4-vscode/1-data-science-tutorial.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Science in VS Code tutorial\n",
    "\n",
    "Let's walk through the complete [Data Science in VS Code tutorial](https://code.visualstudio.com/docs/datascience/data-science-tutorial) using this Jupyter Notebook to copy over the code, run it, and capture our observations for each step in markdown cells. Along the way, explore how we can take advantage of the Data Science Profile we setup (with the Data Wrangler extension) to make our data science workflow more efficient.\n",
    "\n",
    "For convenience, the dataset used for this tutorial (`titanic3.csv`) has already been downloaded to the repo under the `1-data/` folder. All you need to do is load it update the code to reference the right location."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 1. Set up a data science environment\n",
    "\n",
    "The [instructions](https://code.visualstudio.com/docs/datascience/data-science-tutorial#_set-up-a-data-science-environment) require you to setup the required environment manually. However, _this_ dev container is already configured with all the packages and libraries you need (under `requirements.txt`), specifically the following:\n",
    " - pandas\n",
    " - jupyter \n",
    " - seaborn\n",
    " - scikit-learn\n",
    " - keras\n",
    " - tensorflow\n",
    "Note that if you want to add any additional libraries (for your own exploration), make sure you update the `requirements.txt` file, save it, and rebuild the dev container to have them take effect.\n",
    "\n",
    "Once you open this notebook in your dev container (GitHub Codespaces or Docker Desktop) runtime, you should:\n",
    " - `Select Kernel` (top right) - gets a drop down of options\n",
    " - Select `Python Envionments` - gets you a list of available options\n",
    " - Select `Python 3.10.13` - or whatever shows as the recommended one in the list\n",
    " - Check that the `Select Kernel` box is now updated to show this runtime.\n",
    "\n",
    "You are now ready to run the code in this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 2. Prepare the data\n",
    "\n",
    "The [instructions](https://code.visualstudio.com/docs/datascience/data-science-tutorial#_prepare-the-data) start by loading the data from a downloaded CSV file. Let's modify that to use the location in this repo - and run just the first cell. This also validates that our Jupyter Notebook environment is working correctly.\n",
    "\n",
    "Since our dev container configuration contains the [Data Wrangler Extension](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.datawrangler), you'll notice that running the cell below gives you a **Open df** prompt below the `df.head()` code statement in the output. This is a feature of the Data Wrangler extension that allows you to view the data in a tabular format, and perform some basic data wrangling operations on it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
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       "      <td>29.00</td>\n",
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       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
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       "   pclass  survived                                             name     sex  \\\n",
       "0       1         1                    Allen, Miss. Elisabeth Walton  female   \n",
       "1       1         1                   Allison, Master. Hudson Trevor    male   \n",
       "2       1         0                     Allison, Miss. Helen Loraine  female   \n",
       "3       1         0             Allison, Mr. Hudson Joshua Creighton    male   \n",
       "4       1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
       "\n",
       "     age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
       "0  29.00      0      0   24160  211.3375       B5        S    2    NaN   \n",
       "1   0.92      1      2  113781  151.5500  C22 C26        S   11    NaN   \n",
       "2   2.00      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "3  30.00      1      2  113781  151.5500  C22 C26        S  NaN  135.0   \n",
       "4  25.00      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "\n",
       "                         home.dest  \n",
       "0                     St Louis, MO  \n",
       "1  Montreal, PQ / Chesterville, ON  \n",
       "2  Montreal, PQ / Chesterville, ON  \n",
       "3  Montreal, PQ / Chesterville, ON  \n",
       "4  Montreal, PQ / Chesterville, ON  "
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   "source": [
    "# 2.1 | Load the Data into a Pandas Dataframe\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.read_csv('./../1-data/titanic3.csv')\n",
    "\n",
    "# 2.2 | Have Pandas Show the First Few Rows of the Data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 3. Wrangle the data\n",
    "\n",
    "To get started wrangling the data, you have two options:\n",
    "1. [Use the **Variables** menu option](https://code.visualstudio.com/docs/datascience/data-science-tutorial#_prepare-the-data)in Visual Studio Code to open a pane that shows the data variables in your notebook runtime. You should see `df` (data frame) with a data viewer icon to the left (\"Show variable snapshot in data viewer\"). Click that to launch the data view.\n",
    "2. Open the Data Wrangler view by clicking the **Open df** button in the code cell directly. This should launch the data viewer in the same way as the first option.\n",
    "\n",
    "Either way, you get a Data Viewer pane that you can open in **Editing Mode** to explore  [Data Wrangler operations](https://code.visualstudio.com/docs/datascience/data-wrangler#_data-wrangler-operations) like: \n",
    "- Sorting the data\n",
    "- Filtering the data based on a condition\n",
    "- Dropping, selecting, renaming, cloning, or creating - data columns\n",
    "- Finding and replacing values, or filling in missing values\n",
    "- String manipulation (e.g., splitting, capitalizing, transforming, etc.)\n",
    "\n",
