Data_Science_for_Customer_Insights_Project_Framework.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
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      "source": [
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ycXWv5-jYpIC"
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      "source": [
        "# **Project Notebook**\n",
        "Names:\n",
        "\n",
        "Matr.:\n",
        "\n",
        "Dataset:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xo3zbWkWbQCN"
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      "source": [
        "*This is an example notebook, which helps you, to structure your data science project. You may partly adapt/ shorten/ extend it depending on the characteristics of your project.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a8aiLYHdaX9P"
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      "source": [
        "## **Introduction**\n",
        "- State project target and context (what is your dependent variable, what is your dataset about?)\n",
        "- Explain relevance for Marketing context (incl. sources)\n",
        "- Mention main conclusions from major tech papers on topic"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CuIPnDlucl2q"
      },
      "source": [
        "*You may focos on text input here*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4m8XnguvY7Ic"
      },
      "source": [
        "## **Data Understanding**\n",
        "- Load data \n",
        "- Provide visualizations / key attributes into data, e.g., distribution of important variables\n",
        "- Point out relevant challenges, e.g., missing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W-iJXCKjYN2P"
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    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "## **Data Preparation**\n",
        "- Execute potential transformations of data\n",
        "- Create potentially new / combined features\n",
        "- Generate embeddings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hn9qyfQCZkZ5"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q7rM1TalZkJ7"
      },
      "source": [
        "## **Modelling**\n",
        "- Create and train different models\n",
        "- Execute a structured hyper parameter tuning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cpNhkAZiZj4X"
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      "source": [
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    {
      "cell_type": "markdown",
      "metadata": {
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      },
      "source": [
        "## **Performance Evaluation**\n",
        "- Use different performance metrics, e.g., accuracy to compare and evaluate your models\n",
        "- Analyze reasons for error"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z4qDzTIKcItl"
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    },
    {
      "cell_type": "markdown",
      "metadata": {
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      },
      "source": [
        "## **Interpretation**\n",
        "- Analyze your model, what are potential conclusions? E.g., which features are important?\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
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        "id": "VfLhescacb17"
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    {
      "cell_type": "markdown",
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      },
      "source": [
        "## **References**\n",
        "- Cite all references you need according to chair guidelines"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_OLrMDxWc8oM"
      },
      "source": [
        "Liu, Xuan; Shi, Savannah Wei; Teixeira, Thales; Wedel, Michel (2018): Video Content Marketing: The Making of Clips, Journal of Marketing, Vol. 82, 86-101."
      ]
    }
  ]
}