-100)    0\n",
      "dtype: int64\n",
      "              Age  Annual Income (k$)  Spending Score (1-100)\n",
      "count  200.000000          200.000000              200.000000\n",
      "mean    38.850000           60.560000               50.200000\n",
      "std     13.969007           26.264721               25.823522\n",
      "min     18.000000           15.000000                1.000000\n",
      "25%     28.750000           41.500000               34.750000\n",
      "50%     36.000000           61.500000               50.000000\n",
      "75%     49.000000           78.000000               73.000000\n",
      "max     70.000000          137.000000               99.000000\n"
     ]
    }
   ],
   "source": [
    "# Check for missing values\n",
    "print(data.isnull().sum())\n",
    "\n",
    "# Select relevant features for segmentation\n",
    "features = ['Age', 'Annual Income (k$)', 'Spending Score (1-100)']\n",
    "X = data[features]\n",
    "\n",
    "# Display the selected features\n",
    "print(X.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24c2c98e-b02a-46e1-bd54-485fb99a88c1",
   "metadata": {},
   "source": [
    "## 4. **Data Normalization**\n",
    "Next, we’ll normalize the data to ensure all features are on the same scale, which is crucial for distance-based algorithms like K-means..\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ae12ddfd-d7a2-44fe-b567-131f5956491a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.42456879 -1.73899919 -0.43480148]\n",
      " [-1.28103541 -1.73899919  1.19570407]\n",
      " [-1.3528021  -1.70082976 -1.71591298]\n",
      " [-1.13750203 -1.70082976  1.04041783]\n",
      " [-0.56336851 -1.66266033 -0.39597992]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Normalize data\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "# Display the scaled features\n",
    "print(X_scaled[:5])  # Show the first five rows of scaled data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b545995d-82fb-4f76-969d-7209c9f4977d",
   "metadata": {},
   "source": [
    "## 5. **Determine the Optimal Number of Clusters**\n",
    "We will use the Elbow Method to find the optimal number of clusters for our K-means algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b05aec3b-d9cd-42d1-aa99-dfa95bf4aae6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# Step 1: Determine the optimal number of clusters (Elbow Method)\n",
    "inertia = []\n",
    "K = range(1, 11)\n",
    "for k in K:\n",
    "    kmeans = KMeans(n_clusters=k, random_state=0)\n",
    "    kmeans.fit(X_scaled)\n",
    "    inertia.append(kmeans.inertia_)\n",
    "\n",
    "# Plotting the elbow graph\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(K, inertia, 'bo-')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('Inertia')\n",
    "plt.title('Elbow Method for Optimal k')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22d10a29-f7db-4064-b1b1-5d19fdf31706",
   "metadata": {},
   "source": [
    "## 6. **Fit K-means Model**\n",
    "Now we will fit the K-means model using the optimal number of clusters determined in the previous step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9cbcbff1-2632-4990-8fd3-6d3a80bb793a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   CustomerID   Genre  Age  Annual Income (k$)  Spending Score (1-100)  \\\n",
      "0           1    Male   19                  15                      39   \n",
      "1           2    Male   21                  15                      81   \n",
      "2           3  Female   20                  16                       6   \n",
      "3           4  Female   23                  16                      77   \n",
      "4           5  Female   31                  17                      40   \n",
      "\n",
      "   Cluster  \n",
      "0        1  \n",
      "1        1  \n",
      "2        1  \n",
      "3        1  \n",
      "4        1  \n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# Replace with the optimal k found from the elbow method (let’s assume it's 4 for this example)\n",
    "optimal_k = 4  \n",
    "kmeans = KMeans(n_clusters=optimal_k, random_state=0)\n",
    "clusters = kmeans.fit_predict(X_scaled)\n",
    "\n",
    "# Add cluster labels to original data\n",
    "data['Cluster'] = clusters\n",
    "\n",
    "# Display the updated data with cluster labels\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91cae6ec-f759-4076-8e15-33ae728b232a",
   "metadata": {},
   "source": [
    "## 7. **Visualization**: \n",
    "   - Visualize the segments using plots and graphs for better understanding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b1b5be95-c9cf-47b4-9021-ee4f6e817b10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "import seaborn as sns\n",
    "\n",
    "# Reduce dimensions for visualization\n",
    "pca = PCA(n_components=2)\n",
    "X_pca = pca.fit_transform(X_scaled)\n",
    "\n",
    "# Plot the clusters\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x=X_pca[:, 0], y=X_pca[:, 1], hue=data['Cluster'], palette='viridis', s=100)\n",
    "plt.title('Customer Segmentation using K-means Clustering')\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ead1683-9bc1-4b15-b584-dd401b5750f4",
   "metadata": {},
   "source": [
    "## 8. **Deployment**: \n",
    "   - Deploy the tool using a web framework like Flask or Django for easy access and use.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "87457cf4-3c8e-487e-86f5-1858e94f8419",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 0 characteristics:\n",
