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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1c5ab710",
+   "metadata": {},
+   "source": [
+    "# Cleaning Data Exercises\n",
+    "\n",
+    "For the exercises, you will be cleaning data in your chosen data set. [Towards Data Science](https://towardsdatascience.com/data-cleaning-in-python-the-ultimate-guide-2020-c63b88bf0a0d) outlines the steps we should take to clean data using a different data set from Kaggle. While you should use this article as a reference, make sure not to copy paste as the two data sets are very different!\n",
+    "\n",
+    "To start cleaning data, we first need to create a dataframe from the CSV and print out any relevant info to make sure our dataframe is ready to go."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81717993",
+   "metadata": {},
+   "source": [
+    "Now you have a dataframe set up, you are ready to dive in and start cleaning!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "id": "da3513e0",
+   "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>Unnamed: 0</th>\n",
+       "      <th>Clothing ID</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Title</th>\n",
+       "      <th>Review Text</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>Recommended IND</th>\n",
+       "      <th>Positive Feedback Count</th>\n",
+       "      <th>Division Name</th>\n",
+       "      <th>Department Name</th>\n",
+       "      <th>Class Name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>767</td>\n",
+       "      <td>33</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Absolutely wonderful - silky and sexy and comf...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Initmates</td>\n",
+       "      <td>Intimate</td>\n",
+       "      <td>Intimates</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1080</td>\n",
+       "      <td>34</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>General</td>\n",
+       "      <td>Dresses</td>\n",
+       "      <td>Dresses</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1077</td>\n",
+       "      <td>60</td>\n",
+       "      <td>Some major design flaws</td>\n",
+       "      <td>I had such high hopes for this dress and reall...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>General</td>\n",
+       "      <td>Dresses</td>\n",
+       "      <td>Dresses</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1049</td>\n",
+       "      <td>50</td>\n",
+       "      <td>My favorite buy!</td>\n",
+       "      <td>I love, love, love this jumpsuit. it's fun, fl...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>General Petite</td>\n",
+       "      <td>Bottoms</td>\n",
+       "      <td>Pants</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>847</td>\n",
+       "      <td>47</td>\n",
+       "      <td>Flattering shirt</td>\n",
+       "      <td>This shirt is very flattering to all due to th...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>General</td>\n",
+       "      <td>Tops</td>\n",
+       "      <td>Blouses</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23481</th>\n",
+       "      <td>23481</td>\n",
+       "      <td>1104</td>\n",
+       "      <td>34</td>\n",
+       "      <td>Great dress for many occasions</td>\n",
+       "      <td>I was very happy to snag this dress at such a ...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>General Petite</td>\n",
+       "      <td>Dresses</td>\n",
+       "      <td>Dresses</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23482</th>\n",
+       "      <td>23482</td>\n",
+       "      <td>862</td>\n",
+       "      <td>48</td>\n",
+       "      <td>Wish it was made of cotton</td>\n",
+       "      <td>It reminds me of maternity clothes. soft, stre...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>General Petite</td>\n",
+       "      <td>Tops</td>\n",
+       "      <td>Knits</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23483</th>\n",
+       "      <td>23483</td>\n",
+       "      <td>1104</td>\n",
+       "      <td>31</td>\n",
+       "      <td>Cute, but see through</td>\n",
+       "      <td>This fit well, but the top was very see throug...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>General Petite</td>\n",
+       "      <td>Dresses</td>\n",
+       "      <td>Dresses</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23484</th>\n",
+       "      <td>23484</td>\n",
+       "      <td>1084</td>\n",
+       "      <td>28</td>\n",
+       "      <td>Very cute dress, perfect for summer parties an...</td>\n",
+       "      <td>I bought this dress for a wedding i have this ...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>General</td>\n",
+       "      <td>Dresses</td>\n",
+       "      <td>Dresses</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23485</th>\n",
+       "      <td>23485</td>\n",
+       "      <td>1104</td>\n",
+       "      <td>52</td>\n",
+       "      <td>Please make more like this one!</td>\n",
+       "      <td>This dress in a lovely platinum is feminine an...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>22</td>\n",
+       "      <td>General Petite</td>\n",
+       "      <td>Dresses</td>\n",
+       "      <td>Dresses</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>23486 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Unnamed: 0  Clothing ID  Age  \\\n",
+       "0               0          767   33   \n",
+       "1               1         1080   34   \n",
