2_6_10_Crime_Hotspot_Prediction.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# 🔍 Crime Data Clustering Project Overview\n",
        "\n",
        "## 📖 Project Description\n",
        "This project aims to analyze crime data using K-Means clustering to identify crime hotspots based on geographical coordinates (latitude and longitude). The dataset is pre-processed, explored, and visualized to gain insights into crime patterns.\n",
        "\n",
        "## 📊 Steps Overview\n",
        "\n",
        "### 1. 📥 Data Loading\n",
        "- Load the crime dataset from the specified path into a DataFrame for manipulation and analysis.\n",
        "\n",
        "### 2. 🔍 Data Exploration\n",
        "- Display the first five rows, dataset information, summary statistics, and check for missing values.\n",
        "- Visualize the correlation between numeric features using a heatmap.\n",
        "- Generate histograms for all numeric features to understand their distributions.\n",
        "- Create scatter plots to visualize the geographical distribution of crimes.\n",
        "\n",
        "### 3. ⚙️ Data Preparation\n",
        "- Select relevant features (latitude and longitude).\n",
        "- Scale the features using `StandardScaler`.\n",
        "- Perform dimensionality reduction using PCA for better clustering performance.\n",
        "\n",
        "### 4. 🔑 K-Means Clustering\n",
        "- Evaluate different numbers of clusters (2-10) and calculate silhouette scores for each.\n",
        "- Identify the optimal number of clusters based on the highest silhouette score.\n",
        "- Conduct final K-Means clustering and assign cluster labels to the original dataset.\n",
        "\n",
        "### 5. 📈 Visualization of Results\n",
        "- Visualize the clustering results on a scatter plot showing crime hotspots based on the identified clusters.\n",
        "\n",
        "### 6. 💾 Save Clustered Data\n",
        "- Save the clustered data along with the assigned cluster labels to a CSV file for future reference.\n",
        "\n",
        "### 7. 🛠 Model Deployment (Optional)\n",
        "- Save the trained K-Means model for deployment and future predictions.\n",
        "\n",
        "## 📁 Outputs\n",
        "- Data exploration results saved as a CSV file.\n",
        "- Clustered data exported for further analysis.\n",
        "- Visualization plots saved as PDF files.\n",
        "- Trained K-Means model saved for potential deployment.\n",
        "\n",
        "## 📅 Conclusion\n",
        "The project successfully clusters crime data, revealing significant patterns and potential hotspots that can inform law enforcement strategies and resource allocation."
      ],
      "metadata": {
        "id": "qwll-iKLuoRm"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "N0DWS_6rurvv"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HoU6qM7Turz5"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qenwsu1Wur66"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📚 Step 1: Import Libraries\n",
        "\n",
        "In this initial step, we import all the necessary libraries for data analysis and clustering. The libraries include:\n",
        "\n",
        "- **Pandas**: For data manipulation and analysis.\n",
        "- **NumPy**: For numerical computations.\n",
        "- **Matplotlib**: For creating visualizations.\n",
        "- **Seaborn**: For statistical data visualization.\n",
        "- **Scikit-learn**: For implementing machine learning algorithms, including K-Means clustering.\n",
        "- **PCA**: To reduce dimensionality and enhance the clustering process.\n",
        "\n",
        "Ensuring that all libraries are correctly imported is crucial for the seamless execution of the subsequent steps."
