Day1FirstDataProject.ipynb

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      "source": [
        "<img style=\"float: left;\" src=\"D2Klogo.png\" width=\"20%\"> \n",
        "\n",
        "<center>\n",
        "\n",
        "# Welcome to Data Analytics #\n",
        "### Introduction to your first Data project ###\n",
        "\n",
        "### Code Demo for Day 1 ###"
      ],
      "metadata": {
        "id": "Oti1qmFTddkU"
      },
      "id": "Oti1qmFTddkU"
    },
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      "cell_type": "markdown",
      "source": [
        "# This demo is a toy example of how to solve a data analytics problem: \n",
        "## Question $\\rightarrow$ Data $\\rightarrow$ Answer $\\rightarrow$ More questions $\\rightarrow$ ...\n",
        "\n",
        "\n",
        "## Your ultimate goal is to walk through your own Data Science Pipeline by completing a real data project."
      ],
      "metadata": {
        "id": "ubducFb1d0Xd"
      },
      "id": "ubducFb1d0Xd"
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      "cell_type": "markdown",
      "source": [
        "<br/>\n",
        "\n",
        "## Question: Can we predict test scores for students?\n",
        "\n",
        "How can we answer this question?"
      ],
      "metadata": {
        "id": "Q18ewp6Fd5OL"
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      "source": [
        "<br/>\n",
        "\n",
        "\n",
        "## Ideas:\n",
        "\n",
        "1. which test scores are we predicting? \n",
        "2. What features can we use to predict test scores?\n",
        "3. What kind of model can we use to predict test scores?\n",
        "4. What are the most important features to predict test scores?\n",
        "5. Are there features that interact with each other that affect test scores?\n"
      ],
      "metadata": {
        "id": "rIjznNA7eLwV"
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      "source": [
        "<br/>\n",
        "\n",
        "\n",
        "##About Dataset \n",
        "\n",
        "This dataset includes scores from three test scores of students at a (fictional) public school and a variety of personal and socio-economic factors that may have interaction effects upon them."
      ],
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        "tags": [],
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      },
      "source": [
        "#  **Predicting Student Test Scores**"
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "#importing libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import plotly as px\n",
        "import matplotlib as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "**In this notebook:**\n",
        "1. Exploratory Data Analysis\n",
        "2. Preprocessing\n",
        "3. Model Creation\n",
        "4. Model Analysis\n",
        "5. Conclusion"
      ]
    },
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      "source": [
        "df = pd.read_excel('/content/TestScoresDataset.xlsx')\n",
        "df.head(5) #print the first 5 rows"
      ]
    },
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      "source": [
        "df.columns #print columns in the dataframe"
      ]
    },
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      "source": [
        "*Given in the dataset description, we have a data dictionary we will refer back to:*\n",
        "\n",
        "\n",
        "TestPrep: test preparation course (completed or none)\n",
        "\n",
        "\n",
        "PracticeSport: how often the student parctice sport\n",
        "\n",
        "IsFirstChild: if the child is first child in the family or not\n",
        "\n",
        "NrSiblings : Number of siblings the student has\n",
        "\n",
        "TransportMeans: Means of transport to school\n",
        "\n",
        "WklyStudyHours: Weekly self-study hours\n",
        "\n",
        "MathScore: math test score\n",
        "\n",
        "ReadingScore: reading test score\n",
        "\n",
        "WritingScore writing test score"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
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        "id": "_zKuFIyAvq0Q"
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      "source": [
        "#we have an unnecessary column, 'Unamed: 0', which seems to be an index column. \n",
        "#we will remove this column, as it provides no utility at this moment, or for the \n",
        "#model we will create later\n",
        "\n",
        "df = df.drop('Unnamed: 0', axis=1)\n",
        "df.head(5) #shows the column has been dropped"
      ]
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      "source": [
        "#I want to return the datatypes of each feature:\n",
        "df.dtypes"
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "#all of our features are objects except NrSibligns\n",