    "Every operation will effectively write _code_ in the window below the data viewer, which you can then copy over to your notebook (with a click) or to your application script (via clipboard). You can also choose to [export the changed data](https://code.visualstudio.com/docs/datascience/data-wrangler#_edit-and-export-code) to a new data file (CSV) in your workspace. Note that all operations are simply working on a _copy_ of the data in the viewer, to showcase code required, so your input datafile remains unchanged.\n",
    "\n",
    "**For Example:**\n",
    "In the code below, I used Data Wrangler to filter the columns for _female_ passengers only, sort the results _by descending age_ and group it by _survived_ status. Then asked it to export the code for changes back to the Jupyter Notebook to get the code below. I can then comment out specific lines (e.g., groupby) and run the code, to see the output visualized as required. **Note that my original datafile remains unchanged**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "      <th>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
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       "      <th>61</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cavendish, Mrs. Tyrell William (Julia Florence...</td>\n",
       "      <td>female</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19877</td>\n",
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       "      <td>C46</td>\n",
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       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Little Onn Hall, Staffs</td>\n",
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       "      <th>78</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Compton, Mrs. Alexander Taylor (Mary Eliza Ing...</td>\n",
       "      <td>female</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>PC 17756</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>E45</td>\n",
       "      <td>C</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lakewood, NJ</td>\n",
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       "      <th>83</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Crosby, Mrs. Edward Gifford (Catherine Elizabe...</td>\n",
       "      <td>female</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>112901</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>B26</td>\n",
       "      <td>S</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Milwaukee, WI</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Andrews, Miss. Kornelia Theodosia</td>\n",
       "      <td>female</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13502</td>\n",
       "      <td>77.9583</td>\n",
       "      <td>D7</td>\n",
       "      <td>S</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hudson, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Straus, Mrs. Isidor (Rosalie Ida Blun)</td>\n",
       "      <td>female</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17483</td>\n",
       "      <td>221.7792</td>\n",
       "      <td>C55 C57</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York, NY</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "     pclass  survived                                               name  \\\n",
       "61        1         1  Cavendish, Mrs. Tyrell William (Julia Florence...   \n",
       "78        1         1  Compton, Mrs. Alexander Taylor (Mary Eliza Ing...   \n",
       "83        1         1  Crosby, Mrs. Edward Gifford (Catherine Elizabe...   \n",
       "6         1         1                  Andrews, Miss. Kornelia Theodosia   \n",
       "286       1         0             Straus, Mrs. Isidor (Rosalie Ida Blun)   \n",
       "\n",
       "        sex   age  sibsp  parch    ticket      fare    cabin embarked boat  \\\n",
       "61   female  76.0      1      0     19877   78.8500      C46        S    6   \n",
       "78   female  64.0      0      2  PC 17756   83.1583      E45        C   14   \n",
       "83   female  64.0      1      1    112901   26.5500      B26        S    7   \n",
       "6    female  63.0      1      0     13502   77.9583       D7        S   10   \n",
       "286  female  63.0      1      0  PC 17483  221.7792  C55 C57        S  NaN   \n",
       "\n",
       "     body                home.dest  \n",
       "61    NaN  Little Onn Hall, Staffs  \n",
       "78    NaN             Lakewood, NJ  \n",
       "83    NaN            Milwaukee, WI  \n",
       "6     NaN               Hudson, NY  \n",
       "286   NaN             New York, NY  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Cell generated by Data Wrangler.\n",
    "\"\"\"\n",
    "def clean_data(df):\n",
    "    # Filter rows based on column: 'sex'\n",
    "    df = df[df['sex'] == \"female\"]\n",
    "    \n",
    "    # Sort by column: 'age' (descending)\n",
    "    df = df.sort_values(['age'], ascending=[False])\n",
    "    # Grouped on column: 'survived'\n",
    "    # df = df.groupby(['survived']).count().reset_index()[['survived']]\n",
    "    return df\n",
    "\n",
    "df_clean = clean_data(df.copy())\n",
    "df_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 4. Visualize the Data\n",
    "\n",
    "Let's go back to the original dataset for now (or you can use the data frame from the wrangled set if you choose) and follow the [Visual Studio Code Tutorial](https://code.visualstudio.com/docs/datascience/data-science-tutorial#_prepare-the-data) **from step 9** to get a sense for how the different data attributes (columns) related to survival status. Note that our tutorial uses the variable `df` to reference the data frame (vs. `data` in the tutorial).\n",
    "\n",
    "Once you run the code, the output visualization pane will give you options to save the images to a file, copy to clipboard, or expand them for a full-screen view."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='survived', ylabel='fare'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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     "data": {
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",
      "text/plain": [
       "<Figure size 3000x500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reload the original data\n",
    "df = pd.read_csv('./../1-data/titanic3.csv')\n",
    "\n",