      ],
      "metadata": {
        "id": "KDfVuwcgvv9U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the required libraries"
      ],
      "metadata": {
        "id": "R-Ec-zf1ukcp"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd  # Import pandas library for data manipulation"
      ],
      "metadata": {
        "id": "heLsAcf1pfnc"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np  # Import numpy for numerical operations"
      ],
      "metadata": {
        "id": "T0FPkRLeqAfm"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt  # Import matplotlib for plotting"
      ],
      "metadata": {
        "id": "knbKbLcCqAid"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns  # Import seaborn for advanced visualizations"
      ],
      "metadata": {
        "id": "_QztcRxjqAlE"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler  # Import StandardScaler for feature scaling"
      ],
      "metadata": {
        "id": "AbhhZP4bqAp-"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA  # Import PCA for dimensionality reduction"
      ],
      "metadata": {
        "id": "CUbvLPo2qAwc"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans  # Import KMeans for clustering"
      ],
      "metadata": {
        "id": "mQgcBteXqAzK"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import silhouette_score  # Import silhouette_score for evaluating clustering"
      ],
      "metadata": {
        "id": "6o0nq2emqkm1"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib  # Import joblib for saving models"
      ],
      "metadata": {
        "id": "5cGT3OJTqkp-"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CvLY1WRSqkst"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pdswOK1oqkxh"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WW6_9yrtqk0z"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📥 Step 2: Load the Dataset\n",
        "\n",
        "This step involves loading the crime dataset from the specified file path into a Pandas DataFrame. The following actions are performed:\n",
        "\n",
        "- **Read the Data**: Utilize `pd.read_csv()` to read the CSV file into a DataFrame.\n",
        "- **Inspect the Data**: Display the first few rows of the dataset using `head()` to understand its structure and the nature of the data.\n",
        "\n",
        "Confirming the successful loading of the dataset is essential for proceeding with data exploration and analysis."
      ],
      "metadata": {
        "id": "ewpFTTgeuxco"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  Load the dataset"
      ],
      "metadata": {
        "id": "xbQbFp3Dqk3n"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_path = \"/content/small_crime_subset.csv\"  # Path to the crime dataset"
      ],
      "metadata": {
        "id": "XUa8VhAlqk6y"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "crime_data = pd.read_csv(dataset_path)  # Load the dataset into a DataFrame"
      ],
      "metadata": {
        "id": "PReMZrvhqA06"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4ytygn77qA5Y"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7m4ikDr2qA7x"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "c388AxWCqBAl"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🔍 Step 3: Explore the Data\n",
        "\n",
        "Data exploration is conducted to gain insights into the dataset's structure and content. Key activities include:\n",
        "\n",
        "- **Dataset Information**: Use `info()` to check the data types and non-null counts of each column.\n",
        "- **Summary Statistics**: Generate descriptive statistics with `describe()` to understand the distribution of numeric features.\n",
        "- **Missing Values**: Identify any missing values in the dataset to ensure data integrity.\n",
        "- **Visual Correlation**: Create a heatmap to visualize correlations among numeric features, aiding in identifying significant relationships.\n",
        "- **Distributions**: Generate histograms for numeric features to analyze their distributions.\n",
        "- **Geographical Distribution**: Use scatter plots to visualize the geographical distribution of crimes based on latitude and longitude.\n",
        "\n",
        "These exploratory steps are vital for understanding the dataset before performing any clustering."
