{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3ba52918",
   "metadata": {},
   "source": [
    "# Final Project Notebook\n",
    "\n",
    "Use the follow cells prompts to complete the final project for the course. Everything you need should be present in this notebook or previous notebooks we've used in class. You can work together as needed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Run the requirements file to load the required packages\n",
    "!pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cca2a44d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "95149c9b",
   "metadata": {
    "vscode": {
     "languageId": "markdown"
    }
   },
   "outputs": [],
   "source": [
    "## Systems"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5707e81a",
   "metadata": {
    "vscode": {
     "languageId": "markdown"
    }
   },
   "outputs": [],
   "source": [
    "### Question Fork the Repository\n",
    "i. Include a screenshot of the forked repo in your GitHub account\n",
    "\n",
    "To fork the repository:\n",
    "1. Go to https://github.com/NovaVolunteer/ds1001_final\n",
    "2. Click the \"Fork\" button in the top right corner\n",
    "3. The repo will be forked to your GitHub account\n",
    "4. Take a screenshot of your forked repository\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cde0ef0",
   "metadata": {
    "vscode": {
     "languageId": "markdown"
    }
   },
   "outputs": [],
   "source": [
    "### You should now be able to open the new repo in Google Collab "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7153d922",
   "metadata": {
    "vscode": {
     "languageId": "markdown"
    }
   },
   "outputs": [],
   "source": [
    "What version of scikit-learn is current in the working environment?\n",
    "i.\tIs that the latest version or not?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6572ef74",
   "metadata": {},
   "outputs": [],
   "source": [
    "### You can use this command to list all the packages in your environment\n",
    "!pip list\n",
    "#To list all packages with their latest available versions, you can use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "958994c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip list --outdated\n",
    "#This will show you all installed packages that have newer versions available, displaying both the current version and the latest version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "196ba293",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('your_dataset.csv')\n",
    "\n",
    "#a. How many rows are in the dataframe? How many columns?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a6964a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#b. How many numeric columns are in the data set?\n",
    "num_numeric_columns = df.select_dtypes(include=['number']).shape[1]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "finalproj",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