        "#MathScore, ReadingScore, WritingScore are our target values\n",
        "\n",
        "#lets summarize the dataset (the numeric values)\n",
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "#Weekly study hours currently is in an improper format\n",
        "#Lets fix this\n",
        "\n",
        "#first, lets view the unique weekly study hours values:\n",
        "df['WklyStudyHours'].unique()"
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "#let's remove these rows\n",
        "#we also will assign this new data to a new dataframe\n",
        "df2 = df.dropna(subset=['WklyStudyHours']).copy()\n",
        "df2['WklyStudyHours'].unique() #returns unique values after dropping 'nan'"
      ]
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      "source": [
        "df2.head()"
      ]
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      "source": [
        "#I see multiple 'NaN' values outside of this feature\n",
        "#We need to decide how to handle these rows\n",
        "\n",
        "#they aren't numeric values, so we cannot substitute them with the means\n",
        "#we will fix this after the EDA (exploratory data analysis)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5bbb477c",
      "metadata": {
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      },
      "source": [
        "**Exploratory Data Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "evKgCJIBmP4X"
      },
      "id": "evKgCJIBmP4X",
      "execution_count": null,
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    },
    {
      "cell_type": "markdown",
      "source": [
        "*TestPrep Distribution*"
      ],
      "metadata": {
        "id": "gP4c25kahTGR"
      },
      "id": "gP4c25kahTGR"
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    {
      "cell_type": "code",
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      "outputs": [],
      "source": [
        "#testprep distribution\n",
        "plt.figure(figsize=(6,4))\n",
        "df2['TestPrep'].value_counts().plot(kind='bar')\n",
        "plt.xlabel('Test Preparation Course')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Distribution of Test Preparation Courses')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*frequency of practicing sports*"
      ],
      "metadata": {
        "id": "l5ts34_ShduV"
      },
      "id": "l5ts34_ShduV"
    },
    {
      "cell_type": "code",
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      "outputs": [],
      "source": [
        "#frequency of practicing sports\n",
        "plt.figure(figsize=(6,4))\n",
        "df2['PracticeSport'].value_counts().plot(kind='bar')\n",
        "plt.xlabel('Frequency of Practicing Sports')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Distribution of Frequency of Practicing Sports')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ca21c0b9",
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      "source": [
        "#firstchild distribution\n",
        "plt.figure(figsize=(6,4))\n",
        "df2['IsFirstChild'].value_counts().plot(kind='bar')\n",
        "plt.xlabel('Is First Child?')\n",
        "plt.ylabel('Count')\n",
        "plt.title('First Child Distribution')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "#number of siblings\n",
        "plt.figure(figsize=(6,4))\n",
        "df2['NrSiblings'].hist(bins=8)\n",
        "plt.xlabel('Number of Siblings')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Distribution of Number of Siblings')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "97314282",
      "metadata": {
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      "outputs": [],
      "source": [
        "#transportation means\n",
        "plt.figure(figsize=(8,4))\n",
        "df2['TransportMeans'].value_counts().plot(kind='bar')\n",
        "plt.xlabel('Means of Transport to School')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Transportation Distribution')\n",
        "plt.show()"
      ]
    },
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          "shell.execute_reply": "2023-05-23T05:00:20.416323Z"
        },
        "papermill": {
          "duration": 0.274387,
          "end_time": "2023-05-23T05:00:20.419827",
          "exception": false,
          "start_time": "2023-05-23T05:00:20.145440",
          "status": "completed"
        },
        "tags": [],
        "id": "e2129332"
      },
      "outputs": [],
      "source": [
        "#Study Hours Distribution\n",
        "plt.figure(figsize=(6,4))\n",
        "df2['WklyStudyHours'].hist(bins=20)\n",
        "plt.xlabel('Weekly Self-Study Hours')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Distribution of Weekly Self-Study Hours')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8651cc5a",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-05-23T05:00:20.477227Z",
          "iopub.status.busy": "2023-05-23T05:00:20.476365Z",