      ],
      "metadata": {
        "id": "XfxJ1lK7u0Tv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  Data Exploration"
      ],
      "metadata": {
        "id": "OMDZOutEqBDT"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"First 5 rows of the dataset:\")  # Print a message indicating the following output\n",
        "print(crime_data.head())  # Display the first 5 rows of the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5igCGbCEqBHd",
        "outputId": "b6062c6d-63c3-4754-9c57-7f6d97046828"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First 5 rows of the dataset:\n",
            "      CASE#     DATE  OF OCCURRENCE                  BLOCK  IUCR  \\\n",
            "0  JH180578  03/07/2024 06:38:00 PM  021XX S PRINCETON AVE  1320   \n",
            "1  JH179103  03/07/2024 02:10:00 PM   059XX N GLENWOOD AVE  0460   \n",
            "2  JH178557  03/07/2024 04:45:00 AM   026XX W ARMITAGE AVE  041A   \n",
            "3  JH179833  03/07/2024 07:00:00 PM        007XX W 74TH ST  0810   \n",
            "4  JH178450  03/07/2024 12:08:00 AM   022XX S TRUMBULL AVE  502P   \n",
            "\n",
            "   PRIMARY DESCRIPTION         SECONDARY DESCRIPTION  \\\n",
            "0      CRIMINAL DAMAGE                    TO VEHICLE   \n",
            "1              BATTERY                        SIMPLE   \n",
            "2              BATTERY          AGGRAVATED - HANDGUN   \n",
            "3                THEFT                     OVER $500   \n",
            "4        OTHER OFFENSE  FALSE / STOLEN / ALTERED TRP   \n",
            "\n",
            "        LOCATION DESCRIPTION ARREST DOMESTIC  BEAT  WARD FBI CD  X COORDINATE  \\\n",
            "0  NURSING / RETIREMENT HOME      N        N   914    11     14     1174265.0   \n",
            "1   SCHOOL - PUBLIC BUILDING      Y        N  2013    48    08B     1165910.0   \n",
            "2                     STREET      N        N  1421     1    04B     1158387.0   \n",
            "3                     STREET      N        N   732    17     06     1172767.0   \n",
            "4                     STREET      N        N  1024    22     26     1153715.0   \n",
            "\n",
            "   Y COORDINATE   LATITUDE  LONGITUDE                       LOCATION  \n",
            "0     1890173.0  41.854037 -87.635861  (41.854037308, -87.635861095)  \n",
            "1     1939379.0  41.989244 -87.665120  (41.989243623, -87.665119726)  \n",
            "2     1913184.0  41.917521 -87.693510  (41.917520732, -87.693509757)  \n",
            "3     1855810.0  41.759775 -87.642373  (41.759775035, -87.642373275)  \n",
            "4     1888947.0  41.851106 -87.711320  (41.851106306, -87.711320316)  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GT4X60eyq5d8"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3KrXGBWkq5h0"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nDataset info:\")  # Print a message indicating the following output\n",
        "print(crime_data.info())  # Display information about the dataset, including data types and non-null counts\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9eILVGu5q5lM",
        "outputId": "e09e647a-471f-4fe0-d707-8f837c76285a"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Dataset info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 50 entries, 0 to 49\n",
            "Data columns (total 17 columns):\n",
            " #   Column                  Non-Null Count  Dtype  \n",
            "---  ------                  --------------  -----  \n",
            " 0   CASE#                   50 non-null     object \n",
            " 1   DATE  OF OCCURRENCE     50 non-null     object \n",
            " 2   BLOCK                   50 non-null     object \n",
            " 3    IUCR                   50 non-null     object \n",
            " 4    PRIMARY DESCRIPTION    50 non-null     object \n",
            " 5    SECONDARY DESCRIPTION  50 non-null     object \n",
            " 6    LOCATION DESCRIPTION   50 non-null     object \n",
            " 7   ARREST                  50 non-null     object \n",
            " 8   DOMESTIC                50 non-null     object \n",
            " 9   BEAT                    50 non-null     int64  \n",
            " 10  WARD                    50 non-null     int64  \n",
            " 11  FBI CD                  50 non-null     object \n",
            " 12  X COORDINATE            50 non-null     float64\n",
            " 13  Y COORDINATE            50 non-null     float64\n",
            " 14  LATITUDE                50 non-null     float64\n",
            " 15  LONGITUDE               50 non-null     float64\n",
            " 16  LOCATION                50 non-null     object \n",
            "dtypes: float64(4), int64(2), object(11)\n",
            "memory usage: 6.8+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ni7e3jGxq5uV"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GilUH5r7q5ys"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nSummary statistics:\")  # Print a message indicating the following output\n",
