customer_browser_analysis

ecommerce_analysis_notebook.ipynb

{"cells":[{"cell_type":"markdown","metadata":{},"source":["<a href=\"https://colab.research.google.com/gist/stevengonsalvez/48b7bf8ba6f98d2895899e2d7d95a255/ecommerce_analysis_notebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["!pip install dask[complete]\n","!pip install \"dask[complete]\"\n","!pip install bokeh==2.4.2\n","!pip install numpy\n","!pip install sklearn\n","!pip install scikit-learn\n","!pip install scipy\n","!pip install pandas matplotlib seaborn\n"]},{"cell_type":"code","execution_count":17,"metadata":{},"outputs":[{"data":{"text/html":["\n","    <div class=\"bk-root\">\n","        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n","        <span id=\"1424\">Loading BokehJS ...</span>\n","    </div>"]},"metadata":{},"output_type":"display_data"},{"data":{"application/javascript":"\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  const JS_MIME_TYPE = 'application/javascript';\n  const HTML_MIME_TYPE = 'text/html';\n  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n  const CLASS_NAME = 'output_bokeh rendered_html';\n\n  /**\n   * Render data to the DOM node\n   */\n  function render(props, node) {\n    const script = document.createElement(\"script\");\n    node.appendChild(script);\n  }\n\n  /**\n   * Handle when an output is cleared or removed\n   */\n  function handleClearOutput(event, handle) {\n    const cell = handle.cell;\n\n    const id = cell.output_area._bokeh_element_id;\n    const server_id = cell.output_area._bokeh_server_id;\n    // Clean up Bokeh references\n    if (id != null && id in Bokeh.index) {\n      Bokeh.index[id].model.document.clear();\n      delete Bokeh.index[id];\n    }\n\n    if (server_id !== undefined) {\n      // Clean up Bokeh references\n      const cmd_clean = \"from bokeh.io.state import curstate; 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document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.2.min.js\"];\n  const css_urls = [];\n  \n\n  const inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      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ColumnDataSource\n","\n","from bokeh.layouts import gridplot\n","from bokeh.palettes import Viridis256\n","import dask.dataframe as dd\n","from dask.diagnostics import ProgressBar\n","from bokeh.plotting import figure, show, output_notebook\n","from bokeh.models import ColumnDataSource\n","from sklearn.preprocessing import StandardScaler\n","from sklearn.cluster import KMeans\n","from scipy.stats import gaussian_kde\n","import numpy as np\n","\n","# Initialize output for notebook\n","output_notebook()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[],"source":["def load_and_prepare_data(filename):\n","    \"\"\"\n","    Load the dataset and perform basic preparation and cleaning.\n","    \"\"\"\n","    ddf = dd.read_csv(filename, parse_dates=['timestamp'])\n","    # Print columns for debugging\n","    print(\"Columns in the dataset:\", ddf.columns)\n","    # Ensure 'event_type' column exists\n","    assert 'event_type' in ddf.columns, \"'event_type' column not found in the dataset.\"\n","    ddf = ddf.dropna()\n","    ddf['hour'] = ddf['timestamp'].dt.hour\n","    return ddf"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[],"source":["def user_behavior_analysis(ddf):\n","    activity_by_hour = ddf.groupby('hour').size().compute()\n","    \n","    source = ColumnDataSource(data=dict(hour=activity_by_hour.index, counts=activity_by_hour.values))\n","    \n","    p = figure(x_range=(0, 23), height=250, title=\"User Activity by Hour\",\n","               toolbar_location=None, tools=\"\")\n","    \n","    p.vbar(x='hour', top='counts', width=0.9, source=source)\n","    \n","    p.xgrid.grid_line_color = None\n","    p.y_range.start = 0\n","    \n","    show(p)"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[],"source":["def product_category_performance(ddf):\n","    top_categories = ddf[ddf['event_type'] == 'view']['category_string'].value_counts().nlargest(5).compute()\n","    print(top_categories)"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[],"source":["def pricing_strategy_insights(ddf):\n","    average_price_by_category = ddf.groupby('category_string')['price'].mean().compute()\n","    average_price_by_category = average_price_by_category.sort_values(ascending=False)[:10]\n","\n","    source = ColumnDataSource(data=dict(categories=average_price_by_category.index, avg_price=average_price_by_category.values))\n","    \n","    p = figure(x_range=average_price_by_category.index.tolist(), height=250, title=\"Average Price by Category\",\n","               toolbar_location=None, tools=\"\")\n","\n","    p.vbar(x='categories', top='avg_price', width=0.9, source=source)\n","\n","    p.xgrid.grid_line_color = None\n","    p.xaxis.major_label_orientation = \"vertical\"\n","    p.y_range.start = 0\n","\n","    show(p)"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[],"source":["def brand_analysis(ddf):\n","    brand_popularity = ddf[ddf['event_type'] == 'view']['brand'].value_counts().nlargest(10).compute()\n","\n","    source = ColumnDataSource(data=dict(brands=brand_popularity.index, views=brand_popularity.values))\n","    \n","    p = figure(x_range=brand_popularity.index.tolist(), height=250, title=\"Brand Popularity (Views)\",\n","               toolbar_location=None, tools=\"\")\n","\n","    p.vbar(x='brands', top='views', width=0.9, source=source)\n","\n","    p.xgrid.grid_line_color = None\n","    p.xaxis.major_label_orientation = \"vertical\"\n","    p.y_range.start = 0\n","\n","    show(p)"]},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[],"source":["# %%\n","def segment_users_and_visualize_kde(ddf, feature_to_visualize='price', n_clusters=3):\n","    \"\"\"\n","    Segment users based on interaction patterns and visualize the distribution of a selected feature across segments\n","    using Kernel Density Estimation.