      "       CustomerID        Age  Annual Income (k$)  Spending Score (1-100)  \\\n",
      "count    40.00000  40.000000           40.000000               40.000000   \n",
      "mean    161.02500  32.875000           86.100000               81.525000   \n",
      "std      23.33863   3.857643           16.339036                9.999968   \n",
      "min     123.00000  27.000000           69.000000               58.000000   \n",
      "25%     141.50000  30.000000           74.750000               74.000000   \n",
      "50%     161.00000  32.000000           78.500000               83.000000   \n",
      "75%     180.50000  36.000000           94.000000               90.000000   \n",
      "max     200.00000  40.000000          137.000000               97.000000   \n",
      "\n",
      "       Cluster  \n",
      "count     40.0  \n",
      "mean       0.0  \n",
      "std        0.0  \n",
      "min        0.0  \n",
      "25%        0.0  \n",
      "50%        0.0  \n",
      "75%        0.0  \n",
      "max        0.0  \n",
      "\n",
      "Cluster 1 characteristics:\n",
      "       CustomerID        Age  Annual Income (k$)  Spending Score (1-100)  \\\n",
      "count   57.000000  57.000000           57.000000               57.000000   \n",
      "mean    53.438596  25.438596           40.000000               60.298246   \n",
      "std     36.936730   5.707193           17.031483               18.434212   \n",
      "min      1.000000  18.000000           15.000000                6.000000   \n",
      "25%     21.000000  21.000000           24.000000               48.000000   \n",
      "50%     48.000000  24.000000           40.000000               56.000000   \n",
      "75%     88.000000  31.000000           57.000000               73.000000   \n",
      "max    121.000000  38.000000           67.000000               99.000000   \n",
      "\n",
      "       Cluster  \n",
      "count     57.0  \n",
      "mean       1.0  \n",
      "std        0.0  \n",
      "min        1.0  \n",
      "25%        1.0  \n",
      "50%        1.0  \n",
      "75%        1.0  \n",
      "max        1.0  \n",
      "\n",
      "Cluster 2 characteristics:\n",
      "       CustomerID        Age  Annual Income (k$)  Spending Score (1-100)  \\\n",
      "count   38.000000  38.000000           38.000000               38.000000   \n",
      "mean   160.552632  39.368421           86.500000               19.578947   \n",
      "std     23.885648  10.617225           16.761845               11.684204   \n",
      "min    113.000000  19.000000           64.000000                1.000000   \n",
      "25%    141.500000  34.000000           75.250000               10.500000   \n",
      "50%    161.000000  40.500000           79.500000               17.000000   \n",
      "75%    180.500000  46.750000           96.000000               27.750000   \n",
      "max    199.000000  59.000000          137.000000               42.000000   \n",
      "\n",
      "       Cluster  \n",
      "count     38.0  \n",
      "mean       2.0  \n",
      "std        0.0  \n",
      "min        2.0  \n",
      "25%        2.0  \n",
      "50%        2.0  \n",
      "75%        2.0  \n",
      "max        2.0  \n",
      "\n",
      "Cluster 3 characteristics:\n",
      "       CustomerID        Age  Annual Income (k$)  Spending Score (1-100)  \\\n",
      "count   65.000000  65.000000           65.000000               65.000000   \n",
      "mean    69.415385  53.984615           47.707692               39.969231   \n",
      "std     34.295541   9.418221           14.648723               16.405953   \n",
      "min      7.000000  35.000000           18.000000                3.000000   \n",
      "25%     43.000000  48.000000           39.000000               32.000000   \n",
      "50%     71.000000  52.000000           49.000000               46.000000   \n",
      "75%     94.000000  63.000000           60.000000               51.000000   \n",
      "max    161.000000  70.000000           79.000000               60.000000   \n",
      "\n",
      "       Cluster  \n",
      "count     65.0  \n",
      "mean       3.0  \n",
      "std        0.0  \n",
      "min        3.0  \n",
      "25%        3.0  \n",
      "50%        3.0  \n",
      "75%        3.0  \n",
      "max        3.0  \n"
     ]
    }
   ],
   "source": [
    "# Analyze Cluster Characteristics\n",
    "for i in range(optimal_k):\n",
    "    print(f\"\\nCluster {i} characteristics:\")\n",
    "    print(data[data['Cluster'] == i].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1e089fc9-c630-49cd-9a5e-4fd65da4804e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the resulting data with clusters\n",
    "data.to_csv('customer_segments.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2030cf5e-c98a-445b-a2dd-9eab8610145b",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "By following the steps outlined in this project,we created a customer segmentation tool using K-means clustering in Python. The tool will help businesses better understand their customers and optimize their marketing strategies based on the segments identified. we can further explore the dataset, experiment with different clustering algorithms, or apply this approach to other datasets to enhance the analytical capabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64dcc0af-9afd-4ccf-9957-6e6b5b01716a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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