        "print(crime_data.describe())  # Display summary statistics of numerical columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iazgYWx7q6Hs",
        "outputId": "2f029d30-7eca-4939-ce7d-4393f19a8fd5"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Summary statistics:\n",
            "              BEAT       WARD  X COORDINATE  Y COORDINATE   LATITUDE  \\\n",
            "count    50.000000  50.000000  5.000000e+01  5.000000e+01  50.000000   \n",
            "mean   1178.960000  22.040000  1.161428e+06  1.884564e+06  41.838908   \n",
            "std     611.673648  12.970862  1.597171e+04  3.077191e+04   0.084554   \n",
            "min     225.000000   1.000000  1.128612e+06  1.818023e+06  41.655821   \n",
            "25%     812.500000  13.250000  1.150716e+06  1.861276e+06  41.775016   \n",
            "50%    1018.000000  20.000000  1.162902e+06  1.889560e+06  41.852572   \n",
            "75%    1499.000000  34.500000  1.172751e+06  1.908327e+06  41.904146   \n",
            "max    2534.000000  48.000000  1.192969e+06  1.940820e+06  41.993158   \n",
            "\n",
            "       LONGITUDE  \n",
            "count  50.000000  \n",
            "mean  -87.683158  \n",
            "std     0.058320  \n",
            "min   -87.802452  \n",
            "25%   -87.722237  \n",
            "50%   -87.677696  \n",
            "75%   -87.642116  \n",
            "max   -87.568219  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xF_tewYKrhLE"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "e5BAzEGNrhPE"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nMissing values in each column:\")  # Print a message indicating the following output\n",
        "print(crime_data.isnull().sum())  # Display the count of missing values in each column"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dnT40YbNrhTu",
        "outputId": "3f49bb89-164d-4e77-b280-d4282d462364"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Missing values in each column:\n",
            "CASE#                     0\n",
            "DATE  OF OCCURRENCE       0\n",
            "BLOCK                     0\n",
            " IUCR                     0\n",
            " PRIMARY DESCRIPTION      0\n",
            " SECONDARY DESCRIPTION    0\n",
            " LOCATION DESCRIPTION     0\n",
            "ARREST                    0\n",
            "DOMESTIC                  0\n",
            "BEAT                      0\n",
            "WARD                      0\n",
            "FBI CD                    0\n",
            "X COORDINATE              0\n",
            "Y COORDINATE              0\n",
            "LATITUDE                  0\n",
            "LONGITUDE                 0\n",
            "LOCATION                  0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tS2B0NIPqBKM"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NNRxPn98qBMv"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6K-S6xqZqBOe"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualization: Correlation Heatmap"
      ],
      "metadata": {
        "id": "k8x_YeiRqBTj"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Select only numeric columns from the DataFrame"
      ],
      "metadata": {
        "id": "J66LtdW-qBXr"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_data = crime_data.select_dtypes(include=[np.number])"
      ],
      "metadata": {
        "id": "o-KphkOiqBbz"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KCVc_6vWqBef"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 8))  # Create a new figure for the heatmap\n",
        "sns.heatmap(numeric_data.corr(), annot=True, fmt=\".2f\", cmap='coolwarm')  # Plot the correlation heatmap\n",
        "plt.title('Correlation Heatmap')  # Set the title of the heatmap\n",
        "plt.savefig('/content/correlation_heatmap.pdf')  # Save the heatmap as a PDF file\n",
        "plt.show()  # Display the heatmap"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "WVm8GmG7qBgt",
        "outputId": "cc7fcd21-8beb-4087-c1e7-fafc6570304e"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-rhNynrZqBk1"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Kb_r9bDvqBnY"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualization: Histograms"
      ],
      "metadata": {
        "id": "8PDX8HC4qBrl"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "crime_data.hist(bins=30, figsize=(15, 10))  # Create histograms for all numeric features\n",
        "plt.suptitle('Histograms of Crime Data Features')  # Set the title for the histograms\n",
        "plt.savefig('/content/histograms.pdf')  # Save histograms as a PDF file\n",
        "plt.show()  # Display the histograms"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 631
        },
        "id": "RF1i6kpeqBx7",
        "outputId": "140ac7c4-aa44-43df-e123-e4c1d1e1b667"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OSvAYIDZqB0S"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HvyRymdqqB2Q"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualization: Scatter Plots for Latitude and Longitude"
      ],
      "metadata": {
        "id": "FGIpXI7xqB4o"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Create a new figure for the scatter plot\n",