          "iopub.status.idle": "2023-05-23T05:00:20.721159Z",
          "shell.execute_reply": "2023-05-23T05:00:20.719475Z"
        },
        "papermill": {
          "duration": 0.277059,
          "end_time": "2023-05-23T05:00:20.724439",
          "exception": false,
          "start_time": "2023-05-23T05:00:20.447380",
          "status": "completed"
        },
        "tags": [],
        "id": "8651cc5a"
      },
      "outputs": [],
      "source": [
        "#Math Scores\n",
        "plt.figure(figsize=(6,4))\n",
        "df2['MathScore'].hist(bins=20)\n",
        "plt.xlabel('Math Score')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Distribution of Math Test Scores')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "655c1edc",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-05-23T05:00:20.783957Z",
          "iopub.status.busy": "2023-05-23T05:00:20.783485Z",
          "iopub.status.idle": "2023-05-23T05:00:21.038505Z",
          "shell.execute_reply": "2023-05-23T05:00:21.037326Z"
        },
        "papermill": {
          "duration": 0.28759,
          "end_time": "2023-05-23T05:00:21.041080",
          "exception": false,
          "start_time": "2023-05-23T05:00:20.753490",
          "status": "completed"
        },
        "tags": [],
        "id": "655c1edc"
      },
      "outputs": [],
      "source": [
        "#Reading Scores\n",
        "plt.figure(figsize=(6,4))\n",
        "df2['ReadingScore'].hist(bins=20)\n",
        "plt.xlabel('Reading Score')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Distribution of Reading Test Scores')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "905d95ac",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-05-23T05:00:21.100185Z",
          "iopub.status.busy": "2023-05-23T05:00:21.099306Z",
          "iopub.status.idle": "2023-05-23T05:00:21.343092Z",
          "shell.execute_reply": "2023-05-23T05:00:21.341775Z"
        },
        "papermill": {
          "duration": 0.277206,
          "end_time": "2023-05-23T05:00:21.345806",
          "exception": false,
          "start_time": "2023-05-23T05:00:21.068600",
          "status": "completed"
        },
        "tags": [],
        "id": "905d95ac"
      },
      "outputs": [],
      "source": [
        "#Writing Scores\n",
        "plt.figure(figsize=(6,4))\n",
        "df2['WritingScore'].hist(bins=20)\n",
        "plt.xlabel('Writing Score')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Distribution of Writing Test Scores')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6bc7b430",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-05-23T05:00:21.405863Z",
          "iopub.status.busy": "2023-05-23T05:00:21.404980Z",
          "iopub.status.idle": "2023-05-23T05:00:21.683971Z",
          "shell.execute_reply": "2023-05-23T05:00:21.682984Z"
        },
        "papermill": {
          "duration": 0.312826,
          "end_time": "2023-05-23T05:00:21.687148",
          "exception": false,
          "start_time": "2023-05-23T05:00:21.374322",
          "status": "completed"
        },
        "tags": [],
        "id": "6bc7b430"
      },
      "outputs": [],
      "source": [
        "#Scatterplot between Math and Reading\n",
        "sns.scatterplot(x='MathScore', y='ReadingScore', data=df2)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a7f2d7f4",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-05-23T05:00:21.752419Z",
          "iopub.status.busy": "2023-05-23T05:00:21.751107Z",
          "iopub.status.idle": "2023-05-23T05:00:22.019989Z",
          "shell.execute_reply": "2023-05-23T05:00:22.018994Z"
        },
        "papermill": {
          "duration": 0.30363,
          "end_time": "2023-05-23T05:00:22.022833",
          "exception": false,
          "start_time": "2023-05-23T05:00:21.719203",
          "status": "completed"
        },
        "tags": [],
        "id": "a7f2d7f4"
      },
      "outputs": [],
      "source": [
        "#Scatterplot between Math and Writing\n",
        "sns.scatterplot(x='MathScore', y='WritingScore', data=df2)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bbbc7e4c",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-05-23T05:00:22.089656Z",
          "iopub.status.busy": "2023-05-23T05:00:22.088152Z",
          "iopub.status.idle": "2023-05-23T05:00:22.347932Z",
          "shell.execute_reply": "2023-05-23T05:00:22.346405Z"
        },
        "papermill": {
          "duration": 0.296601,
          "end_time": "2023-05-23T05:00:22.351453",
          "exception": false,
          "start_time": "2023-05-23T05:00:22.054852",
          "status": "completed"
        },
        "tags": [],
        "id": "bbbc7e4c"
      },
      "outputs": [],
      "source": [
        "#Scatterplot between Reading and Writing\n",
        "sns.scatterplot(x='ReadingScore', y='WritingScore', data=df2)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note: Reading and Writing have a more narrow distribution"
