Recommendations_with_IBM.ipynb

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    "# Recommendations with IBM\n",
    "\n",
    "For this project I analyze the interactions that users have with articles on the IBM Watson Studio platform, and make recommendations to them about new articles they will like.\n",
    "\n",
    "\n",
    "## Table of Contents\n",
    "\n",
    "I. [Exploratory Data Analysis](#Exploratory-Data-Analysis)<br>\n",
    "II. [Rank Based Recommendations](#Rank)<br>\n",
    "III. [User-User Based Collaborative Filtering](#User-User)<br>\n",
    "IV. [Content Based Recommendations (EXTRA - NOT REQUIRED)](#Content-Recs)<br>\n",
    "V. [Matrix Factorization](#Matrix-Fact)<br>\n",
    "VI. [Extras & Concluding](#conclusions)<br>\n",
    "VII. [Resources](#resources)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>article_id</th>\n",
       "      <th>title</th>\n",
       "      <th>email</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1430.0</td>\n",
       "      <td>using pixiedust for fast, flexible, and easier...</td>\n",
       "      <td>ef5f11f77ba020cd36e1105a00ab868bbdbf7fe7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1314.0</td>\n",
       "      <td>healthcare python streaming application demo</td>\n",
       "      <td>083cbdfa93c8444beaa4c5f5e0f5f9198e4f9e0b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1429.0</td>\n",
       "      <td>use deep learning for image classification</td>\n",
       "      <td>b96a4f2e92d8572034b1e9b28f9ac673765cd074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1338.0</td>\n",
       "      <td>ml optimization using cognitive assistant</td>\n",
       "      <td>06485706b34a5c9bf2a0ecdac41daf7e7654ceb7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1276.0</td>\n",
       "      <td>deploy your python model as a restful api</td>\n",
       "      <td>f01220c46fc92c6e6b161b1849de11faacd7ccb2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   article_id                                              title  \\\n",
       "0      1430.0  using pixiedust for fast, flexible, and easier...   \n",
       "1      1314.0       healthcare python streaming application demo   \n",
       "2      1429.0         use deep learning for image classification   \n",
       "3      1338.0          ml optimization using cognitive assistant   \n",
       "4      1276.0          deploy your python model as a restful api   \n",
       "\n",
       "                                      email  \n",
       "0  ef5f11f77ba020cd36e1105a00ab868bbdbf7fe7  \n",
       "1  083cbdfa93c8444beaa4c5f5e0f5f9198e4f9e0b  \n",
       "2  b96a4f2e92d8572034b1e9b28f9ac673765cd074  \n",
       "3  06485706b34a5c9bf2a0ecdac41daf7e7654ceb7  \n",
       "4  f01220c46fc92c6e6b161b1849de11faacd7ccb2  "
      ]
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     "execution_count": 108,
     "metadata": {},
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   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import project_tests as t\n",
    "import pickle\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.read_csv('data/user-item-interactions.csv')\n",
    "df_content = pd.read_csv('data/articles_community.csv')\n",
    "del df['Unnamed: 0']\n",
    "del df_content['Unnamed: 0']\n",
    "\n",
    "# Show df to get an idea of the data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": false
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       "      <th>doc_full_name</th>\n",
       "      <th>doc_status</th>\n",
       "      <th>article_id</th>\n",
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       "      <th>0</th>\n",
       "      <td>Skip navigation Sign in SearchLoading...\\r\\n\\r...</td>\n",
       "      <td>Detect bad readings in real time using Python ...</td>\n",
       "      <td>Detect Malfunctioning IoT Sensors with Streami...</td>\n",
       "      <td>Live</td>\n",
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       "      <td>No Free Hunch Navigation * kaggle.com\\r\\n\\r\\n ...</td>\n",
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       "      <td>Communicating data science: A guide to present...</td>\n",
       "      <td>Live</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>☰ * Login\\r\\n * Sign Up\\r\\n\\r\\n * Learning Pat...</td>\n",
       "      <td>Here’s this week’s news in Data Science and Bi...</td>\n",
       "      <td>This Week in Data Science (April 18, 2017)</td>\n",
       "      <td>Live</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DATALAYER: HIGH THROUGHPUT, LOW LATENCY AT SCA...</td>\n",
       "      <td>Learn how distributed DBs solve the problem of...</td>\n",
       "      <td>DataLayer Conference: Boost the performance of...</td>\n",
       "      <td>Live</td>\n",
       "      <td>3</td>\n",
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       "      <th>4</th>\n",
       "      <td>Skip navigation Sign in SearchLoading...\\r\\n\\r...</td>\n",
       "      <td>This video demonstrates the power of IBM DataS...</td>\n",
       "      <td>Analyze NY Restaurant data using Spark in DSX</td>\n",
       "      <td>Live</td>\n",
       "      <td>4</td>\n",
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       "                                            doc_body  \\\n",
       "0  Skip navigation Sign in SearchLoading...\\r\\n\\r...   \n",
       "1  No Free Hunch Navigation * kaggle.com\\r\\n\\r\\n ...   \n",
       "2  ☰ * Login\\r\\n * Sign Up\\r\\n\\r\\n * Learning Pat...   \n",
       "3  DATALAYER: HIGH THROUGHPUT, LOW LATENCY AT SCA...   \n",
       "4  Skip navigation Sign in SearchLoading...\\r\\n\\r...   \n",
       "\n",
       "                                     doc_description  \\\n",
       "0  Detect bad readings in real time using Python ...   \n",
       "1  See the forest, see the trees. Here lies the c...   \n",
       "2  Here’s this week’s news in Data Science and Bi...   \n",
       "3  Learn how distributed DBs solve the problem of...   \n",
       "4  This video demonstrates the power of IBM DataS...   \n",
       "\n",
       "                                       doc_full_name doc_status  article_id  \n",
       "0  Detect Malfunctioning IoT Sensors with Streami...       Live           0  \n",
       "1  Communicating data science: A guide to present...       Live           1  \n",
       "2         This Week in Data Science (April 18, 2017)       Live           2  \n",
       "3  DataLayer Conference: Boost the performance of...       Live           3  \n",
       "4      Analyze NY Restaurant data using Spark in DSX       Live           4  "
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   "source": [
    "# Show df_content to get an idea of the data\n",
    "df_content.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((45993, 3), (1056, 5))"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check number of rows and columns\n",
    "df.shape, df_content.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "scrolled": true
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   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45993 entries, 0 to 45992\n",
      "Data columns (total 3 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   article_id  45993 non-null  float64\n",
      " 1   title       45993 non-null  object \n",
      " 2   email       45976 non-null  object \n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# check the data sets\n",
    "df.info()"
   ]
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   "execution_count": 112,
   "metadata": {
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1056 entries, 0 to 1055\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   doc_body         1042 non-null   object\n",
      " 1   doc_description  1053 non-null   object\n",
      " 2   doc_full_name    1056 non-null   object\n",
      " 3   doc_status       1056 non-null   object\n",
      " 4   article_id       1056 non-null   int64 \n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 41.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# check the data sets\n",
    "df_content.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "article_id     0\n",
       "title          0\n",
       "email         17\n",
       "dtype: int64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# check for missing values\n",
    "df.isnull().sum()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "doc_body           14\n",
       "doc_description     3\n",
       "doc_full_name       0\n",
       "doc_status          0\n",
       "article_id          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for missing values\n",
    "df_content.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "714"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of unique articles\n",
    "df['article_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5148"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of unique users\n",
    "df['email'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a class=\"anchor\" id=\"Exploratory-Data-Analysis\">Part I : Exploratory Data Analysis</a>\n",
    "\n",