+       "2               2         1077   60   \n",
+       "3               3         1049   50   \n",
+       "4               4          847   47   \n",
+       "...           ...          ...  ...   \n",
+       "23481       23481         1104   34   \n",
+       "23482       23482          862   48   \n",
+       "23483       23483         1104   31   \n",
+       "23484       23484         1084   28   \n",
+       "23485       23485         1104   52   \n",
+       "\n",
+       "                                                   Title  \\\n",
+       "0                                                    NaN   \n",
+       "1                                                    NaN   \n",
+       "2                                Some major design flaws   \n",
+       "3                                       My favorite buy!   \n",
+       "4                                       Flattering shirt   \n",
+       "...                                                  ...   \n",
+       "23481                     Great dress for many occasions   \n",
+       "23482                         Wish it was made of cotton   \n",
+       "23483                              Cute, but see through   \n",
+       "23484  Very cute dress, perfect for summer parties an...   \n",
+       "23485                    Please make more like this one!   \n",
+       "\n",
+       "                                             Review Text  Rating  \\\n",
+       "0      Absolutely wonderful - silky and sexy and comf...       4   \n",
+       "1      Love this dress!  it's sooo pretty.  i happene...       5   \n",
+       "2      I had such high hopes for this dress and reall...       3   \n",
+       "3      I love, love, love this jumpsuit. it's fun, fl...       5   \n",
+       "4      This shirt is very flattering to all due to th...       5   \n",
+       "...                                                  ...     ...   \n",
+       "23481  I was very happy to snag this dress at such a ...       5   \n",
+       "23482  It reminds me of maternity clothes. soft, stre...       3   \n",
+       "23483  This fit well, but the top was very see throug...       3   \n",
+       "23484  I bought this dress for a wedding i have this ...       3   \n",
+       "23485  This dress in a lovely platinum is feminine an...       5   \n",
+       "\n",
+       "       Recommended IND  Positive Feedback Count   Division Name  \\\n",
+       "0                    1                        0       Initmates   \n",
+       "1                    1                        4         General   \n",
+       "2                    0                        0         General   \n",
+       "3                    1                        0  General Petite   \n",
+       "4                    1                        6         General   \n",
+       "...                ...                      ...             ...   \n",
+       "23481                1                        0  General Petite   \n",
+       "23482                1                        0  General Petite   \n",
+       "23483                0                        1  General Petite   \n",
+       "23484                1                        2         General   \n",
+       "23485                1                       22  General Petite   \n",
+       "\n",
+       "      Department Name Class Name  \n",
+       "0            Intimate  Intimates  \n",
+       "1             Dresses    Dresses  \n",
+       "2             Dresses    Dresses  \n",
+       "3             Bottoms      Pants  \n",
+       "4                Tops    Blouses  \n",
+       "...               ...        ...  \n",
+       "23481         Dresses    Dresses  \n",
+       "23482            Tops      Knits  \n",
+       "23483         Dresses    Dresses  \n",
+       "23484         Dresses    Dresses  \n",
+       "23485         Dresses    Dresses  \n",
+       "\n",
+       "[23486 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Import pandas and any other libraries you need here. HINT: Check out the article to see what they imported to get started!\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import matplotlib.mlab as mlab\n",
+    "import matplotlib\n",
+    "plt.style.use('ggplot')\n",
+    "%matplotlib inline\n",
+    "matplotlib.rcParams['figure.figsize'] = (12,8)\n",
+    "pd.options.mode.chained_assignment = None\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Create a new dataframe from your CSV\n",
+    "df = pd.read_csv('data/Womens Clothing E-Commerce Reviews.csv')\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "045387eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(23486, 11)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print out any information you need to understand your dataframe\n",
+    "print(df.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "id": "d9a77e34",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 23486 entries, 0 to 23485\n",
+      "Data columns (total 11 columns):\n",
+      " #   Column                   Non-Null Count  Dtype \n",
+      "---  ------                   --------------  ----- \n",
+      " 0   Unnamed: 0               23486 non-null  int64 \n",
+      " 1   Clothing ID              23486 non-null  int64 \n",
+      " 2   Age                      23486 non-null  int64 \n",
+      " 3   Title                    19676 non-null  object\n",
+      " 4   Review Text              22641 non-null  object\n",
