scaling-and-normalization.ipynb

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   "source": [
    "**This notebook is an exercise in the [Data Cleaning](https://www.kaggle.com/learn/data-cleaning) course.  You can reference the tutorial at [this link](https://www.kaggle.com/alexisbcook/scaling-and-normalization).**\n",
    "\n",
    "---\n"
   ]
  },
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   "source": [
    "# Get our environment set up\n",
    "\n",
    "To practice scaling and normalization, we're going to use a [dataset of Kickstarter campaigns](https://www.kaggle.com/kemical/kickstarter-projects). (Kickstarter is a website where people can ask people to invest in various projects and concept products.)\n",
    "\n",
    "The next code cell loads in the libraries and dataset we'll be using. "
   ]
  },
  {
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   "execution_count": 1,
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    "execution": {
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   "source": [
    "# modules we'll use\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# for Box-Cox Transformation\n",
    "from scipy import stats\n",
    "\n",
    "# for min_max scaling\n",
    "from mlxtend.preprocessing import minmax_scaling\n",
    "\n",
    "# plotting modules\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# read in all our data\n",
    "kickstarters_2017 = pd.read_csv(\"../input/kickstarter-projects/ks-projects-201801.csv\")\n",
    "\n",
    "# set seed for reproducibility\n",
    "np.random.seed(0)"
   ]
  },
  {
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   "id": "498428e4",
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    "tags": []
   },
   "source": [
    "Let's start by scaling the goals of each campaign, which is how much money they were asking for.  After scaling, all values lie between 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b77e88a0",
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/conda/lib/python3.7/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Scaled data')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# select the usd_goal_real column\n",
    "original_data = pd.DataFrame(kickstarters_2017.usd_goal_real)\n",
    "\n",
    "# scale the goals from 0 to 1\n",
    "scaled_data = minmax_scaling(original_data, columns=['usd_goal_real'])\n",
    "\n",
    "# plot the original & scaled data together to compare\n",
    "fig, ax=plt.subplots(1,2,figsize=(15,3))\n",
    "sns.distplot(kickstarters_2017.usd_goal_real, ax=ax[0])\n",
    "ax[0].set_title(\"Original Data\")\n",
    "sns.distplot(scaled_data, ax=ax[1])\n",
    "ax[1].set_title(\"Scaled data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "43522617",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-10T17:29:43.129104Z",
     "iopub.status.busy": "2022-10-10T17:29:43.128641Z",
     "iopub.status.idle": "2022-10-10T17:29:43.149642Z",
     "shell.execute_reply": "2022-10-10T17:29:43.147829Z"
    },
    "papermill": {
     "duration": 0.03079,
     "end_time": "2022-10-10T17:29:43.152806",
     "exception": false,
     "start_time": "2022-10-10T17:29:43.122016",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original data\n",
      "Preview:\n",
      "    usd_goal_real\n",
      "0        1533.95\n",
      "1       30000.00\n",
      "2       45000.00\n",
      "3        5000.00\n",
      "4       19500.00\n",
      "Minimum value: 0.01 \n",
      "Maximum value: 166361390.71\n",
      "______________________________\n",
      "\n",
      "Scaled data\n",
      "Preview:\n",
      "    usd_goal_real\n",
      "0       0.000009\n",
      "1       0.000180\n",
      "2       0.000270\n",
      "3       0.000030\n",
      "4       0.000117\n",
      "Minimum value: 0.0 \n",
      "Maximum value: 1.0\n"
     ]
    }
   ],
   "source": [
    "print('Original data\\nPreview:\\n', original_data.head())\n",
    "print('Minimum value:', float(original_data.min()),\n",
    "      '\\nMaximum value:', float(original_data.max()))\n",
    "print('_'*30)\n",
    "\n",
    "print('\\nScaled data\\nPreview:\\n', scaled_data.head())\n",
    "print('Minimum value:', float(scaled_data.min()),\n",
    "      '\\nMaximum value:', float(scaled_data.max()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c35c748a",
   "metadata": {
    "papermill": {
     "duration": 0.004738,
     "end_time": "2022-10-10T17:29:43.163109",
     "exception": false,
     "start_time": "2022-10-10T17:29:43.158371",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 1) Practice scaling\n",
    "\n",
    "We just scaled the \"usd_goal_real\" column. What about the \"goal\" column?\n",
    "\n",
    "Begin by running the code cell below to create a DataFrame `original_goal_data` containing the \"goal\" column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "510cb264",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-10T17:29:43.175807Z",
     "iopub.status.busy": "2022-10-10T17:29:43.174548Z",
     "iopub.status.idle": "2022-10-10T17:29:43.181134Z",
     "shell.execute_reply": "2022-10-10T17:29:43.180062Z"
    },
    "papermill": {
     "duration": 0.01546,
     "end_time": "2022-10-10T17:29:43.183668",
     "exception": false,
     "start_time": "2022-10-10T17:29:43.168208",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# select the usd_goal_real column\n",
    "original_goal_data = pd.DataFrame(kickstarters_2017.goal)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be4c3f45",
   "metadata": {
    "papermill": {
     "duration": 0.004832,
     "end_time": "2022-10-10T17:29:43.193670",
     "exception": false,
     "start_time": "2022-10-10T17:29:43.188838",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Use `original_goal_data` to create a new DataFrame `scaled_goal_data` with values scaled between 0 and 1. You must use the `minmax_scaling()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cbbfbad5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-10T17:29:43.206605Z",
     "iopub.status.busy": "2022-10-10T17:29:43.205558Z",
     "iopub.status.idle": "2022-10-10T17:29:43.232453Z",