    "Use the dictionary and cells below to provide some insight into the descriptive statistics of the data.\n",
    "\n",
    "`1.` What is the distribution of how many articles a user interacts with in the dataset?  Provide a visual and descriptive statistics to assist with giving a look at the number of times each user interacts with an article.  \n",
    "\n",
    "Using the seaborn distplot for vizualizations\n",
    "http://seaborn.pydata.org/generated/seaborn.distplot.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# assigning pivot table to a variable\n",
    "user_interact = df.groupby('email').count()['article_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/freemo/anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2551: 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": [
       "<AxesSubplot:xlabel='article_id', ylabel='Density'>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting with seaborn\n",
    "sns.distplot(user_interact, bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/freemo/anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2551: 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 `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/freemo/anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:1649: FutureWarning: The `vertical` parameter is deprecated and will be removed in a future version. Assign the data to the `y` variable instead.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/freemo/anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2055: FutureWarning: The `axis` variable is no longer used and will be removed. Instead, assign variables directly to `x` or `y`.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/freemo/anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:2064: FutureWarning: Using `vertical=True` to control the orientation of the plot  is deprecated. Instead, assign the data directly to `y`. \n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Density', ylabel='article_id'>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# seaborn rug plot version\n",
    "sns.distplot(user_interact, rug=True, hist=False, vertical=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting with pandas\n",
    "user_interact.hist(bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "364"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the max number of user_article interactions\n",
    "df.groupby('email')['article_id'].count().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the medin number of user_article interactions\n",
    "df.groupby('email')['article_id'].count().median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill in the median and maximum number of user_article interactios below\n",
    "\n",
    "# 50% of individuals interact with ___ number of articles or fewer.\n",
    "median_val = df.groupby('email')['article_id'].count().median() \n",
    "\n",
    "# The maximum number of user-article interactions by any 1 user is ______.\n",
    "max_views_by_user = df.groupby('email')['article_id'].count().max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`2.` Explore and remove duplicate articles from the **df_content** dataframe.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1056, 5)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check number of rows and columns of df_content\n",
    "df_content.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find and explore duplicate articles\n",
    "df_content.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove any rows that have the same article_id - only keep the first\n",
    "df_content.drop_duplicates(inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`3.` Use the cells below to find:\n",
    "\n",
    "**a.** The number of unique articles that have an interaction with a user.  \n",
    "**b.** The number of unique articles in the dataset (whether they have any interactions or not).<br>\n",
    "**c.** The number of unique users in the dataset. (excluding null values) <br>\n",
    "**d.** The number of user-article interactions in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "article_id     714\n",
       "title          714\n",
       "email         5148\n",
       "dtype: int64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking df for uniuqe values\n",
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "doc_body           1036\n",
       "doc_description    1022\n",
       "doc_full_name      1051\n",
       "doc_status            1\n",
       "article_id         1051\n",
       "dtype: int64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking df_content for unique values\n",
    "df_content.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "714"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The number of unique articles that have at least one interaction\n",
    "df.article_id.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1051"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The number of unique articles on the IBM platform\n",
    "df_content.article_id.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5148"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The number of unique users (email column refers to users)\n",
    "df.email.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45993"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The number of user-article interactions = the number of rows in df\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The number of unique articles that have at least one interaction\n",
    "unique_articles = df.article_id.nunique()\n",
    "\n",
    "# The number of unique articles on the IBM platform\n",
    "total_articles = df_content.article_id.nunique()\n",
    "\n",
    "# The number of unique users\n",
    "unique_users = df.email.nunique()\n",
    "\n",
    "# The number of user-article interactions\n",
    "user_article_interactions = len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`4.` Use the cells below to find the most viewed **article_id**, as well as how often it was viewed.  After talking to the company leaders, the `email_mapper` function was deemed a reasonable way to map users to ids.  There were a small number of null values, and it was found that all of these null values likely belonged to a single user (which is how they are stored using the function below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1429.0    937\n",
       "1330.0    927\n",
       "1431.0    671\n",
       "1427.0    643\n",
       "1364.0    627\n",
       "         ... \n",
       "1119.0      1\n",
       "417.0       1\n",
       "724.0       1\n",
       "653.0       1\n",
       "1127.0      1\n",
       "Name: article_id, Length: 714, dtype: int64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for the most viewed article_id\n",
    "df['article_id'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "most_viewed_article_id = '1429.0' # The most viewed article in the dataset as a string with one value following the decimal \n",
    "max_views = 937 # The most viewed article in the dataset was viewed how many times?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>article_id</th>\n",
       "      <th>title</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1430.0</td>\n",
       "      <td>using pixiedust for fast, flexible, and easier...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1314.0</td>\n",
       "      <td>healthcare python streaming application demo</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1429.0</td>\n",
       "      <td>use deep learning for image classification</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1338.0</td>\n",
       "      <td>ml optimization using cognitive assistant</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1276.0</td>\n",
       "      <td>deploy your python model as a restful api</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   article_id                                              title  user_id\n",
       "0      1430.0  using pixiedust for fast, flexible, and easier...        1\n",
       "1      1314.0       healthcare python streaming application demo        2\n",
       "2      1429.0         use deep learning for image classification        3\n",
       "3      1338.0          ml optimization using cognitive assistant        4\n",
       "4      1276.0          deploy your python model as a restful api        5"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## No need to change the code here - this will be helpful for later parts of the notebook\n",
    "# Run this cell to map the user email to a user_id column and remove the email column\n",
    "\n",
    "def email_mapper():\n",
    "    coded_dict = dict()\n",
    "    cter = 1\n",
    "    email_encoded = []\n",
    "    \n",
    "    for val in df['email']:\n",
    "        if val not in coded_dict:\n",
    "            coded_dict[val] = cter\n",
    "            cter+=1\n",
    "        \n",
    "        email_encoded.append(coded_dict[val])\n",
    "    return email_encoded\n",
    "\n",
    "email_encoded = email_mapper()\n",
    "del df['email']\n",
    "df['user_id'] = email_encoded\n",
    "\n",