+      " 5   Rating                   23486 non-null  int64 \n",
+      " 6   Recommended IND          23486 non-null  int64 \n",
+      " 7   Positive Feedback Count  23486 non-null  int64 \n",
+      " 8   Division Name            23472 non-null  object\n",
+      " 9   Department Name          23472 non-null  object\n",
+      " 10  Class Name               23472 non-null  object\n",
+      "dtypes: int64(6), object(5)\n",
+      "memory usage: 2.0+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "303aee35",
+   "metadata": {},
+   "source": [
+    "## Missing Data\n",
+    "\n",
+    "In the Towards Data Science article, they list 3 techniques for detecting any missing data frame. Try all three before deciding on a way to solve any missing data issues."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "id": "f359e5b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cols = df.columns[:30] \n",
+    "colours = ['#000099', '#ffff00'] \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "68fd548c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot a heatmap of any missing data\n",
+    "sns.heatmap(df[cols].isnull(), cmap=sns.color_palette(colours))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "id": "0ae37b5d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unnamed: 0 - 0%\n",
+      "Clothing ID - 0%\n",
+      "Age - 0%\n",
+      "Title - 16%\n",
+      "Review Text - 4%\n",
+      "Rating - 0%\n",
+      "Recommended IND - 0%\n",
+      "Positive Feedback Count - 0%\n",
+      "Division Name - 0%\n",
+      "Department Name - 0%\n",
+      "Class Name - 0%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Try out a missing data percentage list! \n",
+    "for col in df.columns:\n",
+    "    pct_missing = np.mean(df[col].isnull())\n",
+    "    print('{} - {}%'.format(col, round(pct_missing*100)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "id": "51c62f02",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "created missing indicator for: Title\n",
+      "created missing indicator for: Review Text\n",
+      "created missing indicator for: Division Name\n",
+      "created missing indicator for: Department Name\n",
+      "created missing indicator for: Class Name\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Plot a missing data histogram\n",
+    "for col in df.columns:\n",
+    "    missing = df[col].isnull()\n",
+    "    num_missing = np.sum(missing)\n",
+    "    \n",
+    "    if num_missing > 0:  \n",
+    "        print('created missing indicator for: {}'.format(col))\n",
+    "        df['{}_ismissing'.format(col)] = missing\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "id": "eb77a11e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='index'>"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ismissing_cols = [col for col in df.columns if 'ismissing' in col]\n",
+    "df['num_missing'] = df[ismissing_cols].sum(axis=1)\n",
+    "\n",
+    "df['num_missing'].value_counts().reset_index().sort_values(by='index').plot.bar(x='index', y='num_missing')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e791fbe",
+   "metadata": {},
+   "source": [
+    "Now that you have tried all 3 techniques for detecting missing data, did you find any? Reading through the article, which solution do you think will help you clean the data set and handle the missing data? Try out your solution below!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "id": "53c2d34c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 179,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['Title'] = df['Title'].fillna('_MISSING_')\n",
+    "df['Review Text'] = df['Review Text'].fillna('_MISSING_')\n",
+    "sns.heatmap(df[cols].isnull(), cmap=sns.color_palette(colours))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "id": "8a202353",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 23486 entries, 0 to 23485\n",
+      "Data columns (total 17 columns):\n",
+      " #   Column                     Non-Null Count  Dtype \n",
+      "---  ------                     --------------  ----- \n",
+      " 0   Unnamed: 0                 23486 non-null  int64 \n",
+      " 1   Clothing ID                23486 non-null  int64 \n",
+      " 2   Age                        23486 non-null  int64 \n",
+      " 3   Title                      23486 non-null  object\n",
+      " 4   Review Text                23486 non-null  object\n",
+      " 5   Rating                     23486 non-null  int64 \n",
+      " 6   Recommended IND            23486 non-null  int64 \n",
+      " 7   Positive Feedback Count    23486 non-null  int64 \n",
+      " 8   Division Name              23472 non-null  object\n",
+      " 9   Department Name            23472 non-null  object\n",
+      " 10  Class Name                 23472 non-null  object\n",
+      " 11  Title_ismissing            23486 non-null  bool  \n",
+      " 12  Review Text_ismissing      23486 non-null  bool  \n",
+      " 13  Division Name_ismissing    23486 non-null  bool  \n",
+      " 14  Department Name_ismissing  23486 non-null  bool  \n",
+      " 15  Class Name_ismissing       23486 non-null  bool  \n",
+      " 16  num_missing                23486 non-null  int64 \n",
+      "dtypes: bool(5), int64(7), object(5)\n",
+      "memory usage: 2.3+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "id": "3a4f2cff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<AxesSubplot:title={'center':'Unnamed: 0'}>,\n",
+       "        <AxesSubplot:title={'center':'Clothing ID'}>,\n",