        "plt.scatter(crime_data['LONGITUDE'], crime_data['LATITUDE'], alpha=0.5)  # Plot latitude vs longitude\n",
        "plt.title('Scatter Plot of Crime Locations')  # Set the title of the scatter plot\n",
        "plt.xlabel('Longitude')  # Label the x-axis\n",
        "plt.ylabel('Latitude')  # Label the y-axis\n",
        "plt.savefig('/content/scatter_plot_crime_locations.pdf')  # Save the scatter plot as a PDF file\n",
        "plt.show()  # Display the scatter plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 535
        },
        "id": "3G9XkCZ4qB68",
        "outputId": "6dcb0b06-6bb2-4099-bf76-28fa1e1594db"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DrgPYWGOsvHX"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "svB1osLdsvKp"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save data exploration results as CSV"
      ],
      "metadata": {
        "id": "wgLTTANrsvNk"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_exploration_path = \"/content/crime_data_exploration.csv\"  # Path to save exploration results"
      ],
      "metadata": {
        "id": "7HxRP3W2svQk"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "crime_data.describe().to_csv(data_exploration_path)  # Save summary statistics to a CSV file"
      ],
      "metadata": {
        "id": "naQrfdffsvTw"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OPZLSTBus2m9"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2R4hexWKs2qR"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bVB8dY9_s2ua"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ⚙️ Step 4: Data Preparation\n",
        "\n",
        "In this step, we prepare the data for clustering by performing the following actions:\n",
        "\n",
        "- **Feature Selection**: Choose relevant features (latitude and longitude) that will be used for clustering.\n",
        "- **Feature Scaling**: Normalize the selected features using `StandardScaler` to ensure that all features contribute equally to the distance calculations in clustering.\n",
        "- **Dimensionality Reduction**: Apply Principal Component Analysis (PCA) to reduce the number of features while preserving variance, enhancing clustering performance.\n",
        "\n",
        "Proper data preparation is critical for effective clustering and achieving meaningful results."
      ],
      "metadata": {
        "id": "ihrS7R4iu49X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Preparation"
      ],
      "metadata": {
        "id": "BgZEINfss2xk"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Selecting the relevant features (latitude and longitude)"
      ],
      "metadata": {
        "id": "M5dFir5ns21A"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "coordinates = crime_data[['LATITUDE', 'LONGITUDE']]  # Extract latitude and longitude columns for clustering\n"
      ],
      "metadata": {
        "id": "nRnDQI4FsvXB"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kgIqCaVjsvZ3"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature scaling"
      ],
      "metadata": {
        "id": "ByW3ExuRqB9X"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()  # Initialize StandardScaler"
      ],
      "metadata": {
        "id": "S3cVbn4TqB_R"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_coordinates = scaler.fit_transform(coordinates)  # Scale the coordinates to have mean 0 and variance 1\n"
      ],
      "metadata": {
        "id": "HfpAnF2XqCBt"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cjgNOpTnqCDl"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Dimensionality reduction using PCA"
      ],
      "metadata": {
        "id": "fVRQdC7PqCF8"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pca = PCA(n_components=2)  # Initialize PCA to reduce to 2 components"
      ],
      "metadata": {
        "id": "Rpz_b24nqCIT"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reduced_features = pca.fit_transform(scaled_coordinates)  # Apply PCA on the scaled coordinates"
      ],
      "metadata": {
        "id": "udaDs7yGqCMr"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "X-AZ1eVumEce"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hB94cbGBmEe7"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "G0tHyvf-mEhk"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🔑 Step 5: K-Means Clustering\n",
        "\n",
        "This step focuses on applying the K-Means clustering algorithm to the prepared data. Key components include:\n",
        "\n",
        "- **Evaluating Clusters**: Test different numbers of clusters (e.g., from 2 to 10) and calculate silhouette scores for each to assess the quality of the clusters.\n",
        "- **Optimal Clusters**: Identify the optimal number of clusters based on the silhouette scores, which indicates how well-separated the clusters are.\n",
        "- **Final Clustering**: Execute K-Means clustering using the optimal number of clusters and assign cluster labels to each data point.\n",
        "\n",
        "Clustering analysis helps in understanding crime patterns and identifying hotspots effectively."