    "# show header\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It looks like you have everything right here! Nice job!\n"
     ]
    }
   ],
   "source": [
    "## If you stored all your results in the variable names above, \n",
    "## you shouldn't need to change anything in this cell\n",
    "\n",
    "sol_1_dict = {\n",
    "    '`50% of individuals have _____ or fewer interactions.`': median_val,\n",
    "    '`The total number of user-article interactions in the dataset is ______.`': user_article_interactions,\n",
    "    '`The maximum number of user-article interactions by any 1 user is ______.`': max_views_by_user,\n",
    "    '`The most viewed article in the dataset was viewed _____ times.`': max_views,\n",
    "    '`The article_id of the most viewed article is ______.`': most_viewed_article_id,\n",
    "    '`The number of unique articles that have at least 1 rating ______.`': unique_articles,\n",
    "    '`The number of unique users in the dataset is ______`': unique_users,\n",
    "    '`The number of unique articles on the IBM platform`': total_articles\n",
    "}\n",
    "\n",
    "# Test your dictionary against the solution\n",
    "t.sol_1_test(sol_1_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a class=\"anchor\" id=\"Rank\">Part II: Rank-Based Recommendations</a>\n",
    "\n",
    "Unlike in the earlier lessons, we don't actually have ratings for whether a user liked an article or not.  We only know that a user has interacted with an article.  In these cases, the popularity of an article can really only be based on how often an article was interacted with.\n",
    "\n",
    "`1.` Fill in the function below to return the **n** top articles ordered with most interactions as the top. Test your function using the tests below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>article_id</th>\n",
       "      <th>title</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1430.0</td>\n",
       "      <td>using pixiedust for fast, flexible, and easier...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1314.0</td>\n",
       "      <td>healthcare python streaming application demo</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1429.0</td>\n",
       "      <td>use deep learning for image classification</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1338.0</td>\n",
       "      <td>ml optimization using cognitive assistant</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1276.0</td>\n",
       "      <td>deploy your python model as a restful api</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   article_id                                              title  user_id\n",
       "0      1430.0  using pixiedust for fast, flexible, and easier...        1\n",
       "1      1314.0       healthcare python streaming application demo        2\n",
       "2      1429.0         use deep learning for image classification        3\n",
       "3      1338.0          ml optimization using cognitive assistant        4\n",
       "4      1276.0          deploy your python model as a restful api        5"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Float64Index([1429.0, 1330.0, 1431.0, 1427.0, 1364.0, 1314.0, 1293.0, 1170.0,\n",
       "              1162.0, 1304.0],\n",
       "             dtype='float64')"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# testing a function\n",
    "df['article_id'].value_counts().iloc[:10].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['use deep learning for image classification',\n",
       " 'insights from new york car accident reports',\n",
       " 'visualize car data with brunel',\n",
       " 'use xgboost, scikit-learn & ibm watson machine learning apis',\n",
       " 'predicting churn with the spss random tree algorithm']"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# testing a function 2\n",
    "df.groupby(by='title').count().sort_values(by='user_id', ascending=False).head().index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1429.0,\n",
       " 1330.0,\n",
       " 1431.0,\n",
       " 1427.0,\n",
       " 1364.0,\n",
       " 1314.0,\n",
       " 1293.0,\n",
       " 1170.0,\n",
       " 1162.0,\n",
       " 1304.0]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# testing a function 3\n",
    "df.groupby(by='article_id').count().sort_values(by='user_id', ascending=False).head(10).index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1429.0,\n",
       " 1330.0,\n",
       " 1431.0,\n",
       " 1427.0,\n",
       " 1364.0,\n",
       " 1314.0,\n",
       " 1293.0,\n",
       " 1170.0,\n",
       " 1162.0,\n",
       " 1304.0]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# testing a funciton 4\n",
    "list(df.groupby(by='article_id').count().sort_values(by='user_id', ascending=False).head(10).index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_top_articles(n, df=df):\n",
    "    '''\n",
    "    INPUT:\n",
    "    n - (int) the number of top articles to return\n",
    "    df - (pandas dataframe) df as defined at the top of the notebook \n",
    "    \n",
    "    OUTPUT:\n",
    "    top_articles - (list) A list of the top 'n' article titles \n",
    "    \n",
    "    '''\n",
    "    # creating a list with top articles using 'title'\n",
    "    top_articles = df['title'].value_counts().iloc[:n].index.tolist()\n",
    "    \n",
    "    return top_articles # Return the top article titles from df (not df_content)\n",
    "\n",
    "def get_top_article_ids(n, df=df):\n",
    "    '''\n",
    "    INPUT:\n",
    "    n - (int) the number of top articles to return\n",
    "    df - (pandas dataframe) df as defined at the top of the notebook \n",
    "    \n",
    "    OUTPUT:\n",
    "    top_articles - (list) A list of the top 'n' article titles \n",
    "    \n",
    "    '''\n",
    "    # creating a list with top article ids\n",
    "    top_articles_ids = df['article_id'].value_counts().iloc[:n].index.tolist()\n",
    " \n",
    "    return top_articles_ids # Return the top article ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['use deep learning for image classification', 'insights from new york car accident reports', 'visualize car data with brunel', 'use xgboost, scikit-learn & ibm watson machine learning apis', 'predicting churn with the spss random tree algorithm', 'healthcare python streaming application demo', 'finding optimal locations of new store using decision optimization', 'apache spark lab, part 1: basic concepts', 'analyze energy consumption in buildings', 'gosales transactions for logistic regression model']\n",
      "[1429.0, 1330.0, 1431.0, 1427.0, 1364.0, 1314.0, 1293.0, 1170.0, 1162.0, 1304.0]\n"
     ]
    }
   ],
   "source": [
    "print(get_top_articles(10))\n",
    "print(get_top_article_ids(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['use deep learning for image classification', 'insights from new york car accident reports', 'visualize car data with brunel', 'use xgboost, scikit-learn & ibm watson machine learning apis', 'predicting churn with the spss random tree algorithm', 'healthcare python streaming application demo', 'finding optimal locations of new store using decision optimization', 'apache spark lab, part 1: basic concepts', 'analyze energy consumption in buildings', 'gosales transactions for logistic regression model', 'welcome to pixiedust', 'customer demographics and sales', 'total population by country', 'deep learning with tensorflow course by big data university', 'model bike sharing data with spss', 'the nurse assignment problem', 'classify tumors with machine learning', 'analyze accident reports on amazon emr spark', 'movie recommender system with spark machine learning', 'putting a human face on machine learning']\n",
      "[1429.0, 1330.0, 1431.0, 1427.0, 1364.0, 1314.0, 1293.0, 1170.0, 1162.0, 1304.0, 1436.0, 1271.0, 1398.0, 43.0, 1351.0, 1393.0, 1185.0, 1160.0, 1354.0, 1368.0]\n"
     ]
    }
   ],
   "source": [
    "print(get_top_articles(20))\n",
    "print(get_top_article_ids(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your top_5 looks like the solution list! Nice job.\n",
      "Your top_10 looks like the solution list! Nice job.\n",
      "Your top_20 looks like the solution list! Nice job.\n"
     ]
    }
   ],
   "source": [
    "# Test your function by returning the top 5, 10, and 20 articles\n",
    "top_5 = get_top_articles(5)\n",
    "top_10 = get_top_articles(10)\n",
    "top_20 = get_top_articles(20)\n",
    "\n",
    "# Test each of your three lists from above\n",
    "t.sol_2_test(get_top_articles)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a class=\"anchor\" id=\"User-User\">Part III: User-User Based Collaborative Filtering</a>\n",
    "\n",
    "\n",
    "`1.` Use the function below to reformat the **df** dataframe to be shaped with users as the rows and articles as the columns.  \n",
    "\n",
    "* Each **user** should only appear in each **row** once.\n",
    "\n",
    "\n",
    "* Each **article** should only show up in one **column**.  \n",
    "\n",
    "\n",