      ],
      "metadata": {
        "id": "IflMktkFhzCh"
      },
      "id": "IflMktkFhzCh"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8e00be71",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-05-23T05:00:22.494260Z",
          "iopub.status.busy": "2023-05-23T05:00:22.493800Z",
          "iopub.status.idle": "2023-05-23T05:00:23.000002Z",
          "shell.execute_reply": "2023-05-23T05:00:22.998764Z"
        },
        "papermill": {
          "duration": 0.543399,
          "end_time": "2023-05-23T05:00:23.002816",
          "exception": false,
          "start_time": "2023-05-23T05:00:22.459417",
          "status": "completed"
        },
        "tags": [],
        "id": "8e00be71"
      },
      "outputs": [],
      "source": [
        "#correlation map\n",
        "corr = df2.corr()\n",
        "sns.heatmap(corr, annot=True, cmap='coolwarm')\n",
        "plt.title('Correlation Matrix')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Reading and Writing are highly correlated.\n",
        "It doesn't seem (so far) that number of siblings is correlated much to any test\n"
      ],
      "metadata": {
        "id": "SzKGuGg9h_zL"
      },
      "id": "SzKGuGg9h_zL"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ad18fcba",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-05-23T05:00:23.071519Z",
          "iopub.status.busy": "2023-05-23T05:00:23.071121Z",
          "iopub.status.idle": "2023-05-23T05:00:23.175925Z",
          "shell.execute_reply": "2023-05-23T05:00:23.174540Z"
        },
        "papermill": {
          "duration": 0.142827,
          "end_time": "2023-05-23T05:00:23.179190",
          "exception": false,
          "start_time": "2023-05-23T05:00:23.036363",
          "status": "completed"
        },
        "tags": [],
        "id": "ad18fcba"
      },
      "outputs": [],
      "source": [
        "#lets view the NaN values\n",
        "#This will show how many NAN values exist on each feature\n",
        "df2.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a28b7622",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-05-23T05:00:23.247737Z",
          "iopub.status.busy": "2023-05-23T05:00:23.247229Z",
          "iopub.status.idle": "2023-05-23T05:00:23.259292Z",
          "shell.execute_reply": "2023-05-23T05:00:23.258068Z"
        },
        "papermill": {
          "duration": 0.049215,
          "end_time": "2023-05-23T05:00:23.261591",
          "exception": false,
          "start_time": "2023-05-23T05:00:23.212376",
          "status": "completed"
        },
        "tags": [],
        "id": "a28b7622"
      },
      "outputs": [],
      "source": [
        "# Another way to count NAN entries per feature.\n",
        "# this should equal 1779\n",
        "df2['TestPrep'].isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "197ee5bc",
      "metadata": {
        "papermill": {
          "duration": 0.034308,
          "end_time": "2023-05-23T05:00:23.329126",
          "exception": false,
          "start_time": "2023-05-23T05:00:23.294818",
          "status": "completed"
        },
        "tags": [],
        "id": "197ee5bc"
      },
      "source": [
        "**Preprocessing**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5e508ab8",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-05-23T05:00:23.399008Z",
          "iopub.status.busy": "2023-05-23T05:00:23.397835Z",
          "iopub.status.idle": "2023-05-23T05:00:23.503929Z",
          "shell.execute_reply": "2023-05-23T05:00:23.502718Z"
        },
        "papermill": {
          "duration": 0.143857,
          "end_time": "2023-05-23T05:00:23.506412",
          "exception": false,
          "start_time": "2023-05-23T05:00:23.362555",
          "status": "completed"
        },
        "tags": [],
        "id": "5e508ab8"
      },
      "outputs": [],
      "source": [
        "#we can drop these values, but we may have significantly less data to use. \n",
        "#lets see the difference\n",
        "df_dropped = df2.dropna().copy()\n",
        "\n",
        "rows_df2 = df2.shape[0]\n",
        "rows_df_dropped = df_dropped.shape[0]\n",
        "print(\"Number of rows in df2:\", rows_df2)\n",
        "print(\"Number of rows in df_dropped:\", rows_df_dropped)\n",
        "print(\"Percentage of Rows that Remain:\", rows_df_dropped / rows_df2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9accf91e",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-05-23T05:00:23.575681Z",
          "iopub.status.busy": "2023-05-23T05:00:23.574991Z",
          "iopub.status.idle": "2023-05-23T05:00:23.599073Z",
          "shell.execute_reply": "2023-05-23T05:00:23.597866Z"
        },
        "papermill": {
          "duration": 0.061521,
          "end_time": "2023-05-23T05:00:23.601801",
          "exception": false,
          "start_time": "2023-05-23T05:00:23.540280",