      ],
      "metadata": {
        "id": "bI7odsZTvNz9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# K-Means Clustering"
      ],
      "metadata": {
        "id": "NlQ-sOQrmElb"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Try different numbers of clusters"
      ],
      "metadata": {
        "id": "_Bit-tEXmEpj"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "silhouette_scores = []  # Initialize a list to hold silhouette scores"
      ],
      "metadata": {
        "id": "7QNoRRVDlAaD"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n_clusters_range = range(2, 11)  # Define the range of clusters to try from 2 to 10"
      ],
      "metadata": {
        "id": "o6NXGxoilAc0"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "izncB8nYtY1f"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for n_clusters in n_clusters_range:  # Loop through the range of clusters\n",
        "    kmeans = KMeans(n_clusters=n_clusters, random_state=42)  # Initialize KMeans with the current number of clusters\n",
        "    cluster_labels = kmeans.fit_predict(reduced_features)  # Fit the model and predict cluster labels\n",
        "    silhouette_avg = silhouette_score(reduced_features, cluster_labels)  # Calculate the average silhouette score\n",
        "    silhouette_scores.append(silhouette_avg)  # Append the score to the list\n",
        "    print(f\"Number of clusters: {n_clusters}, Silhouette Score: {silhouette_avg:.4f}\")  # Print the number of clusters and score\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8IrJUp6LtY5I",
        "outputId": "d013e9a0-95f2-4122-e744-4fcf95d16d88"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of clusters: 2, Silhouette Score: 0.3758\n",
            "Number of clusters: 3, Silhouette Score: 0.4031\n",
            "Number of clusters: 4, Silhouette Score: 0.3902\n",
            "Number of clusters: 5, Silhouette Score: 0.4271\n",
            "Number of clusters: 6, Silhouette Score: 0.4460\n",
            "Number of clusters: 7, Silhouette Score: 0.5028\n",
            "Number of clusters: 8, Silhouette Score: 0.4452\n",
            "Number of clusters: 9, Silhouette Score: 0.4579\n",
            "Number of clusters: 10, Silhouette Score: 0.4737\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hcNf8BcttY8J"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2vKmgP_dtY-2"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot Silhouette Scores"
      ],
      "metadata": {
        "id": "ke51p14stZCG"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Create a new figure for the plot\n",
        "plt.plot(n_clusters_range, silhouette_scores, marker='o')  # Plot the silhouette scores against the number of clusters\n",
        "plt.title('Silhouette Scores for Different Numbers of Clusters')  # Set the title of the plot\n",
        "plt.xlabel('Number of Clusters')  # Label the x-axis\n",
        "plt.ylabel('Silhouette Score')  # Label the y-axis\n",
        "plt.xticks(n_clusters_range)  # Set x-ticks to the range of clusters\n",
        "plt.grid()  # Enable the grid on the plot\n",
        "plt.savefig('/content/silhouette_scores.pdf')  # Save the plot as a PDF file\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "7Zcfu0EBtZFj",
        "outputId": "5c7fed14-422d-4d71-839a-633286cd100e"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "o7kAet2KtZHp"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bu5BWjp-tZTO"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Choose the best number of clusters (the one with the highest silhouette score)"
      ],
      "metadata": {
        "id": "FLG5u4bClAfd"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "best_n_clusters = n_clusters_range[np.argmax(silhouette_scores)]  # Identify the best number of clusters\n"
      ],
      "metadata": {
        "id": "Z6EpciW7lAkK"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Best number of clusters: {best_n_clusters}\")  # Print the best number of clusters"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4usyqMltlAnc",
        "outputId": "364cd7fa-705b-47cb-f292-229720d6f620"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best number of clusters: 7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WTva6R4atr5Q"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ph4EUclFtr9V"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Final KMeans clustering"