    "* **If a user has interacted with an article, then place a 1 where the user-row meets for that article-column**.  It does not matter how many times a user has interacted with the article, all entries where a user has interacted with an article should be a 1.  \n",
    "\n",
    "\n",
    "* **If a user has not interacted with an item, then place a zero where the user-row meets for that article-column**. \n",
    "\n",
    "Use the tests to make sure the basic structure of your matrix matches what is expected by the solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>article_id</th>\n",
       "      <th>0.0</th>\n",
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       "      <th>1442.0</th>\n",
       "      <th>1443.0</th>\n",
       "      <th>1444.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <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>5145</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5146</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5147</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5148</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5149</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5149 rows × 714 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            title                                                          \\\n",
       "article_id 0.0    2.0    4.0    8.0    9.0    12.0   14.0   15.0   16.0     \n",
       "user_id                                                                     \n",
       "1             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "2             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "3             0.0    0.0    0.0    0.0    0.0    1.0    0.0    0.0    0.0   \n",
       "4             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "5             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "...           ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "5145          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "5146          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "5147          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "5148          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "5149          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    1.0   \n",
       "\n",
       "                   ...                                                   \\\n",
       "article_id 18.0    ... 1434.0 1435.0 1436.0 1437.0 1439.0 1440.0 1441.0   \n",
       "user_id            ...                                                    \n",
       "1             0.0  ...    0.0    0.0    1.0    0.0    1.0    0.0    0.0   \n",
       "2             0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "3             0.0  ...    0.0    0.0    1.0    0.0    0.0    0.0    0.0   \n",
       "4             0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "5             0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "...           ...  ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "5145          0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "5146          0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "5147          0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "5148          0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "5149          0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "                                 \n",
       "article_id 1442.0 1443.0 1444.0  \n",
       "user_id                          \n",
       "1             0.0    0.0    0.0  \n",
       "2             0.0    0.0    0.0  \n",
       "3             0.0    0.0    0.0  \n",
       "4             0.0    0.0    0.0  \n",
       "5             0.0    0.0    0.0  \n",
       "...           ...    ...    ...  \n",
       "5145          0.0    0.0    0.0  \n",
       "5146          0.0    0.0    0.0  \n",
       "5147          0.0    0.0    0.0  \n",
       "5148          0.0    0.0    0.0  \n",
       "5149          0.0    0.0    0.0  \n",
       "\n",
       "[5149 rows x 714 columns]"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['user_id', 'article_id']).agg(lambda x: 1).unstack().fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the user-article matrix with 1's and 0's\n",
    "\n",
    "def create_user_item_matrix(df):\n",
    "    '''\n",
    "    INPUT:\n",
    "    df - pandas dataframe with article_id, title, user_id columns\n",
    "    \n",
    "    OUTPUT:\n",
    "    user_item - user item matrix \n",
    "    \n",
    "    Description:\n",
    "    Return a matrix with user ids as rows and article ids on the columns with 1 values where a user interacted with \n",
    "    an article and a 0 otherwise\n",
    "    '''\n",
    "    # Using groupby and lambda function to create the matrix\n",
    "    user_item = df.groupby(['user_id', 'article_id']).agg(lambda x: 1).unstack().fillna(0)\n",
    "    \n",
    "    return user_item # return the user_item matrix \n",
    "\n",
    "user_item = create_user_item_matrix(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You have passed our quick tests!  Please proceed!\n"
     ]
    }
   ],
   "source": [
    "## Tests: You should just need to run this cell.  Don't change the code.\n",
    "assert user_item.shape[0] == 5149, \"Oops!  The number of users in the user-article matrix doesn't look right.\"\n",
    "assert user_item.shape[1] == 714, \"Oops!  The number of articles in the user-article matrix doesn't look right.\"\n",
    "assert user_item.sum(axis=1)[1] == 36, \"Oops!  The number of articles seen by user 1 doesn't look right.\"\n",
    "print(\"You have passed our quick tests!  Please proceed!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`2.` Complete the function below which should take a user_id and provide an ordered list of the most similar users to that user (from most similar to least similar).  The returned result should not contain the provided user_id, as we know that each user is similar to him/herself. Because the results for each user here are binary, it (perhaps) makes sense to compute similarity as the dot product of two users. \n",
    "\n",
    "Use the tests to test your function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_similar_users(user_id, user_item=user_item):\n",
    "    '''\n",
    "    INPUT:\n",
    "    user_id - (int) a user_id\n",
    "    user_item - (pandas dataframe) matrix of users by articles: \n",
    "                1's when a user has interacted with an article, 0 otherwise\n",
    "    \n",
    "    OUTPUT:\n",
    "    similar_users - (list) an ordered list where the closest users (largest dot product users)\n",
    "                    are listed first\n",
    "    \n",
    "    Description:\n",
    "    Computes the similarity of every pair of users based on the dot product\n",
    "    Returns an ordered list\n",
    "    \n",
    "    '''\n",
    "    # compute similarity of each user to the provided user\n",
    "    similar_mat = user_item.dot(user_item.loc[user_id].T)\n",
    "    \n",
    "    # sort by similarity\n",
    "    most_similar_users = similar_mat.sort_values(ascending=False).index.tolist()\n",
    "\n",
    "    most_similar_users.remove(user_id)\n",
    "\n",
    "    return most_similar_users # return a list of the users in order from most to least similar "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 10 most similar users to user 1 are: [3933, 23, 3782, 203, 4459, 131, 3870, 46, 4201, 49]\n",
      "The 5 most similar users to user 3933 are: [1, 23, 3782, 203, 4459]\n",
      "The 3 most similar users to user 46 are: [4201, 23, 3782]\n"
     ]
    }
   ],
   "source": [
    "# Do a spot check of your function\n",
    "print(\"The 10 most similar users to user 1 are: {}\".format(find_similar_users(1)[:10]))\n",
    "print(\"The 5 most similar users to user 3933 are: {}\".format(find_similar_users(3933)[:5]))\n",
    "print(\"The 3 most similar users to user 46 are: {}\".format(find_similar_users(46)[:3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`3.` Now that you have a function that provides the most similar users to each user, you will want to use these users to find articles you can recommend.  Complete the functions below to return the articles you would recommend to each user. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_article_names(article_ids, df=df):\n",
    "    '''\n",
    "    INPUT:\n",
    "    article_ids - (list) a list of article ids\n",
    "    df - (pandas dataframe) df as defined at the top of the notebook\n",
    "    \n",
    "    OUTPUT:\n",
    "    article_names - (list) a list of article names associated with the list of article ids \n",
    "                    (this is identified by the title column)\n",
    "    '''\n",
    "    # create article names \n",
    "    article_names = [df[df['article_id'] == float(id)]['title'].values[0] for id in article_ids]\n",
    "    \n",
    "    return article_names # Return the article names associated with list of article ids\n",
    "\n",
    "\n",
    "def get_user_articles(user_id, user_item=user_item):\n",
    "    '''\n",
    "    INPUT:\n",
    "    user_id - (int) a user id\n",
    "    user_item - (pandas dataframe) matrix of users by articles: \n",
    "                1's when a user has interacted with an article, 0 otherwise\n",