     "shell.execute_reply": "2022-10-10T17:29:43.231318Z"
    },
    "papermill": {
     "duration": 0.036624,
     "end_time": "2022-10-10T17:29:43.235411",
     "exception": false,
     "start_time": "2022-10-10T17:29:43.198787",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "scaled_goal_data = minmax_scaling(original_goal_data, columns=['goal'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "189edc76",
   "metadata": {
    "papermill": {
     "duration": 0.004898,
     "end_time": "2022-10-10T17:29:43.246039",
     "exception": false,
     "start_time": "2022-10-10T17:29:43.241141",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 2) Practice normalization\n",
    "\n",
    "Now you'll practice normalization. We begin by normalizing the amount of money pledged to each campaign."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1ec70c4a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-10T17:29:43.258190Z",
     "iopub.status.busy": "2022-10-10T17:29:43.257762Z",
     "iopub.status.idle": "2022-10-10T17:29:47.430121Z",
     "shell.execute_reply": "2022-10-10T17:29:47.428829Z"
    },
    "papermill": {
     "duration": 4.181506,
     "end_time": "2022-10-10T17:29:47.432659",
     "exception": false,
     "start_time": "2022-10-10T17:29:43.251153",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/conda/lib/python3.7/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the index of all positive pledges (Box-Cox only takes positive values)\n",
    "index_of_positive_pledges = kickstarters_2017.usd_pledged_real > 0\n",
    "\n",
    "# get only positive pledges (using their indexes)\n",
    "positive_pledges = kickstarters_2017.usd_pledged_real.loc[index_of_positive_pledges]\n",
    "\n",
    "# normalize the pledges (w/ Box-Cox)\n",
    "normalized_pledges = pd.Series(stats.boxcox(positive_pledges)[0], \n",
    "                               name='usd_pledged_real', index=positive_pledges.index)\n",
    "\n",
    "# plot both together to compare\n",
    "fig, ax=plt.subplots(1,2,figsize=(15,3))\n",
    "sns.distplot(positive_pledges, ax=ax[0])\n",
    "ax[0].set_xscale('log')\n",
    "ax[0].set_title(\"Original Data\")\n",
    "sns.distplot(normalized_pledges, ax=ax[1])\n",
    "ax[1].set_title(\"Normalized data\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "063c2100",
   "metadata": {
    "papermill": {
     "duration": 0.005406,
     "end_time": "2022-10-10T17:29:47.443853",
     "exception": false,
     "start_time": "2022-10-10T17:29:47.438447",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The values have changed significantly with normalization!\n",
    "\n",
    "In the next code cell, you'll take a look at the distribution of the normalized data, where it should now resemble a normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "468d5042",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-10T17:29:47.457225Z",
     "iopub.status.busy": "2022-10-10T17:29:47.456813Z",
     "iopub.status.idle": "2022-10-10T17:29:47.471353Z",
     "shell.execute_reply": "2022-10-10T17:29:47.469742Z"
    },
    "papermill": {
     "duration": 0.024869,
     "end_time": "2022-10-10T17:29:47.474304",
     "exception": false,
     "start_time": "2022-10-10T17:29:47.449435",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original data\n",
      "Preview:\n",
      " 1     2421.0\n",
      "2      220.0\n",
      "3        1.0\n",
      "4     1283.0\n",
      "5    52375.0\n",
      "Name: usd_pledged_real, dtype: float64\n",
      "Minimum value: 0.45 \n",
      "Maximum value: 20338986.27\n",
      "______________________________\n",
      "\n",
      "Normalized data\n",
      "Preview:\n",
      " 1    10.165142\n",
      "2     6.468598\n",
      "3     0.000000\n",
      "4     9.129277\n",
      "5    15.836853\n",
      "Name: usd_pledged_real, dtype: float64\n",
      "Minimum value: -0.7779954122762203 \n",
      "Maximum value: 30.69054020451361\n"
     ]
    }
   ],
   "source": [
    "print('Original data\\nPreview:\\n', positive_pledges.head())\n",
    "print('Minimum value:', float(positive_pledges.min()),\n",
    "      '\\nMaximum value:', float(positive_pledges.max()))\n",
    "print('_'*30)\n",
    "\n",
    "print('\\nNormalized data\\nPreview:\\n', normalized_pledges.head())\n",
    "print('Minimum value:', float(normalized_pledges.min()),\n",
    "      '\\nMaximum value:', float(normalized_pledges.max()))"
   ]
  },
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   "source": [
    "We used the \"usd_pledged_real\" column. Follow the same process to normalize the \"pledged\" column. "
   ]
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   "source": [
    "How does the normalized \"usd_pledged_real\" column look different from when we normalized the \"pledged\" column?  Or, do they look mostly the same?\n",
    "\n",
    "Once you have an answer, run the code cell below."
   ]
  },
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "# (Optional) More practice\n",
    "\n",
    "Try finding a new dataset and pretend you're preparing to perform a [regression analysis](https://www.kaggle.com/rtatman/the-5-day-regression-challenge). \n",
    "\n",
    "[These datasets are a good start!](https://www.kaggle.com/rtatman/datasets-for-regression-analysis)\n",
    "\n",
    "Pick three or four variables and decide if you need to normalize or scale any of them and, if you think you should, practice applying the correct technique.\n",
    "\n",
    "# Keep going\n",
    "\n",
    "In the next lesson, learn how to [**parse dates**](https://www.kaggle.com/alexisbcook/parsing-dates) in a dataset."
   ]
  },
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   "id": "25521af9",
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     "start_time": "2022-10-10T17:29:47.515105",
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    "tags": []
   },
   "source": [
    "---\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "*Have questions or comments? Visit the [course discussion forum](https://www.kaggle.com/learn/data-cleaning/discussion) to chat with other learners.*"
   ]
  }
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