SCI564_Assignment_2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "SCI564:Assignment_2.ipynb",
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tawfiqam/MI564/blob/main/SCI564_Assignment_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Because of changes in APIs (take a look at accompanying readings), I am providing the data needed for the assignment. Please try to run the API just to see what happens\n",
        "\n"
      ],
      "metadata": {
        "id": "o5XNfCiJ1AHe"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "73i1PHpddLUl",
        "outputId": "5df055c8-660c-411e-be48-fdb105150fde"
      },
      "source": [
        "!pip install PSAW"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting PSAW\n",
            "  Downloading https://files.pythonhosted.org/packages/01/fe/e2f43241ff7545588d07bb93dd353e4333ebc02c31d7e0dc36a8a9d93214/psaw-0.1.0-py3-none-any.whl\n",
            "Requirement already satisfied: Click in /usr/local/lib/python3.6/dist-packages (from PSAW) (7.1.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from PSAW) (2.23.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->PSAW) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->PSAW) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->PSAW) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->PSAW) (2020.12.5)\n",
            "Installing collected packages: PSAW\n",
            "Successfully installed PSAW-0.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p3Bv1MzrdCTm"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "#we will need datetime in order to specify the timeline we need to collect the data\n",
        "import datetime as dt\n",
        "\n",
        "#now we import the wrapper in order to use the API\n",
        "from psaw import PushshiftAPI\n",
        "\n",
        "api = PushshiftAPI()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Q0. Why do you think it's not running?! [Extra 10 points]"
      ],
      "metadata": {
        "id": "rgL8P_kC-E6S"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A0."
      ],
      "metadata": {
        "id": "hZcRZvFl-L0H"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C-DOhoOPMw8t"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.rcParams.update({'font.size':20,'figure.figsize':(10,8)})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pbGawSKSdVD3",
        "outputId": "73ab2baf-a710-4868-f29c-44ca13d143f7"
      },
      "source": [
        "#mount the google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XlOWiNFDYP3h"
      },
      "source": [
        "#API\n",
        "\n",
        "Have you heard of Game Stop or r/wallsteetbets? Basically, a bunch of redditors decided to buy a lot of stock in Game Stop - a brick and mortar gaming company. Game Stop's stock had been on the decline. The redditors heard about a hedge fund company that had shorted Game Stop's stock (see [this story](https://abcnews.go.com/Business/gamestop-timeline-closer-saga-upended-wall-street/story?id=75617315) for details).\n",
        "\n",
        "![WallStreetBets.png](data:image/png;base64,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)\n",
        "\n",
        "\n",
        "Now, one of the interesting twists in [this saga came when Robin Hood](https://www.prweek.com/article/1705984/timeline-crisis-r-wallstreetbets-gamestop) (interactive brokerage company), on January 28th, decided to halt trading on Game Stop and few other stocks.\n",
        "\n",
        "We want to analyze the discussions on the r/wallstreetbets on January 28th between 11 am and 11:10 EST (10 minutes). It is imporant to note that the system stores data using GMT timestamps. So, you will have to take the time difference into account when writing your code. You can check time differences [here](https://savvytime.com/converter/gmt-to-est)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2h3rKLHQblwY"
      },
      "source": [
        "##Q1a. Please read the data from this [file](https://drive.google.com/file/d/1ud_vHqODLnO4egeliq9A-IUfv9q7YUTF/view?usp=sharing).It is a json file. Read the file into a pandas dataframe.\n",
        "\n",
        "##[10 points]\n",
        "\n",
        "[This should take around 8 minutes]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6GM6sN8acNm_"
      },
      "source": [
        "#A1a."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VDJFWyT0f4O1"
      },
      "source": [
        "##Q1b. How many comments did you collect? What is the shape of the DataFrame? [5 points]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "81YXI3m1f7uC"
      },
      "source": [
        "#A1b."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SMBzRD6efBsp"
      },
      "source": [
        "##Q1c. Using a box plot, show the distribution of scores in these comments [5 points]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jQea5lnsfLCX"
      },
      "source": [
        "#A1c."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "61ZtX4Q7bz9r"
      },
      "source": [
        "#PCA\n",
        "\n",
        "For this question, you will be working with the Midophonis Subreddit dataset. The dataset can be accessed [here](https://drive.google.com/file/d/1q97jePHzpFWmF0uhVYzrw_nvl46PjQs-/view?usp=sharing). The dataset is represents users in the [r/misophonia subreddit](https://www.reddit.com/r/misophonia/).\n",
        "\n",
        "Misophonia is a neurophysiological disorder in which sufferers face an aversive reaction to otherwise normal sounds and (visual) stimuli.\n",
        "\n",
        "The dataset contains:\n",