\n","    \n","    :param ddf: Dask DataFrame with the ecommerce interactions\n","    :param feature_to_visualize: String name of the feature to visualize with KDE\n","    :param n_clusters: Number of clusters to use for segmentation\n","    \"\"\"\n","    # Ensure the feature exists in the dataframe\n","    assert feature_to_visualize in ddf.columns, f\"'{feature_to_visualize}' not found in the dataset.\"\n","    \n","    # Convert Dask DataFrame to Pandas DataFrame for clustering\n","    pdf = ddf.compute()\n","    \n","    # Assuming clustering based on the selected feature for simplicity\n","    X = pdf[[feature_to_visualize]].dropna().values\n","    X_scaled = StandardScaler().fit_transform(X)\n","    \n","    # Clustering\n","    kmeans = KMeans(n_clusters=n_clusters, random_state=42)\n","    clusters = kmeans.fit_predict(X_scaled)\n","    pdf['cluster'] = clusters\n","    \n","    # Prepare the KDE plot\n","    p = figure(title=f'KDE of {feature_to_visualize} Across Clusters', tools=\"\", background_fill_color=\"#fafafa\")\n","    \n","    # Color palette for clusters\n","    palette = Viridis256[0:len(Viridis256):int(len(Viridis256)/n_clusters)]\n","    \n","    for i, color in zip(range(n_clusters), palette):\n","        subset = pdf[pdf['cluster'] == i][feature_to_visualize].values\n","        if len(subset) > 1:  # KDE requires more than one data point\n","            # Calculate the KDE\n","            kde = gaussian_kde(subset)\n","            x_range = np.linspace(subset.min(), subset.max(), 1000)\n","            y = kde(x_range)\n","            p.line(x_range, y, legend_label=f'Cluster {i}', line_width=2, color=color)\n","    \n","    p.legend.location = \"top_right\"\n","    p.legend.title = 'Clusters'\n","    p.xaxis.axis_label = feature_to_visualize\n","    p.yaxis.axis_label = 'Density'\n","    \n","    show(p)\n"]},{"cell_type":"markdown","metadata":{},"source":["# Main execution\n","Replace 'dataset.csv' with the path to your actual dataset file."]},{"cell_type":"code","execution_count":18,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Columns in the dataset: Index(['timestamp', 'event_type', 'product_id', 'category_id',\n","       'category_string', 'brand', 'price', 'user_id', 'user_session_id'],\n","      dtype='object')\n","Total number of records: 26560620.0\n"]}],"source":["# Example: Count total records (executes computation)\n","ddf = load_and_prepare_data('dataset.csv')\n","\n","total_records = ddf.shape[0].compute()\n","print(f\"Total number of records: {total_records}\")"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Columns in the dataset: Index(['timestamp', 'event_type', 'product_id', 'category_id',\n","       'category_string', 'brand', 'price', 'user_id', 'user_session_id'],\n","      dtype='object')\n","[########################################] | 100% Completed | 98.37 s\n"]},{"data":{"text/html":["\n","\n","\n","\n","\n","\n","  <div class=\"bk-root\" id=\"0ab08981-757b-4c08-b36d-a9230385cc80\" data-root-id=\"1005\"></div>\n"]},"metadata":{},"output_type":"display_data"},{"data":{"application/javascript":"(function(root) {\n  function embed_document(root) {\n    \n  const docs_json = 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{\n      if (root.Bokeh !== undefined) {\n        clearInterval(timer);\n        embed_document(root);\n      } else {\n        attempts++;\n        if (attempts > 100) {\n          clearInterval(timer);\n          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n        }\n      }\n    }, 10, root)\n  }\n})(window);","application/vnd.bokehjs_exec.v0+json":""},"metadata":{"application/vnd.bokehjs_exec.v0+json":{"id":"1005"}},"output_type":"display_data"}],"source":["with ProgressBar():\n","    user_behavior_analysis(ddf)\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["with ProgressBar():   \n","    product_category_performance(ddf)\n","    "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["with ProgressBar():\n","    pricing_strategy_insights(ddf)\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["with ProgressBar():\n","    brand_analysis(ddf)"]},{"cell_type":"code","execution_count":16,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["[########################################] | 100% Completed | 98.19 s\n"]},{"data":{"text/html":["\n","\n","\n","\n","\n","\n","  <div class=\"bk-root\" id=\"24dd5bec-ddc9-4816-b4f0-bcb591b6bd62\" data-root-id=\"1249\"></div>\n"]},"metadata":{},"output_type":"display_data"},{"data":{"application/javascript":"(function(root) {\n  function embed_document(root) {\n    \n  const docs_json = 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