          "status": "completed"
        },
        "tags": [],
        "id": "9accf91e"
      },
      "outputs": [],
      "source": [
        "#Is this tradeoff worth it?\n",
        "#For this sake of this analysis, I will remove the nulls and continue\n",
        "NewDF = df_dropped.copy()\n",
        "NewDF.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "94a3b7a3",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-05-23T05:00:23.673638Z",
          "iopub.status.busy": "2023-05-23T05:00:23.672956Z",
          "iopub.status.idle": "2023-05-23T05:00:23.677706Z",
          "shell.execute_reply": "2023-05-23T05:00:23.676812Z"
        },
        "papermill": {
          "duration": 0.044694,
          "end_time": "2023-05-23T05:00:23.680102",
          "exception": false,
          "start_time": "2023-05-23T05:00:23.635408",
          "status": "completed"
        },
        "tags": [],
        "id": "94a3b7a3"
      },
      "outputs": [],
      "source": [
        "#no remaining nulls\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9065e9f0",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-05-23T05:00:23.855980Z",
          "iopub.status.busy": "2023-05-23T05:00:23.855181Z",
          "iopub.status.idle": "2023-05-23T05:00:23.863693Z",
          "shell.execute_reply": "2023-05-23T05:00:23.862399Z"
        },
        "papermill": {
          "duration": 0.049911,
          "end_time": "2023-05-23T05:00:23.866546",
          "exception": false,
          "start_time": "2023-05-23T05:00:23.816635",
          "status": "completed"
        },
        "tags": [],
        "id": "9065e9f0"
      },
      "outputs": [],
      "source": [
        "NewDF.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f435fdc3",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-05-23T05:00:23.938473Z",
          "iopub.status.busy": "2023-05-23T05:00:23.938040Z",
          "iopub.status.idle": "2023-05-23T05:00:23.943849Z",
          "shell.execute_reply": "2023-05-23T05:00:23.942600Z"
        },
        "papermill": {
          "duration": 0.044863,
          "end_time": "2023-05-23T05:00:23.946532",
          "exception": false,
          "start_time": "2023-05-23T05:00:23.901669",
          "status": "completed"
        },
        "tags": [],
        "id": "f435fdc3"
      },
      "outputs": [],
      "source": [
        "#Categorical Data Preprocessing\n",
        "\n",
        "#-----------------------------------------#\n",
        "\n",
        "\n",
        "#TestPrep: Label Encoding\n",
        "#PracticeSport: Ordinal Encoding\n",
        "#IsFirstChild: Label Encoding\n",
        "#NrSiblings: Already Numerical\n",
        "#TranportMeans: One-Hot Encoding\n",
        "#WklyStudyHours: One-Hot Encoding\n",
        "\n",
        "#-----------------------------------------#"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "83d52401",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-05-23T05:00:24.727081Z",
          "iopub.status.busy": "2023-05-23T05:00:24.725829Z",
          "iopub.status.idle": "2023-05-23T05:00:24.735422Z",
          "shell.execute_reply": "2023-05-23T05:00:24.733970Z"
        },
        "papermill": {
          "duration": 0.050402,
          "end_time": "2023-05-23T05:00:24.738787",
          "exception": false,
          "start_time": "2023-05-23T05:00:24.688385",
          "status": "completed"
        },
        "tags": [],
        "id": "83d52401"
      },
      "outputs": [],
      "source": [
        "#TestPrep Unique\n",
        "unique_testprep = NewDF['TestPrep'].unique()\n",
        "print(unique_testprep)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f85e5e6e",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-05-23T05:00:24.813220Z",
          "iopub.status.busy": "2023-05-23T05:00:24.812767Z",
          "iopub.status.idle": "2023-05-23T05:00:24.843548Z",
          "shell.execute_reply": "2023-05-23T05:00:24.842651Z"
        },
        "papermill": {
          "duration": 0.070818,
          "end_time": "2023-05-23T05:00:24.846003",
          "exception": false,
          "start_time": "2023-05-23T05:00:24.775185",
          "status": "completed"
        },
        "tags": [],
        "id": "f85e5e6e"
      },
      "outputs": [],
      "source": [
        "#TestPrep\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "label_encoder = LabelEncoder()\n",
        "NewDF['TestPrep_Encoded'] = label_encoder.fit_transform(NewDF['TestPrep'])\n",
        "NewDF.drop('TestPrep', axis=1, inplace=True)\n",
        "NewDF.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1f7b4634",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-05-23T05:00:25.121458Z",
          "iopub.status.busy": "2023-05-23T05:00:25.120666Z",
          "iopub.status.idle": "2023-05-23T05:00:25.130646Z",
          "shell.execute_reply": "2023-05-23T05:00:25.128449Z"
        },
        "papermill": {