        "\n",
        "1.   a number of discussion topics (Topic 0...Topic 9). The higher the score, the higher the discussion of that particular topic\n",
        "2.   sentiment variables from the [LIWC](http://liwc.wpengine.com/)\n",
        "3.   tenure (how long users have used the site): old users:0; new users:1\n",
        "4.   karma score on Reddit\n",
        "\n",
        "Following a list showing all these features. Please run the next cell before you continue."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rZVe3Cb8OTTK"
      },
      "source": [
        "columns = ['Topic 0', 'Topic 1', 'Topic 10', 'Topic 11', 'Topic 12', 'Topic 13',\\\n",
        "           'Topic 14', 'Topic 15', 'Topic 16', 'Topic 17', 'Topic 18', 'Topic 19',\\\n",
        "           'Topic 2', 'Topic 3', 'Topic 4', 'Topic 5', 'Topic 6', 'Topic 7', 'Topic 8',\\\n",
        "           'Topic 9', 'WC', 'achieve', 'adj', 'adverb', 'affect', 'affiliation', 'anger',\\\n",
        "           'anx', 'article', 'assent', 'auxverb', 'bio', 'cause', 'certain', 'cogproc',\\\n",
        "           'compare', 'conj', 'death', 'differ', 'discrep', 'drives', 'family', 'feel',\\\n",
        "           'female', 'filler', 'focusfuture', 'focuspast', 'focuspresent', 'friend',\\\n",
        "           'function', 'health', 'hear', 'home', 'i', 'informal', 'ingest', 'insight',\\\n",
        "           'interrog', 'ipron', 'leisure', 'male', 'money', 'motion', 'negate', 'negemo',\\\n",
        "           'netspeak', 'nonflu', 'number', 'percept', 'posemo', 'power', 'ppron', 'prep',\\\n",
        "           'pronoun', 'quant', 'relativ', 'relig', 'reward', 'risk', 'sad', 'score', 'see',\\\n",
        "           'sexual', 'shehe', 'social', 'space', 'swear', 'tentat', 'they', 'time',\\\n",
        "           'verb', 'we', 'work', 'you','tenure']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZPOUDOxIJxou"
      },
      "source": [
        "##Q2a. What is the shape of the dataset? How many rows and columns are there in this dataframe (#rows,#columns)? [5 points]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6LCeK2mhJ5So"
      },
      "source": [
        "#A2a."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AvIP_ElMLw2c"
      },
      "source": [
        "##Q2b. Create a scatter diagram with 'score' on the x-axis, and 'Topic 1' on the y-axis. Color old users red (tenure=0), and new users blue (tenure=1). [5 points]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RNhdFh6lNDrM"
      },
      "source": [
        "#A2b."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kutmtviFCBcV"
      },
      "source": [
        "##Q2c. Create a dataframe X, which includes all features but tenure. [5 points]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OIPI_UeePpXE"
      },
      "source": [
        "#A2c."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Gv71ez4P6y8"
      },
      "source": [
        "##Q2d. Run PCA on X with n_components = 10 (10 components) [10 points]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pgudRCjmQNId"
      },
      "source": [
        "#A2d."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XDA60ep8QZBP"
      },
      "source": [
        "##Q2e. Create a graph showing the cumulative explained variance with 10 components. What is the cumulative explained variance? [10 points]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fnEddcHpQnvM"
      },
      "source": [
        "#A2e."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kzvpv2ZfRG6J"
      },
      "source": [
        "##Q2f. Create a scree graph for this PCA. Which component explains the most variance? [10 points]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oTnrasoTCbhR"
      },
      "source": [
        "#A2f."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wwYRPAgmJjD6"
      },
      "source": [
        "#Kmeans\n",
        "\n",
        "##Q3a. First, load the wine quality sklearn dataset:\n",
        "\n",
        "https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_wine.html\n",
        "\n",
        "##After you do that, load the Wine Data into x. Do not load the Wine Target [5 points]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oiuVuaKXNNMn"
      },
      "source": [
        "#A3a."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xh9YQy8fNzM1"
      },
      "source": [
        "##Q3b. Find the best K for this dataset. To do so, you should use the elbow method. What is the best K? [10 points]\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5djNjdbpOEvo"
      },
      "source": [
        "#A3b."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZFXm8TewOHs_"
      },
      "source": [
        "##Q3c. Looking at the dataset, does the K you found in Q3b make sense? Explain why you think this K makes sense givne this dataset. [10 points]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K62sovMSOZ6S"
      },
      "source": [
        "A3c."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aPimxPBvP5n3"
      },
      "source": [
        "##Q3d. Create a hierarchical agglomerative cluster using the same data. The cluster should have a k=3, euclidean affinity, and Ward linkage.\n",
        "\n",
        "##Plot a dendogram of the cluster using scipy.cluster.hierarchy.\n",
        "\n",
        "##Plot a scatter diagram of the agglomerative cluster. [10 Points]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U9gTVqx8RBh-"
      },
      "source": [
        "#A3d."
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}