    "    \n",
    "    OUTPUT:\n",
    "    article_ids - (list) a list of the article ids seen by the user\n",
    "    article_names - (list) a list of article names associated with the list of article ids \n",
    "                    (this is identified by the doc_full_name column in df_content)\n",
    "    \n",
    "    Description:\n",
    "    Provides a list of the article_ids and article titles that have been seen by a user\n",
    "    '''\n",
    "    # Your code here\n",
    "    article_ids = [str(id) for id in list(user_item.loc[user_id][user_item.loc[user_id]==1].title.index)]\n",
    "    \n",
    "    article_names = get_article_names(article_ids)\n",
    "    \n",
    "    return article_ids, article_names # return the ids and names\n",
    "\n",
    "\n",
    "def user_user_recs(user_id, m=10):\n",
    "    '''\n",
    "    INPUT:\n",
    "    user_id - (int) a user id\n",
    "    m - (int) the number of recommendations you want for the user\n",
    "    \n",
    "    OUTPUT:\n",
    "    recs - (list) a list of recommendations for the user\n",
    "    \n",
    "    Description:\n",
    "    Loops through the users based on closeness to the input user_id\n",
    "    For each user - finds articles the user hasn't seen before and provides them as recs\n",
    "    Does this until m recommendations are found\n",
    "    \n",
    "    Notes:\n",
    "    Users who are the same closeness are chosen arbitrarily as the 'next' user\n",
    "    \n",
    "    For the user where the number of recommended articles starts below m \n",
    "    and ends exceeding m, the last items are chosen arbitrarily\n",
    "    \n",
    "    '''\n",
    "    # create user-user recommendation\n",
    "    recs = []\n",
    "    most_similar_users = find_similar_users(user_id)\n",
    "    the_user_articles, the_article_names = get_user_articles(user_id)\n",
    "    for user in most_similar_users:\n",
    "        article_ids, article_names = get_user_articles(user)\n",
    "        for id in article_ids:\n",
    "            if id not in the_user_articles:\n",
    "                recs.append(id)\n",
    "            if len(recs) >= m:\n",
    "                break\n",
    "        if len(recs) >= m:\n",
    "                break\n",
    "    \n",
    "    if len(recs) < m:\n",
    "        for id in str(df['article_id']):\n",
    "            if id not in the_user_articles:\n",
    "                recs.append(id)\n",
    "            if len(recs) >= m:\n",
    "                    break\n",
    "    \n",
    "    return recs # return your recommendations for this user_id    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['this week in data science (april 18, 2017)',\n",
       " 'timeseries data analysis of iot events by using jupyter notebook',\n",
       " 'got zip code data? prep it for analytics. – ibm watson data lab – medium',\n",
       " 'higher-order logistic regression for large datasets',\n",
       " 'using machine learning to predict parking difficulty',\n",
       " 'deep forest: towards an alternative to deep neural networks',\n",
       " 'experience iot with coursera',\n",
       " 'using brunel in ipython/jupyter notebooks',\n",
       " 'graph-based machine learning',\n",
       " 'the 3 kinds of context: machine learning and the art of the frame']"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check Results\n",
    "get_article_names(user_user_recs(1, 10)) # Return 10 recommendations for user 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`4.` Now we are going to improve the consistency of the **user_user_recs** function from above.  \n",
    "\n",
    "* Instead of arbitrarily choosing when we obtain users who are all the same closeness to a given user - choose the users that have the most total article interactions before choosing those with fewer article interactions.\n",
    "\n",
    "\n",
    "* Instead of arbitrarily choosing articles from the user where the number of recommended articles starts below m and ends exceeding m, choose articles with the articles with the most total interactions before choosing those with fewer total interactions. This ranking should be  what would be obtained from the **top_articles** function you wrote earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_top_sorted_users(user_id, df=df, user_item=user_item):\n",
    "    '''\n",
    "    INPUT:\n",
    "    user_id - (int)\n",
    "    df - (pandas dataframe) df as defined at the top of the notebook \n",
    "    user_item - (pandas dataframe) matrix of users by articles: \n",
    "            1's when a user has interacted with an article, 0 otherwise\n",
    "    \n",
    "            \n",
    "    OUTPUT:\n",
    "    neighbors_df - (pandas dataframe) a dataframe with:\n",
    "                    neighbor_id - is a neighbor user_id\n",
    "                    similarity - measure of the similarity of each user to the provided user_id\n",
    "                    num_interactions - the number of articles viewed by the user - if a u\n",
    "                    \n",
    "    Other Details - sort the neighbors_df by the similarity and then by number of interactions where \n",
    "                    highest of each is higher in the dataframe\n",
    "     \n",
    "    '''\n",
    "    # creating a variable based on panda dataframe of the columns we need\n",
    "    neighbors_df = pd.DataFrame(columns=['neighbor_id', 'similarity', 'num_interactions'])\n",
    "    \n",
    "    for i in user_item.index.values:\n",
    "        if i == user_id:\n",
    "            continue\n",
    "        neighbor_id = i\n",
    "        similarity = user_item[user_item.index == user_id].dot(user_item.loc[i].T).values[0]\n",
    "        num_interactions = user_item.loc[i].values.sum()\n",
    "        neighbors_df.loc[neighbor_id] = [neighbor_id, similarity, num_interactions]\n",
    "        \n",
    "    neighbors_df['similarity'] = neighbors_df['similarity'].astype('int')\n",
    "    neighbors_df['neighbor_id'] = neighbors_df['neighbor_id'].astype('int')\n",
    "    neighbors_df = neighbors_df.sort_values(by = ['similarity', 'neighbor_id'], ascending = [False, True])\n",
    "    \n",
    "    return neighbors_df # Return the dataframe specified in the doc_string\n",
    "\n",
    "def user_user_recs_part2(user_id, m=10):\n",
    "    '''\n",
    "    INPUT:\n",
    "    user_id - (int) a user id\n",
    "    m - (int) the number of recommendations you want for the user\n",
    "    \n",
    "    OUTPUT:\n",
    "    recs - (list) a list of recommendations for the user by article id\n",
    "    rec_names - (list) a list of recommendations for the user by article title\n",
    "    \n",
    "    Description:\n",
    "    Loops through the users based on closeness to the input user_id\n",
    "    For each user - finds articles the user hasn't seen before and provides them as recs\n",
    "    Does this until m recommendations are found\n",
    "    \n",
    "    Notes:\n",
    "    * Choose the users that have the most total article interactions \n",
    "    before choosing those with fewer article interactions.\n",
    "\n",
    "    * Choose articles with the articles with the most total interactions \n",
    "    before choosing those with fewer total interactions. \n",
    "   \n",
    "    '''\n",
    "    # create a list with recommendations based on total article interactions\n",
    "     \n",
    "    top_df = get_top_sorted_users(user_id)\n",
    "    uid_list = top_df['neighbor_id'].values.tolist()\n",
    "    recs = []\n",
    "    name_ids = []\n",
    "\n",
    "    exp_article_ids = list(set(df[df['user_id'] == user_id]['article_id'].values.tolist()))\n",
    "\n",
    "    for uid in uid_list:\n",
    "        recs += df[df['user_id'] == uid]['article_id'].values.tolist()\n",
    "\n",
    "    recs = list(set(recs))\n",
    "    recs = [ x for x in recs if x not in exp_article_ids ]\n",
    "\n",
    "    rec_all = df[df.article_id.isin(recs)][['article_id','title']].drop_duplicates().head(m)\n",
    "    recs = rec_all['article_id'].values.tolist()\n",
    "    rec_names = rec_all['title'].values.tolist()\n",
    "    \n",
    "    return recs, rec_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The top 10 recommendations for user 20 are the following article ids:\n",
      "[1430.0, 1314.0, 1429.0, 1338.0, 1276.0, 1432.0, 593.0, 1185.0, 993.0, 14.0]\n",
      "\n",
      "The top 10 recommendations for user 20 are the following article names:\n",
      "['using pixiedust for fast, flexible, and easier data analysis and experimentation', 'healthcare python streaming application demo', 'use deep learning for image classification', 'ml optimization using cognitive assistant', 'deploy your python model as a restful api', 'visualize data with the matplotlib library', 'upload files to ibm data science experience using the command line', 'classify tumors with machine learning', 'configuring the apache spark sql context', 'got zip code data? prep it for analytics. – ibm watson data lab – medium']\n"
     ]
    }
   ],
   "source": [