+       "        <AxesSubplot:title={'center':'Age'}>],\n",
+       "       [<AxesSubplot:title={'center':'Rating'}>,\n",
+       "        <AxesSubplot:title={'center':'Recommended IND'}>,\n",
+       "        <AxesSubplot:title={'center':'Positive Feedback Count'}>],\n",
+       "       [<AxesSubplot:title={'center':'num_missing'}>, <AxesSubplot:>,\n",
+       "        <AxesSubplot:>]], dtype=object)"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot a histogram to see if there are any outliers.\n",
+    "df.hist(bins=100)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "id": "f118d818",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 182,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[\"Age\"].hist(bins=50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be2121df",
+   "metadata": {},
+   "source": [
+    "## Irregular Data\n",
+    "\n",
+    "With missing data out of the way, turn your attention to any outliers. Just as we did for missing data, we first need to detect the outliers. The article outlines the top techniques for finding outliers in a dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "id": "31e09266",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 183,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.boxplot(column=['Age'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "id": "ae9008bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    23486.000000\n",
+       "mean        43.198544\n",
+       "std         12.279544\n",
+       "min         18.000000\n",
+       "25%         34.000000\n",
+       "50%         41.000000\n",
+       "75%         52.000000\n",
+       "max         99.000000\n",
+       "Name: Age, dtype: float64"
+      ]
+     },
+     "execution_count": 184,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Use the describe() method\n",
+    "df[\"Age\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "id": "4af44edf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 185,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot a bar chart\n",
+    "\n",
+    "df[\"Age\"].value_counts().plot.bar()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "id": "3ee7d694",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Int64Index([  659,  3023,  5901,  6543,  8078,  8327,  9513, 10617, 10686,\n",
+       "            11545, 12250, 12631, 14997, 15372, 15802, 17412, 17578, 18036,\n",
+       "            18563, 19304],\n",
+       "           dtype='int64')"
+      ]
+     },
+     "execution_count": 186,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Handle any outliers in your dataframe\n",
+    "df[df['Age'] > 88].index\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "id": "e15a605e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Unnamed: 0                                                                3023\n",
+       "Clothing ID                                                                302\n",
+       "Age                                                                         89\n",
+       "Title                                                                _MISSING_\n",
+       "Review Text                  Love the fabric, fits true to size, great qual...\n",
+       "Rating                                                                       5\n",
+       "Recommended IND                                                              1\n",
+       "Positive Feedback Count                                                      0\n",
+       "Division Name                                                        Initmates\n",
+       "Department Name                                                       Intimate\n",
+       "Class Name                                                                Swim\n",
+       "Title_ismissing                                                           True\n",
+       "Review Text_ismissing                                                    False\n",
+       "Division Name_ismissing                                                  False\n",
+       "Department Name_ismissing                                                False\n",
+       "Class Name_ismissing                                                     False\n",
+       "num_missing                                                                  1\n",
+       "Name: 3023, dtype: object"
+      ]
+     },
+     "execution_count": 187,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.iloc[3023]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "id": "90f01b1e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.drop(df[df['Age'] > 88].index, inplace= True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "id": "3d952095",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 195,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[\"Age\"].hist(bins=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da8d6080",
+   "metadata": {},
+   "source": [
+    "Which of the three techniques helped you find any outliers? Now that you have found outliers, what will you do to solve the problem?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c802522d",
+   "metadata": {},
+   "source": [
+    "## Unnecessary Data\n",
+    "\n",
+    "Unnecessary data could be duplicates, irrelevant, or any uninformative data. Dive into each type of unnecessary data to see if there is any in the data set. Make sure to make note of how you would handle each type of unnecessary data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "id": "8de40f50",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Review Text_ismissing: 96.40331%\n",
