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analytics.ipynb 2,23 ko
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     "cells": [
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "# Analytics Module\n",
        "The Analytics module provides descriptive statistics on content data, evidence data and model evaluations "
       ]
      },
      {
       "cell_type": "code",
       "execution_count": 2,
       "metadata": {},
       "outputs": [],
       "source": [
        "# reloads modules automatically before entering the execution of code\n",
        "%load_ext autoreload\n",
        "%autoreload 2\n",
        "\n",
        "# third parties imports\n",
        "import numpy as np \n",
        "import pandas as pd\n",
        "# -- add new imports here --\n",
        "\n",
        "# local imports\n",
        "from constants import Constant as C\n",
        "from loaders import load_ratings\n",
        "from loaders import load_items"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "# 1 - Content analytics\n",
        "Explore and perform descriptive statistics on content data"
       ]
      },
      {
       "cell_type": "code",
       "execution_count": 10,
       "metadata": {},
       "outputs": [],
       "source": [
        "# -- load the items and display the Dataframe"
       ]
      },
      {
       "cell_type": "code",
       "execution_count": 11,
       "metadata": {},
       "outputs": [],
       "source": [
        "# -- display relevant informations that can be extracted from the dataset"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "# 2 - Evidence analytics\n",
        "Explore and perform descriptive statistics on evidence data"
       ]
      },
      {
       "cell_type": "code",
       "execution_count": 14,
       "metadata": {},
       "outputs": [],
       "source": [
        "# -- load the items and display the Dataframe"
       ]
      },
      {
       "cell_type": "code",
       "execution_count": 15,
       "metadata": {},
       "outputs": [],
       "source": [
        "# -- display relevant informations that can be extracted from the dataset"
       ]
      }
     ],
     "metadata": {
      "kernelspec": {
       "display_name": "mlsmm2156",
       "language": "python",
       "name": "mlsmm2156"
      },
      "language_info": {
       "codemirror_mode": {
        "name": "ipython",
        "version": 3
       },
       "file_extension": ".py",
       "mimetype": "text/x-python",
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.9.9"
      }
     },
     "nbformat": 4,
     "nbformat_minor": 4
    }