          "duration": 0.051291,
          "end_time": "2023-05-23T05:00:25.133290",
          "exception": false,
          "start_time": "2023-05-23T05:00:25.081999",
          "status": "completed"
        },
        "tags": [],
        "id": "1f7b4634"
      },
      "outputs": [],
      "source": [
        "#PracticeSport Unique Values\n",
        "unique_ps = NewDF['PracticeSport'].unique()\n",
        "print(unique_ps)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "56941902",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-05-23T05:00:25.211055Z",
          "iopub.status.busy": "2023-05-23T05:00:25.209441Z",
          "iopub.status.idle": "2023-05-23T05:00:25.242766Z",
          "shell.execute_reply": "2023-05-23T05:00:25.241125Z"
        },
        "papermill": {
          "duration": 0.075001,
          "end_time": "2023-05-23T05:00:25.245407",
          "exception": false,
          "start_time": "2023-05-23T05:00:25.170406",
          "status": "completed"
        },
        "tags": [],
        "id": "56941902"
      },
      "outputs": [],
      "source": [
        "#PracticeSport\n",
        "#define the ordinal mapping dictionary\n",
        "ordinal_mapping1 = {\n",
        "    'never': 0,\n",
        "    'sometimes': .5,\n",
        "    'regularly': 1 \n",
        "}\n",
        "#apply ordinal encoding\n",
        "NewDF['PracticeSport_Encoded'] = NewDF['PracticeSport'].map(ordinal_mapping1)\n",
        "NewDF.drop('PracticeSport', axis=1, inplace=True)\n",
        "NewDF.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b9907246",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-05-23T05:00:25.323793Z",
          "iopub.status.busy": "2023-05-23T05:00:25.322685Z",
          "iopub.status.idle": "2023-05-23T05:00:25.360964Z",
          "shell.execute_reply": "2023-05-23T05:00:25.358854Z"
        },
        "papermill": {
          "duration": 0.080853,
          "end_time": "2023-05-23T05:00:25.363917",
          "exception": false,
          "start_time": "2023-05-23T05:00:25.283064",
          "status": "completed"
        },
        "tags": [],
        "id": "b9907246"
      },
      "outputs": [],
      "source": [
        "#IsFirstChild\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "label_encoder2 = LabelEncoder()\n",
        "NewDF['IsFirstChild_Encoded'] = label_encoder2.fit_transform(NewDF['IsFirstChild'])\n",
        "NewDF.drop('IsFirstChild', axis=1, inplace=True)\n",
        "NewDF.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4eeddaaf",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-05-23T05:00:25.446109Z",
          "iopub.status.busy": "2023-05-23T05:00:25.445569Z",
          "iopub.status.idle": "2023-05-23T05:00:25.455790Z",
          "shell.execute_reply": "2023-05-23T05:00:25.454040Z"
        },
        "papermill": {
          "duration": 0.053908,
          "end_time": "2023-05-23T05:00:25.458326",
          "exception": false,
          "start_time": "2023-05-23T05:00:25.404418",
          "status": "completed"
        },
        "tags": [],
        "id": "4eeddaaf"
      },
      "outputs": [],
      "source": [
        "#TransportMeans Unique\n",
        "unique_TM = NewDF['TransportMeans'].unique()\n",
        "print(unique_TM)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5f36980e",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-05-23T05:00:25.538690Z",
          "iopub.status.busy": "2023-05-23T05:00:25.537857Z",
          "iopub.status.idle": "2023-05-23T05:00:25.574133Z",
          "shell.execute_reply": "2023-05-23T05:00:25.572799Z"
        },
        "papermill": {
          "duration": 0.080304,
          "end_time": "2023-05-23T05:00:25.577078",
          "exception": false,
          "start_time": "2023-05-23T05:00:25.496774",
          "status": "completed"
        },
        "tags": [],
        "id": "5f36980e"
      },
      "outputs": [],
      "source": [
        "#TransportMeans\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "label_encoder4 = LabelEncoder()\n",
        "NewDF['TransportMeans_Encoded'] = label_encoder4.fit_transform(NewDF['TransportMeans'])\n",
        "NewDF.drop('TransportMeans', axis=1, inplace=True)\n",
        "NewDF.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5507ff6d",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-05-23T05:00:25.658304Z",
          "iopub.status.busy": "2023-05-23T05:00:25.657845Z",
          "iopub.status.idle": "2023-05-23T05:00:25.691275Z",
          "shell.execute_reply": "2023-05-23T05:00:25.689963Z"
        },
        "papermill": {
          "duration": 0.076824,
          "end_time": "2023-05-23T05:00:25.693897",
          "exception": false,
          "start_time": "2023-05-23T05:00:25.617073",
          "status": "completed"
        },
        "tags": [],
        "id": "5507ff6d"
      },
      "outputs": [],
      "source": [
        "#WklyStudyHours\n",