    "# Quick spot check - don't change this code - just use it to test your functions\n",
    "rec_ids, rec_names = user_user_recs_part2(20, 10)\n",
    "print(\"The top 10 recommendations for user 20 are the following article ids:\")\n",
    "print(rec_ids)\n",
    "print()\n",
    "print(\"The top 10 recommendations for user 20 are the following article names:\")\n",
    "print(rec_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`5.` Use your functions from above to correctly fill in the solutions to the dictionary below.  Then test your dictionary against the solution.  Provide the code you need to answer each following the comments below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3933"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the user that is most similar to user 1 \n",
    "find_similar_users(1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "242"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the 10th most similar user to user 131\n",
    "find_similar_users(131)[10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "scrolled": true
   },
   "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",
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       "    }\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>neighbor_id</th>\n",
       "      <th>similarity</th>\n",
       "      <th>num_interactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3933</th>\n",
       "      <td>3933</td>\n",
       "      <td>35</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>17</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3782</th>\n",
       "      <td>3782</td>\n",
       "      <td>17</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>203</td>\n",
       "      <td>15</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4459</th>\n",
       "      <td>4459</td>\n",
       "      <td>15</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      neighbor_id  similarity  num_interactions\n",
       "3933         3933          35              35.0\n",
       "23             23          17             135.0\n",
       "3782         3782          17             135.0\n",
       "203           203          15              96.0\n",
       "4459         4459          15              96.0"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_top_sorted_users(1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>neighbor_id</th>\n",
       "      <th>similarity</th>\n",
       "      <th>num_interactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3870</th>\n",
       "      <td>3870</td>\n",
       "      <td>74</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3782</th>\n",
       "      <td>3782</td>\n",
       "      <td>39</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>38</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>203</td>\n",
       "      <td>33</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4459</th>\n",
       "      <td>4459</td>\n",
       "      <td>33</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>49</td>\n",
       "      <td>29</td>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>98</td>\n",
       "      <td>29</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3697</th>\n",
       "      <td>3697</td>\n",
       "      <td>29</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3764</th>\n",
       "      <td>3764</td>\n",
       "      <td>29</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>242</td>\n",
       "      <td>25</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      neighbor_id  similarity  num_interactions\n",
       "3870         3870          74              75.0\n",
       "3782         3782          39             135.0\n",
       "23             23          38             135.0\n",
       "203           203          33              96.0\n",
       "4459         4459          33              96.0\n",
       "49             49          29             101.0\n",
       "98             98          29              97.0\n",
       "3697         3697          29             100.0\n",
       "3764         3764          29              97.0\n",
       "242           242          25              59.0"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_top_sorted_users(131).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Tests with a dictionary of results\n",
    "\n",
    "# Find the user that is most similar to user 1 \n",
    "user1_most_sim = find_similar_users(1)[0] \n",
    "\n",
    "# Find the 10th most similar user to user 131\n",
    "user131_10th_sim = find_similar_users(131)[10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This all looks good!  Nice job!\n"
     ]
    }
   ],
   "source": [
    "## Dictionary Test Here\n",
    "sol_5_dict = {\n",
    "    'The user that is most similar to user 1.': user1_most_sim, \n",
    "    'The user that is the 10th most similar to user 131': user131_10th_sim,\n",
    "}\n",
    "\n",
    "t.sol_5_test(sol_5_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`6.` If we were given a new user, which of the above functions would you be able to use to make recommendations?  Explain.  Can you think of a better way we might make recommendations?  Use the cell below to explain a better method for new users."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Provide your response here.**\n",
    "\n",
    "RESPONSE:\n",
    "\n",
    "The reccomendations for new users is always challenging. New users have no previous records thus it's impossible to use user collaborative filtering based recommendations methods. The solution would be to use a content based recommendations. While in collaborative filtering we used the connections of users and items, in content based method, we would use the information about the users and items, but not the connections between them. **Rank based filtering would help here with new users: get_top_articles and get_top_articles_ids are the fucntions that we can use.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`7.` Using your existing functions, provide the top 10 recommended articles you would provide for the a new user below.  You can test your function against our thoughts to make sure we are all on the same page with how we might make a recommendation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_user = '0.0'\n",
    "\n",
    "# What would your recommendations be for this new user '0.0'?  As a new user, they have no observed articles.\n",
    "# Provide a list of the top 10 article ids you would give to \n",
    "new_user = get_top_article_ids(10)\n",
    "new_user_recs = map(str, new_user)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "That's right!  Nice job!\n"
     ]
    }
   ],
   "source": [
    "assert set(new_user_recs) == set(['1314.0','1429.0','1293.0','1427.0','1162.0','1364.0','1304.0','1170.0','1431.0','1330.0']), \"Oops!  It makes sense that in this case we would want to recommend the most popular articles, because we don't know anything about these users.\"\n",
    "\n",
    "print(\"That's right!  Nice job!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a class=\"anchor\" id=\"Content-Recs\">Part IV: Content Based Recommendations (EXTRA - NOT REQUIRED)</a>\n",
    "\n",
    "Another method we might use to make recommendations is to perform a ranking of the highest ranked articles associated with some term.  You might consider content to be the **doc_body**, **doc_description**, or **doc_full_name**.  There isn't one way to create a content based recommendation, especially considering that each of these columns hold content related information.  \n",
    "\n",
    "`1.` Use the function body below to create a content based recommender.  Since there isn't one right answer for this recommendation tactic, no test functions are provided.  Feel free to change the function inputs if you decide you want to try a method that requires more input values.  The input values are currently set with one idea in mind that you may use to make content based recommendations.  One additional idea is that you might want to choose the most popular recommendations that meet your 'content criteria', but again, there is a lot of flexibility in how you might make these recommendations.\n",
    "\n",
    "### This part is NOT REQUIRED to pass this project.  However, you may choose to take this on as an extra way to show off your skills."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def make_content_recs():\n",
    "    '''\n",
    "    INPUT:\n",
    "    \n",
    "    OUTPUT:\n",
    "    \n",
    "    '''\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`2.` Now that you have put together your content-based recommendation system, use the cell below to write a summary explaining how your content based recommender works.  Do you see any possible improvements that could be made to your function?  Is there anything novel about your content based recommender?\n",
    "\n",
    "### This part is NOT REQUIRED to pass this project.  However, you may choose to take this on as an extra way to show off your skills.\n",
    "\n",