+      "False    22622\n",
+      "True       844\n",
+      "Name: Review Text_ismissing, dtype: int64\n",
+      "\n",
+      "Division Name_ismissing: 99.94034%\n",
+      "False    23452\n",
+      "True        14\n",
+      "Name: Division Name_ismissing, dtype: int64\n",
+      "\n",
+      "Department Name_ismissing: 99.94034%\n",
+      "False    23452\n",
+      "True        14\n",
+      "Name: Department Name_ismissing, dtype: int64\n",
+      "\n",
+      "Class Name_ismissing: 99.94034%\n",
+      "False    23452\n",
+      "True        14\n",
+      "Name: Class Name_ismissing, dtype: int64\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Look for any irrelevant data in the dataframe. How will you handle it?\n",
+    "num_rows = len(df.index)\n",
+    "low_information_cols = [] \n",
+    "\n",
+    "for col in df.columns:\n",
+    "    cnts = df[col].value_counts(dropna=False)\n",
+    "    top_pct = (cnts/num_rows).iloc[0]\n",
+    "    \n",
+    "    if top_pct > 0.95:\n",
+    "        low_information_cols.append(col)\n",
+    "        print('{0}: {1:.5f}%'.format(col, top_pct*100))\n",
+    "        print(cnts)\n",
+    "        print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "id": "888b49f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(23466, 17)\n",
+      "(23466, 16)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Look for duplicates. How will you handle any duplicates?\n",
+    "df_dedupped = df.drop('Clothing ID', axis=1).drop_duplicates()\n",
+    "print(df.shape)\n",
+    "print(df_dedupped.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cbd6ee14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Think about what data in the dataframe may be uninformative. Make note here of what situations may render data uninformative and how you would handle it?\n",
+    "# duplicate observations or irrelevant observations will mke our datasets uninformative,also Structural errors are when you measure or transfer data and notice strange naming conventions, typos, or incorrect capitalization. These inconsistencies can cause mislabeled categories or classes .\n",
+    "# and we should Handle missing data to make our data  more informative."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdcad93a",
+   "metadata": {},
+   "source": [
+    "## Inconsistent Data\n",
+    "\n",
+    "Inconsistent data is anything that messes with your model. This is likely due to inconsistent formatting and can be addressed by re-formatting all values in a column or row."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "id": "4fc6bbb7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "general           13836\n",
+       "general petite     8117\n",
+       "initmates          1499\n",
+       "NaN                  14\n",
+       "Name: Division Name, dtype: int64"
+      ]
+     },
+     "execution_count": 210,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Try to convert all the strings in a column to lower or upper case.\n",
+    "df['Division Name'] = df['Division Name'].str.lower()\n",
+    "df['Division Name'].value_counts(dropna=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "id": "16bdf90f",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "_missing_                                    3803\n",
+       "love it!                                      135\n",
+       "beautiful                                      95\n",
+       "love                                           88\n",
+       "love!                                          84\n",
+       "                                             ... \n",
+       "summer favorite                                 1\n",
+       "cute shirt but the sewing is slightly off       1\n",
+       "little something different for a t-shirt        1\n",
+       "dress for the younger girls                     1\n",
+       "flattering romper                               1\n",
+       "Name: Title, Length: 13987, dtype: int64"
+      ]
+     },
+     "execution_count": 209,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "df['Title'] = df['Title'].str.lower()\n",
+    "df['Title'].value_counts(dropna=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 217,
+   "id": "cfa0c388",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dresses           6312\n",
+       "knits             4841\n",
+       "blouses           3097\n",
+       "sweaters          1426\n",
+       "pants             1388\n",
+       "jeans             1144\n",
+       "fine gauge        1099\n",
+       "skirts             944\n",
+       "jackets            704\n",
+       "lounge             690\n",
+       "swim               347\n",
+       "outerwear          328\n",
+       "shorts             317\n",
+       "sleep              228\n",
+       "legwear            165\n",
+       "intimates          154\n",
+       "layering           146\n",
+       "trend              119\n",
+       "NaN                 14\n",
+       "casual bottoms       2\n",
+       "chemises             1\n",
+       "Name: Class Name, dtype: int64"
+      ]
+     },
+     "execution_count": 217,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Class Name\"] = df['Class Name'].str.lower()\n",
+    "df['Class Name'].value_counts(dropna=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c49b9ad4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}