        "#PracticeSport\n",
        "#define the ordinal mapping dictionary\n",
        "ordinal_mapping3 = {\n",
        "    '< 5': 0,\n",
        "    '5 - 10': .5,\n",
        "    '> 10': 1 \n",
        "}\n",
        "#apply ordinal encoding\n",
        "NewDF['WklyStudyHours_Encoded'] = NewDF['WklyStudyHours'].map(ordinal_mapping3)\n",
        "NewDF.drop('WklyStudyHours', axis=1, inplace=True)\n",
        "NewDF.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b5d75e7f",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-05-23T05:00:25.775663Z",
          "iopub.status.busy": "2023-05-23T05:00:25.775238Z",
          "iopub.status.idle": "2023-05-23T05:00:25.810311Z",
          "shell.execute_reply": "2023-05-23T05:00:25.808926Z"
        },
        "papermill": {
          "duration": 0.080269,
          "end_time": "2023-05-23T05:00:25.813290",
          "exception": false,
          "start_time": "2023-05-23T05:00:25.733021",
          "status": "completed"
        },
        "tags": [],
        "id": "b5d75e7f"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "siblings = NewDF['NrSiblings'].values.reshape(-1, 1)\n",
        "normalized_siblings = scaler.fit_transform(siblings)\n",
        "NewDF['NrSiblings_normalized'] = normalized_siblings\n",
        "NewDF.drop('NrSiblings', axis=1, inplace=True)\n",
        "NewDF.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "57c149d6",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-05-23T05:00:25.898682Z",
          "iopub.status.busy": "2023-05-23T05:00:25.898247Z",
          "iopub.status.idle": "2023-05-23T05:00:25.939282Z",
          "shell.execute_reply": "2023-05-23T05:00:25.937939Z"
        },
        "papermill": {
          "duration": 0.088176,
          "end_time": "2023-05-23T05:00:25.941994",
          "exception": false,
          "start_time": "2023-05-23T05:00:25.853818",
          "status": "completed"
        },
        "tags": [],
        "id": "57c149d6"
      },
      "outputs": [],
      "source": [
        "# Remove missing values\n",
        "NewDF.dropna()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f5d558db",
      "metadata": {
        "papermill": {
          "duration": 0.039928,
          "end_time": "2023-05-23T05:00:26.021871",
          "exception": false,
          "start_time": "2023-05-23T05:00:25.981943",
          "status": "completed"
        },
        "tags": [],
        "id": "f5d558db"
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# **Model Creation / Data Split**\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cf34f904",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-05-23T05:00:26.106645Z",
          "iopub.status.busy": "2023-05-23T05:00:26.106235Z",
          "iopub.status.idle": "2023-05-23T05:00:26.493115Z",
          "shell.execute_reply": "2023-05-23T05:00:26.491431Z"
        },
        "papermill": {
          "duration": 0.435252,
          "end_time": "2023-05-23T05:00:26.497495",
          "exception": false,
          "start_time": "2023-05-23T05:00:26.062243",
          "status": "completed"
        },
        "tags": [],
        "id": "cf34f904"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
        "\n",
        "#MathScore regression\n",
        "X_math = NewDF.drop([\"MathScore\", \"ReadingScore\", \"WritingScore\"], axis=1)\n",
        "y_math = NewDF[\"MathScore\"]\n",
        "\n",
        "#ReadingScore regression\n",
        "X_reading = NewDF.drop([\"MathScore\", \"ReadingScore\", \"WritingScore\"], axis=1)\n",
        "y_reading = NewDF[\"ReadingScore\"]\n",
        "\n",
        "#WritingScore regression\n",
        "X_writing = NewDF.drop([\"MathScore\", \"ReadingScore\", \"WritingScore\"], axis=1)\n",
        "y_writing = NewDF[\"WritingScore\"]\n",
        "\n",
        "X_math_train, X_math_test, y_math_train, y_math_test = train_test_split(X_math, y_math, test_size=0.2, random_state=42)\n",
        "X_reading_train, X_reading_test, y_reading_train, y_reading_test = train_test_split(X_reading, y_reading, test_size=0.2, random_state=42)\n",
        "X_writing_train, X_writing_test, y_writing_train, y_writing_test = train_test_split(X_writing, y_writing, test_size=0.2, random_state=42)\n",
        "\n",
        "#MathScore regression\n",
        "reg_math = LinearRegression()\n",
        "reg_math.fit(X_math_train, y_math_train)\n",
        "\n",
        "#ReadingScore regression\n",
        "reg_reading = LinearRegression()\n",
        "reg_reading.fit(X_reading_train, y_reading_train)\n",
        "\n",
        "#WritingScore regression\n",
        "reg_writing = LinearRegression()\n",
        "reg_writing.fit(X_writing_train, y_writing_train)\n",
        "\n",
        "#MathScore regression\n",
        "y_math_pred = reg_math.predict(X_math_test)\n",
        "\n",
        "#ReadingScore regression\n",
        "y_reading_pred = reg_reading.predict(X_reading_test)\n",
