ML Intro & ML in GameDev Workshop.ipynb

ml-intro-ml-in-gamedev-workshop.ipynb

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      "name": "ML Intro & ML in GameDev Workshop.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/embiem/0434fe421b06ee13f92db9ff7991ca99/ml-intro-ml-in-gamedev-workshop.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YdrOqn0OBSpx",
        "colab_type": "text"
      },
      "source": [
        "# ML Intro & ML in GameDev\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RZARGEjBBAVk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import necessary libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import io\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "# Load the dataset\n",
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "\n",
        "file_name = next(iter(uploaded.keys()))\n",
        "\n",
        "data = pd.read_csv(io.BytesIO(uploaded[file_name]))\n",
        "playerYs = data['playerY']\n",
        "features = data.drop('playerY', axis = 1)\n",
        "    \n",
        "# Success\n",
        "print(\"The dataset has {} data points with {} variables.\".format(*data.shape))\n",
        "data.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f3SbmFE9E6_z",
        "colab_type": "text"
      },
      "source": [
        "## Data Exploration"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QwfgOccqFL60",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# TODO: Minimum playerYs of the data\n",
        "minimum_playerYs = \n",
        "\n",
        "# TODO: Maximum playerYs of the data\n",
        "maximum_playerYs = \n",
        "\n",
        "# TODO: Mean playerYs of the data\n",
        "mean_playerYs = \n",
        "\n",
        "# TODO: Median playerYs of the data\n",
        "median_playerYs = \n",
        "\n",
        "# TODO: Standard deviation of playerYs of the data\n",
        "std_playerYs = \n",
        "\n",
        "print(\"Min: {:,.4f}\".format(minimum_playerYs))\n",
        "print(\"Max: {:,.4f}\".format(maximum_playerYs))\n",
        "print(\"Mean: {:,.4f}\".format(mean_playerYs))\n",
        "print(\"Median: {:,.4f}\".format(median_playerYs))\n",
        "print(\"Standard deviation: {:,.4f}\".format(std_playerYs))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XT2J-8OQ7VG5",
        "colab_type": "text"
      },
      "source": [
        "### Measures of Center\n",
        "\n",
        "**Mean**: sum of the values divided by the number of values\n",
        "\n",
        "**Median**: sort the data and pick the value which lies in the middle, or for a even count the average of the two values in the middle. It has a robust tendency, which means it won’t be affected by outliers as much as the mean."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HKOxxPMz94E5",
        "colab_type": "text"
      },
      "source": [
        "### Measures of Spread\n",
        "\n",
        "**Standard Deviation**: measure of the amount of variation. A low standard deviation indicates that the values tend to be close to the mean (also called the expected value) of the set, while a high standard deviation indicates that the values are spread out over a wider range.\n",
        "\n",
        "Calculated by taking the square root of average squared deviation.\n",
        "\n",
        "*Standard deviation is an excellent way to identify outliers*. Data points that lie more than one standard deviation from the mean can be considered unusual.\n",
        "\n",
        "![alt text](https://upload.wikimedia.org/wikipedia/commons/8/8c/Standard_deviation_diagram.svg)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FXEbkvgY948F",
        "colab_type": "text"
      },
      "source": [
        "### Pairplot Graph\n",
        "\n",
        "Plot pairwise relationships in a dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qvB-iNebLNUO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import seaborn as sns; sns.set()\n",
        "sns.pairplot(data);"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lYFy1yLzIEtD",
        "colab_type": "text"
      },
      "source": [
        "## Shuffle and Split Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kl3BK35dINHm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# TODO Shuffle and split the data into training and testing subsets\n",
        "X_train, X_test, y_train, y_test = "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HvIgH_3_QeGk",
        "colab_type": "text"
      },
      "source": [
        "### Download the shuffled & split data\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8wzyp_MmQkVe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train.to_csv(\"train-features.csv\", index=False)\n",
        "y_train.to_csv(\"train-target.csv\", index=False, header=\"playerY\")\n",
        "X_test.to_csv(\"test-features.csv\", index=False)\n",
        "y_test.to_csv(\"test-target.csv\", index=False, header=\"playerY\")\n",
        "\n",
        "files.download(\"train-features.csv\")\n",
        "files.download(\"train-target.csv\")\n",
        "files.download(\"test-features.csv\")\n",
        "files.download(\"test-target.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}