    "https://view3f484599.udacity-student-workspaces.com/notebooks/Content%20Based%20Recommendations%20-%20Solution.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Write an explanation of your content based recommendation system here.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`3.` Use your content-recommendation system to make recommendations for the below scenarios based on the comments.  Again no tests are provided here, because there isn't one right answer that could be used to find these content based recommendations.\n",
    "\n",
    "### This part is NOT REQUIRED to pass this project.  However, you may choose to take this on as an extra way to show off your skills."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# make recommendations for a brand new user\n",
    "\n",
    "\n",
    "# make a recommendations for a user who only has interacted with article id '1427.0'\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a class=\"anchor\" id=\"Matrix-Fact\">Part V: Matrix Factorization</a>\n",
    "\n",
    "In this part of the notebook, you will build user matrix factorization to make article recommendations to the users on the IBM Watson Studio platform.\n",
    "\n",
    "`1.` You should have already created a **user_item** matrix above in **question 1** of **Part III** above.  This first question here will just require that you run the cells to get things set up for the rest of **Part V** of the notebook. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the matrix here\n",
    "user_item_matrix = pd.read_pickle('user_item_matrix.p')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>article_id</th>\n",
       "      <th>0.0</th>\n",
       "      <th>100.0</th>\n",
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       "      <th>1004.0</th>\n",
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       "      <th>1014.0</th>\n",
       "      <th>1015.0</th>\n",
       "      <th>1016.0</th>\n",
       "      <th>...</th>\n",
       "      <th>977.0</th>\n",
       "      <th>98.0</th>\n",
       "      <th>981.0</th>\n",
       "      <th>984.0</th>\n",
       "      <th>985.0</th>\n",
       "      <th>986.0</th>\n",
       "      <th>990.0</th>\n",
       "      <th>993.0</th>\n",
       "      <th>996.0</th>\n",
       "      <th>997.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 714 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "article_id  0.0  100.0  1000.0  1004.0  1006.0  1008.0  101.0  1014.0  1015.0  \\\n",
       "user_id                                                                         \n",
       "1           0.0    0.0     0.0     0.0     0.0     0.0    0.0     0.0     0.0   \n",
       "2           0.0    0.0     0.0     0.0     0.0     0.0    0.0     0.0     0.0   \n",
       "3           0.0    0.0     0.0     0.0     0.0     0.0    0.0     0.0     0.0   \n",
       "4           0.0    0.0     0.0     0.0     0.0     0.0    0.0     0.0     0.0   \n",
       "5           0.0    0.0     0.0     0.0     0.0     0.0    0.0     0.0     0.0   \n",
       "\n",
       "article_id  1016.0  ...  977.0  98.0  981.0  984.0  985.0  986.0  990.0  \\\n",
       "user_id             ...                                                   \n",
       "1              0.0  ...    0.0   0.0    1.0    0.0    0.0    0.0    0.0   \n",
       "2              0.0  ...    0.0   0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "3              0.0  ...    1.0   0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "4              0.0  ...    0.0   0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "5              0.0  ...    0.0   0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "article_id  993.0  996.0  997.0  \n",
       "user_id                          \n",
       "1             0.0    0.0    0.0  \n",
       "2             0.0    0.0    0.0  \n",
       "3             0.0    0.0    0.0  \n",
       "4             0.0    0.0    0.0  \n",
       "5             0.0    0.0    0.0  \n",
       "\n",
       "[5 rows x 714 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# quick look at the matrix\n",
    "user_item_matrix.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`2.` In this situation, you can use Singular Value Decomposition from [numpy](https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.linalg.svd.html) on the user-item matrix.  Use the cell to perform SVD, and explain why this is different than in the lesson."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform SVD on the User-Item Matrix Here\n",
    "\n",
    "u, s, vt = np.linalg.svd(user_item_matrix) # use the built in to get the three matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((714,), (5149, 5149), (714, 714))"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.shape, u.shape,  vt.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Provide your response here.**\n",
    "\n",
    "RESPONSE: \n",
    "\n",
    "Latent features in the lesson were just 4, however here we're talking about 714 latent features. So, the scale is very different comparing to the lesson. Plus, in the lessons there were null values in the data so FunkSVD was used, while here there were no null values.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`3.` Now for the tricky part, how do we choose the number of latent features to use?  Running the below cell, you can see that as the number of latent features increases, we obtain a lower error rate on making predictions for the 1 and 0 values in the user-item matrix.  Run the cell below to get an idea of how the accuracy improves as we increase the number of latent features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/freemo/anaconda3/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Number of Latent Features'),\n",
       " Text(0, 0.5, 'Accuracy'),\n",
       " Text(0.5, 1.0, 'Accuracy vs. Number of Latent Features')]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_latent_feats = np.arange(10,700+10,20)\n",
    "sum_errs = []\n",
    "\n",
    "for k in num_latent_feats:\n",
    "    # restructure with k latent features\n",
    "    s_new, u_new, vt_new = np.diag(s[:k]), u[:, :k], vt[:k, :]\n",
    "    \n",
    "    # take dot product\n",
    "    user_item_est = np.around(np.dot(np.dot(u_new, s_new), vt_new))\n",
    "    \n",
    "    # compute error for each prediction to actual value\n",
    "    diffs = np.subtract(user_item_matrix, user_item_est)\n",
    "    \n",
    "    # total errors and keep track of them\n",
    "    err = np.sum(np.sum(np.abs(diffs)))\n",
    "    sum_errs.append(err)\n",
    "    \n",
    "# plot results with seaborn\n",
    "graph = sns.lineplot(num_latent_feats, 1 - np.array(sum_errs)/df.shape[0])\n",
    "graph.set(xlabel ='Number of Latent Features',\n",
    "         ylabel = 'Accuracy', \n",
    "         title ='Accuracy vs. Number of Latent Features')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`4.` From the above, we can't really be sure how many features to use, because simply having a better way to predict the 1's and 0's of the matrix doesn't exactly give us an indication of if we are able to make good recommendations.  Instead, we might split our dataset into a training and test set of data, as shown in the cell below.  \n",
    "\n",
    "Use the code from question 3 to understand the impact on accuracy of the training and test sets of data with different numbers of latent features. Using the split below: \n",
    "\n",
    "* How many users can we make predictions for in the test set?  \n",
    "* How many users are we not able to make predictions for because of the cold start problem?\n",
    "* How many articles can we make predictions for in the test set?  \n",
    "* How many articles are we not able to make predictions for because of the cold start problem?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df.head(40000)\n",
    "df_test = df.tail(5993)\n",
    "\n",
    "def create_test_and_train_user_item(df_train, df_test):\n",
    "    '''\n",
    "    INPUT:\n",
    "    df_train - training dataframe\n",
    "    df_test - test dataframe\n",
    "    \n",
    "    OUTPUT:\n",
    "    user_item_train - a user-item matrix of the training dataframe \n",
    "                      (unique users for each row and unique articles for each column)\n",
    "    user_item_test - a user-item matrix of the testing dataframe \n",
    "                    (unique users for each row and unique articles for each column)\n",
    "    test_idx - all of the test user ids\n",
    "    test_arts - all of the test article ids\n",
    "    \n",
    "    '''\n",
    "    # create matrix\n",
    "    user_item_train = create_user_item_matrix(df_train)\n",
    "    user_item_test = create_user_item_matrix(df_test)\n",
    "    test_idx = list(user_item_test.index.values)\n",
    "    test_arts = user_item_test.title.columns.values\n",
    "    \n",
    "    return user_item_train, user_item_test, test_idx, test_arts\n",
    "\n",
    "user_item_train, user_item_test, test_idx, test_arts = create_test_and_train_user_item(df_train, df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 714 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            title                                                          \\\n",