      ],
      "metadata": {
        "id": "iQ0mR2xrtsAs"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_kmeans = KMeans(n_clusters=best_n_clusters, random_state=42)  # Initialize KMeans with the best number of clusters\n"
      ],
      "metadata": {
        "id": "3s7CGuY8tsEy"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "crime_data['Cluster'] = final_kmeans.fit_predict(reduced_features)  # Assign cluster labels to the original DataFrame\n"
      ],
      "metadata": {
        "id": "wHc3vblXtsHR"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "X4Vl-E5BtsLC"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate final silhouette score"
      ],
      "metadata": {
        "id": "8sbbzsWFtsQW"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_silhouette_score = silhouette_score(reduced_features, crime_data['Cluster'])  # Calculate silhouette score for the final clusters\n"
      ],
      "metadata": {
        "id": "UhhnhgEAt10u"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Final Silhouette Score: {final_silhouette_score:.4f}\")  # Print the final silhouette score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YI4R6Bdwt138",
        "outputId": "55c2b3f1-b0e5-4ee3-8179-e66d00fcd302"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Silhouette Score: 0.5028\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kZ5Z8zCft17Q"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ok7IX-b2t1-Z"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yrYRDjw9t2Av"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📈 Step 6: Visualize Results\n",
        "\n",
        "In this step, we visualize the results of the K-Means clustering to interpret the findings. Key activities include:\n",
        "\n",
        "- **Scatter Plot**: Create a scatter plot to represent the clustered data, highlighting different clusters to reveal crime hotspots visually.\n",
        "- **Cluster Characteristics**: Analyze the characteristics of each cluster to understand the factors contributing to crime patterns in various regions.\n",
        "\n",
        "Visualization aids in communicating insights derived from the clustering process effectively."
      ],
      "metadata": {
        "id": "iVd8qC0TvVPo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualization"
      ],
      "metadata": {
        "id": "M3exYYjyt9xu"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Create a new figure for the scatter plot\n",
        "plt.scatter(crime_data['LONGITUDE'], crime_data['LATITUDE'], c=crime_data['Cluster'], cmap='viridis', marker='o', edgecolor='k', alpha=0.7)  # Plot the clusters\n",
        "plt.title('Crime Hotspots')  # Set the title of the scatter plot\n",
        "plt.xlabel('Longitude')  # Label the x-axis\n",
        "plt.ylabel('Latitude')  # Label the y-axis\n",
        "plt.colorbar(label='Cluster ID')  # Add a colorbar indicating cluster IDs\n",
        "plt.savefig('/content/crime_hotspots.pdf')  # Save the plot as a PDF file\n",
        "plt.show()  # Display the plot\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "lAJWKB60t92_",
        "outputId": "d2df3049-98d3-4355-c684-d5a16c24d642"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5FvC1wQeuGWX"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hB0WsotnuIPZ"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yO3cO_rAuITA"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 💾 Step 7: Save Results\n",
        "\n",
        "The final step involves saving the clustered data and relevant results for future reference. Actions taken include:\n",
        "\n",
        "- **Export Clustered Data**: Save the DataFrame containing original data along with assigned cluster labels to a CSV file for later analysis.\n",
        "- **Save Visualizations**: Export visualizations (e.g., scatter plots) as PDF files for presentation or reporting.\n",
        "- **Model Persistence**: Optionally, save the trained K-Means model to facilitate future predictions without retraining.\n",
        "\n",
        "Properly saving results ensures accessibility for further analysis and application."