        "\n",
        "#WritingScore regression\n",
        "y_writing_pred = reg_writing.predict(X_writing_test)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "09525ce8",
      "metadata": {
        "papermill": {
          "duration": 0.039858,
          "end_time": "2023-05-23T05:00:26.658781",
          "exception": false,
          "start_time": "2023-05-23T05:00:26.618923",
          "status": "completed"
        },
        "tags": [],
        "id": "09525ce8"
      },
      "source": [
        "# **Model Summary and Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1f0a67a2",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-05-23T05:00:26.742384Z",
          "iopub.status.busy": "2023-05-23T05:00:26.741969Z",
          "iopub.status.idle": "2023-05-23T05:00:26.760898Z",
          "shell.execute_reply": "2023-05-23T05:00:26.759179Z"
        },
        "papermill": {
          "duration": 0.064176,
          "end_time": "2023-05-23T05:00:26.763573",
          "exception": false,
          "start_time": "2023-05-23T05:00:26.699397",
          "status": "completed"
        },
        "tags": [],
        "id": "1f0a67a2"
      },
      "outputs": [],
      "source": [
        "# MathScore regression evaluation\n",
        "mse_math = mean_squared_error(y_math_test, y_math_pred)\n",
        "mae_math = mean_absolute_error(y_math_test, y_math_pred)\n",
        "r2_math = r2_score(y_math_test, y_math_pred)\n",
        "\n",
        "# ReadingScore regression evaluation\n",
        "mse_reading = mean_squared_error(y_reading_test, y_reading_pred)\n",
        "mae_reading = mean_absolute_error(y_reading_test, y_reading_pred)\n",
        "r2_reading = r2_score(y_reading_test, y_reading_pred)\n",
        "\n",
        "# WritingScore regression evaluation\n",
        "mse_writing = mean_squared_error(y_writing_test, y_writing_pred)\n",
        "mae_writing = mean_absolute_error(y_writing_test, y_writing_pred)\n",
        "r2_writing = r2_score(y_writing_test, y_writing_pred)\n",
        "\n",
        "# Print the evaluation metrics\n",
        "print()\n",
        "print(\"MathScore Regression:\")\n",
        "print(\"Mean Squared Error (MSE):\", mse_math)\n",
        "print(\"Mean Absolute Error (MAE):\", mae_math)\n",
        "print(\"R-squared (R2):\", r2_math)\n",
        "print()\n",
        "print(\"ReadingScore Regression:\")\n",
        "print(\"Mean Squared Error (MSE):\", mse_reading)\n",
        "print(\"Mean Absolute Error (MAE):\", mae_reading)\n",
        "print(\"R-squared (R2):\", r2_reading)\n",
        "print()\n",
        "print(\"WritingScore Regression:\")\n",
        "print(\"Mean Squared Error (MSE):\", mse_writing)\n",
        "print(\"Mean Absolute Error (MAE):\", mae_writing)\n",
        "print(\"R-squared (R2):\", r2_writing)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "06fa6926",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-05-23T05:00:26.950060Z",
          "iopub.status.busy": "2023-05-23T05:00:26.949590Z",
          "iopub.status.idle": "2023-05-23T05:00:26.972454Z",
          "shell.execute_reply": "2023-05-23T05:00:26.971464Z"
        },
        "papermill": {
          "duration": 0.0694,
          "end_time": "2023-05-23T05:00:26.975404",
          "exception": false,
          "start_time": "2023-05-23T05:00:26.906004",
          "status": "completed"
        },
        "tags": [],
        "id": "06fa6926"
      },
      "outputs": [],
      "source": [
        "# MathScore regression feature importances\n",
        "math_feature_importances = pd.DataFrame({'Feature': X_math.columns, 'Coefficient': reg_math.coef_})\n",
        "math_feature_importances = math_feature_importances.sort_values(by='Coefficient', ascending=False)\n",
        "\n",
        "# ReadingScore regression feature importances\n",
        "reading_feature_importances = pd.DataFrame({'Feature': X_reading.columns, 'Coefficient': reg_reading.coef_})\n",
        "reading_feature_importances = reading_feature_importances.sort_values(by='Coefficient', ascending=False)\n",
        "\n",
        "# WritingScore regression feature importances\n",
        "writing_feature_importances = pd.DataFrame({'Feature': X_writing.columns, 'Coefficient': reg_writing.coef_})\n",
        "writing_feature_importances = writing_feature_importances.sort_values(by='Coefficient', ascending=False)\n",
        "\n",
        "# Print feature importances for each model\n",
        "print(\"MathScore Regression Feature Importances:\")\n",
        "print(math_feature_importances)\n",
        "print()\n",
        "print(\"ReadingScore Regression Feature Importances:\")\n",
        "print(reading_feature_importances)\n",
        "print()\n",
        "print(\"WritingScore Regression Feature Importances:\")\n",
        "print(writing_feature_importances)"
      ]
    }
  ],
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