2019/kaggle/kaggle-report-template.ipynb

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   "source": [
    "<h1><center>Data Analysis</center></h1>\n",
    "<h2><center>Kaggle report notebook</center></h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams['figure.figsize'] = (12,8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduce yourself"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Put your team name and team-members' names below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
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  {
   "cell_type": "markdown",
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   "source": [
    "# Task"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this task we are working with posts on famous russian advertisement and resell platform.\n",
    "\n",
    "Ginen information about the post and corresponding item you have to estimate its probability to be sold in 3 days.\n",
    "\n",
    "Below you will find some desirable stages of your report."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('./train.tsv.gz', sep='\\t', index_col=0)\n",
    "df_test = pd.read_csv('./test.tsv.gz', sep='\\t', index_col=0)"
   ]
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   "source": [
    "df_train.columns"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Descriptive Data Analysis\n",
    "\n",
    "* Analyze train dataset features\n",
    "    * Provide some meaningfull description about corresponding features (in form of pictures or some coefficients)\n",
    "    * Outline any findings you are able to reveal\n",
    "* Try to measure analyse any correspondense between features and target features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (almost) Zero-knowledge prediction\n",
    "* Develop non-machine learning approach to get predictions\n",
    "    * Describe it and measure its quality on Kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine-learning pipeline and prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you develop your pipeline, generate features for train and test sets\n",
    "* Your pipeline should be reproducible - make sure you set all `random_states` if needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model interpretation\n",
    "Try to interpret your model results\n",
    "* What **knowledge** does it reveal?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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