      ],
      "metadata": {
        "id": "E_X0u2gNvYtf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the clustered data to a CSV file"
      ],
      "metadata": {
        "id": "XvYHXsVSuIiG"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clustered_data_path = \"/content/clustered_crime_data.csv\"  # Path to save clustered data"
      ],
      "metadata": {
        "id": "ZIQQTZ9mt96V"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "crime_data.to_csv(clustered_data_path, index=False)  # Save the clustered DataFrame to a CSV file"
      ],
      "metadata": {
        "id": "Mbo-LoAtt992"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1bQ5L9IJuPoO"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SpMys1wSuPtI"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OtAkzzTMuPye"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🚀 Model Deployment Notes\n",
        "\n",
        "In this step, we focus on saving the trained K-Means model for future use. This process is essential for applying the model to new data without the need to retrain.\n",
        "\n",
        "- **Model Persistence**: Using the `joblib` library, we can efficiently serialize the K-Means model, ensuring that it retains its state and parameters.\n",
        "- **File Format**: The model is saved in a `.joblib` format, which is suitable for storing large numpy arrays, making it ideal for machine learning models.\n",
        "- **Deployment**: Once saved, the model can be easily loaded for making predictions on new datasets, facilitating real-time application in crime analysis.\n",
        "\n",
        "This deployment step enhances the project's usability and scalability, allowing stakeholders to leverage the insights generated by the clustering model."
      ],
      "metadata": {
        "id": "z2ltiSDovm81"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Model Deployment (optional)"
      ],
      "metadata": {
        "id": "RZulO57BuP82"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the KMeans model for deployment"
      ],
      "metadata": {
        "id": "K5Up7_gHuQBO"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_path = '/content/kmeans_model.joblib'  # Path to save the KMeans model"
      ],
      "metadata": {
        "id": "0tDMzffCuThn"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "joblib.dump(final_kmeans, model_path)  # Save the KMeans model to a file"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "faap3AQKuTkw",
        "outputId": "cc6de4db-e01b-4592-a909-3dbcb6f5de61"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/kmeans_model.joblib']"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "K4aXEHKIugHO"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "s9PCi8VzuhMP"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Project completed successfully!\")  # Print a success message\n",
        "print(f\"Data exploration saved at: {data_exploration_path}\")  # Print the path of saved exploration results\n",
        "print(f\"Clustered data saved at: {clustered_data_path}\")  # Print the path of saved clustered data\n",
        "print(f\"Hotspot plot saved at: /content/crime_hotspots.pdf\")  # Print the path of saved hotspot plot\n",
        "print(f\"K-Means model saved at: {model_path}\")  # Print the path of saved KMeans model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NN7oxDmtuhuO",
        "outputId": "cbab1e15-3ce9-4c88-943f-9f2b46bbc6a5"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Project completed successfully!\n",
            "Data exploration saved at: /content/crime_data_exploration.csv\n",
            "Clustered data saved at: /content/clustered_crime_data.csv\n",
            "Hotspot plot saved at: /content/crime_hotspots.pdf\n",
            "K-Means model saved at: /content/kmeans_model.joblib\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yKPjGMFxuToA"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ucKeLshPuQES"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "X6g9Y-yxvfJB"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📝 Conclusion\n",
        "\n",
        "In this project, we successfully performed K-Means clustering on the crime dataset to identify patterns and hotspots of criminal activity. The following key points summarize the outcomes:\n",
        "\n",
        "- **Data Exploration**: Initial exploration provided valuable insights into the dataset's structure and distribution of features.\n",
        "- **Clustering Insights**: K-Means clustering revealed distinct clusters, allowing us to identify areas with high crime rates and understand their characteristics.\n",
        "- **Actionable Insights**: The findings can guide law enforcement and policymakers in allocating resources and developing targeted crime prevention strategies.\n",
        "\n",
        "Future work may include refining the clustering approach, incorporating additional features, or applying different clustering algorithms to enhance insights further."
      ],
      "metadata": {
        "id": "P_0mNX32vfT5"
      }
    }
  ]
}