    "## (Step 9) Make data corrections for missing values and data types\n",
    "df.replace('?', np.nan, inplace= True)\n",
    "df = df.astype({\"age\": np.float64, \"fare\": np.float64})\n",
    "\n",
    "# (Step 10) Use the seaborn and matplot libraries to visualize the data\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axs = plt.subplots(ncols=5, figsize=(30,5))\n",
    "sns.violinplot(x=\"survived\", y=\"age\", hue=\"sex\", data=df, ax=axs[0])\n",
    "sns.pointplot(x=\"sibsp\", y=\"survived\", hue=\"sex\", data=df, ax=axs[1])\n",
    "sns.pointplot(x=\"parch\", y=\"survived\", hue=\"sex\", data=df, ax=axs[2])\n",
    "sns.pointplot(x=\"pclass\", y=\"survived\", hue=\"sex\", data=df, ax=axs[3])\n",
    "sns.violinplot(x=\"survived\", y=\"fare\", hue=\"sex\", data=df, ax=axs[4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 5. Explore Correlation with Pandas\n",
    "\n",
    "Let's continue with steps 11 and 12 of the [Visual Studio Code Tutorial](https://code.visualstudio.com/docs/datascience/data-science-tutorial#_prepare-the-data) to complete our data analysis journey.\n",
    "\n",
    "Correlation requires us to have numerical data - but the `sex` column is categorical. So we need to convert it to a numerical value. We can use string manipulation directly (replace male and female strings with values) then analyze the correlation using the `corr()` method as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3587/1421770689.py:2: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  df.replace({'male': 1, 'female': 0}, inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pclass</th>\n",
       "      <td>0.312469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>survived</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>0.528693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.055512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sibsp</th>\n",
       "      <td>0.027825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parch</th>\n",
       "      <td>0.082660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fare</th>\n",
       "      <td>0.244265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>body</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          survived\n",
       "pclass    0.312469\n",
       "survived  1.000000\n",
       "sex       0.528693\n",
       "age       0.055512\n",
       "sibsp     0.027825\n",
       "parch     0.082660\n",
       "fare      0.244265\n",
       "body           NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (Step 11) Map string variables to numbers for the gender attribute\n",
    "df.replace({'male': 1, 'female': 0}, inplace=True)\n",
    "\n",
    "## Now analyze correlation between all passenger attributes and their surival status\n",
    "df.corr(numeric_only=True).abs()[[\"survived\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's keep going with [step 13](https://code.visualstudio.com/docs/datascience/data-science-tutorial#_prepare-the-data) - the above correlation indicates sex (gender) correlated closely with the changes of survival - but attributes like sibsp (siblings or spouse) and parch (parents or children) were not. Say we want to validate the hypothesis that _\"having a relative on board increased chances of survival\"_ - we can create a new variable that holds a relevant value and then analyze the correlation again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pclass</th>\n",
       "      <td>0.312469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>survived</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>0.528693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.055512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sibsp</th>\n",
       "      <td>0.027825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parch</th>\n",
       "      <td>0.082660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fare</th>\n",
       "      <td>0.244265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>body</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relatives</th>\n",
       "      <td>0.201719</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           survived\n",
       "pclass     0.312469\n",
       "survived   1.000000\n",
       "sex        0.528693\n",
       "age        0.055512\n",
       "sibsp      0.027825\n",
       "parch      0.082660\n",
       "fare       0.244265\n",
       "body            NaN\n",
       "relatives  0.201719"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new column that has a boolean (yes/no) value for whether the passenger had any relatives on board\n",
    "# computed based on whether they had siblings, spouses, parents, or children on board.\n",
    "df['relatives'] = df.apply (lambda row: int((row['sibsp'] + row['parch']) > 0), axis=1)\n",
    "df.corr(numeric_only=True).abs()[[\"survived\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows us that survivabilty has a higher correlation to the \"existence of any relative\" rather than to the specific number or type of relative. So, to train a model, we can remove less-correlated columns and just keep the ones that have the higher correlation - and we can remove any rows that had **NaN** values in them - to get out final training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# (Step 12) Remove all rows with missing values, and drop less correlated columns\n",
    "df = df[['sex', 'pclass','age','relatives','fare','survived']].dropna()\n",
    "\n",
    "# We are now ready for training."
   ]
  },
  {
   "cell_type": "markdown",
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    "---\n"
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    "# Train and evaluate a model\n",
    "\n",
    "Try exploring [this section of the tutorial](https://code.visualstudio.com/docs/datascience/data-science-tutorial#_train-and-evaluate-a-model) on your own by adding the code cells one at a time, and documenting your experiences with markdown cells. We will revisit this section later on when we explore tools like GitHub Copilot and Responsible AI. For now, this section will help you explore the first steps in machine learning with Python."
   ]
  }
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