       "article_id 0.0    2.0    4.0    8.0    9.0    12.0   14.0   15.0   16.0     \n",
       "user_id                                                                     \n",
       "1             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "2             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "3             0.0    0.0    0.0    0.0    0.0    1.0    0.0    0.0    0.0   \n",
       "4             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "5             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "                   ...                                                   \\\n",
       "article_id 18.0    ... 1434.0 1435.0 1436.0 1437.0 1439.0 1440.0 1441.0   \n",
       "user_id            ...                                                    \n",
       "1             0.0  ...    0.0    0.0    1.0    0.0    1.0    0.0    0.0   \n",
       "2             0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "3             0.0  ...    0.0    0.0    1.0    0.0    0.0    0.0    0.0   \n",
       "4             0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "5             0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "                                 \n",
       "article_id 1442.0 1443.0 1444.0  \n",
       "user_id                          \n",
       "1             0.0    0.0    0.0  \n",
       "2             0.0    0.0    0.0  \n",
       "3             0.0    0.0    0.0  \n",
       "4             0.0    0.0    0.0  \n",
       "5             0.0    0.0    0.0  \n",
       "\n",
       "[5 rows x 714 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_item_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4487, 714)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_item_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'How many users can we make predictions for in the test set\n",
    "users = user_item_train.index.isin(test_idx)\n",
    "users.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "682"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many users in the test set are we not able to make predictions for because of the cold start problem?\n",
    "len(test_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "574"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many movies can we make predictions for in the test set?'\n",
    "articles = user_item_train.title.columns.isin(test_arts)\n",
    "articles.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Awesome job!  That's right!  All of the test movies are in the training data, but there are only 20 test users that were also in the training set.  All of the other users that are in the test set we have no data on.  Therefore, we cannot make predictions for these users using SVD.\n"
     ]
    }
   ],
   "source": [
    "# Replace the values in the dictionary below\n",
    "a = 662 \n",
    "b = 574 \n",
    "c = 20 \n",
    "d = 0 \n",
    "\n",
    "\n",
    "sol_4_dict = {\n",
    "    'How many users can we make predictions for in the test set?': c, # letter here, \n",
    "    'How many users in the test set are we not able to make predictions for because of the cold start problem?': a, # letter here, \n",
    "    'How many movies can we make predictions for in the test set?': b, # letter here,\n",
    "    'How many movies in the test set are we not able to make predictions for because of the cold start problem?':d # letter here\n",
    "}\n",
    "\n",
    "t.sol_4_test(sol_4_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`5.` Now use the **user_item_train** dataset from above to find U, S, and V transpose using SVD. Then find the subset of rows in the **user_item_test** dataset that you can predict using this matrix decomposition with different numbers of latent features to see how many features makes sense to keep based on the accuracy on the test data. This will require combining what was done in questions `2` - `4`.\n",
    "\n",
    "Use the cells below to explore how well SVD works towards making predictions for recommendations on the test data.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit SVD on the user_item_train matrix\n",
    " # fit svd similar to above then use the cells below\n",
    "u_train, s_train, vt_train = np.linalg.svd(user_item_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((714,), (4487, 4487), (714, 714))"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_train.shape, u_train.shape, vt_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use these cells to see how well you can use the training \n",
    "# decomposition to predict on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find the users that exists in both training and test datasets\n",
    "user_present_both = np.intersect1d(user_item_test.index, user_item_train.index)\n",
    "user_item_test_predictable = user_item_test[user_item_test.index.isin(user_present_both)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((20,), (20, 574))"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_present_both.shape, user_item_test_predictable.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create u_test and vt_test variables\n",
    "common_ids = user_item_train.index.isin(test_idx)\n",
    "common_articles = user_item_train.title.columns.isin(test_arts)\n",
    "u_test = u_train[common_ids, :]\n",
    "vt_test = vt_train[:, common_articles]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# initialize testing parameters\n",
    "num_latent_feats = np.arange(10,700+10,20)\n",
    "sum_errs_train = []\n",
    "sum_errs_test = []\n",
    "\n",
    "for k in num_latent_feats:\n",
    "    # restructure with k latent features for both training and test sets\n",
    "    s_train_lat, u_train_lat, vt_train_lat = np.diag(s_train[:k]), u_train[:, :k], vt_train[:k, :]\n",
    "    u_test_lat, vt_test_lat = u_test[:, :k], vt_test[:k, :]\n",
    "    \n",
    "    # take dot product for both training and test sets\n",
    "    user_item_train_est = np.around(np.dot(np.dot(u_train_lat, s_train_lat), vt_train_lat))\n",
    "    user_item_test_est = np.around(np.dot(np.dot(u_test_lat, s_train_lat), vt_test_lat))\n",
    "    \n",
    "    # compute error for each prediction to actual value\n",
    "    diffs_train = np.subtract(user_item_train, user_item_train_est)\n",
    "    diffs_test = np.subtract(user_item_test_predictable, user_item_test_est)\n",
    "    \n",
    "    # total errors and keep track of them for both training and test sets\n",
    "    err_train = np.sum(np.sum(np.abs(diffs_train)))\n",
    "    err_test = np.sum(np.sum(np.abs(diffs_test)))\n",
    "    sum_errs_train.append(err_train)\n",
    "    sum_errs_test.append(err_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/freemo/anaconda3/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "/home/freemo/anaconda3/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Latent Features'),\n",
       " Text(0, 0.5, 'Accuracy'),\n",
       " Text(0.5, 1.0, 'Train & Test Accuracy vs. Latent Features')]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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L+Kwoqpl2ap3FAXkduCyvA0PzOtC/Sxu/19M5PLm6anxWtJFXZiznnXlFfLJkDSVlRnaLNA7I68hZQ3txaL9c9ujS2n/iO1cJT67uW+av2MBLny7npU+XMXfFBgAGdW/LxYf25bB+ndi3d3u/eu9cEjy5NnNmxrwVG3nx02W8/Oky5n+1EQn2792BX584kBGDutJtl5apDtO5RseTazO1ZlMxz039gicKCpm9bD1pgqF5HfjBQXtx3F5d6dI2O9UhOteoeXJtRkrLjHfnF/FkQSGvz1pBcWkZg3vswv87eS9GDupGbpusVIfoXJPhybUZ+HzVZp4oWMpTkwtZvn4rHVpl8oODenNGfg/27No21eE51yR5cm3C5i7fwB1vLeCF6VF3uEf078yYkwZy1J5d/N5T52LmybUJml64lr/9ZwGvzVpBq8x0LjmsLxcMy6PrLt6O6lx98eTahExctJq/vbWACfOKaJudwY+H9+OCg/vQLicz1aE51+x4cm0Cpny+hv97eQ4TF62mU+tMfj5iT845sBdtslukOjTnmi1Pro3Ymk3F3PLqHB6btJTc1ln8+sSBjNq/Fy0z/SZ/51LNk2sjVFZmPDl5KWNfnsP6rSVcdHAe1xyzB62z/HQ611D4v8ZGZuaX6/jlczP45PO17N+nPTedMshvp3KuAfLk2kis37qdP702j4c+XEz7nExuPWNvTtu3u3ea4lwD5cm1EZgwr4jrn5rOig1bOeeA3lx3bH92yfGLVc41ZJ5cG7DNxSX8/qXZPPzR5/Tr3Jq7f3Awe/dsl+qwnHNJ8OTaQE1espqfPjGNJas3c/EheVx3XH/v6s+5RsSTawNTXFLGbW/M4653PqPbLi159JIDObBvx1SH5ZyrJU+uDcjsZev5yRPTmL1sPWfm9+R/TxjgDwI410jF2nuHpBGS5kpaIGl0JcvbS3pW0nRJEyUNSlj2Y0kzJM2UdE3C/DGSvpA0NQzHx3kM9eW5KV9w8t/ep2jDNu49N5+bTx/sidW5Riy2mqukdOAO4BigEJgkabyZzUoodiMw1cxOlbRnKD88JNlLgKFAMfCKpBfNbH5Y789m9se4Yq9v9767kN++OJuD+nbkjrP3pUMr7wvAucYuzprrUGCBmS00s2LgMeDkCmUGAm8CmNkcoI+kLsAA4CMz22xmJcA7wKkxxpoSZsb/vTSb3744m+O/05UHLtzfE6tzTUScybU7sDRhujDMSzQNOA1A0lCgN9ADmAEcJqmjpBzgeKBnwnpXhqaEcZLax3UAcdpeWsZPn5zG3RMWcu5BvfnrWfuSleF3AzjXVMSZXCt7dMgqTI8F2kuaClwFTAFKzGw2cDPwOvAKURIuCevcCewGDAGWAbdWunPpUkkFkgqKiop27kjq2ObiEi59qIBnPvmCnx6zB785aS/S0/xJK+eakjjvFijk27XNHsCXiQXMbD1wAYCi5zgXhQEzuw+4Lyz7fdgeZraifH1J/wBeqGznZnYPcA9Afn5+xaSeMms2FXPhg5OYtnQt/3fadzhraK9Uh+Sci0GcNddJQD9JeZIygVHA+MQCktqFZQAXAxNCwkVS5/DZi6jp4NEw3S1hE6cSNSE0Cl+s3cLpd33AzC/X8/ez9/PE6lwTFlvN1cxKJF0JvAqkA+PMbKaky8Pyu4guXD0kqRSYBVyUsImnJXUEtgM/MrM1Yf4tkoYQNTEsBi6L6xjq0sqN2zjrno9Ys7mYf144lAP8wQDnmjSZNZhfzLHJz8+3goKClO1/S3Epo/7xEXOXr+fRSw5kn16N8hqcc64CSZPNLL+yZf6EVsxKy4yrH5vC9MK13H3Ofp5YnWsm/P3KMTIzbnphFq/PWsGvTxjIsXt1TXVIzrl64sk1Rve9t4gHPljMRYfkcf7BeakOxzlXjzy5xuTlT5fxu5dmM3JQV35x/IBUh+Ocq2eeXGMweclqrnl8Kvv0bMefzxxCmj8g4Fyz48m1ji1auYmLHyyg2y7Z3Hve/t7BtXPNlCfXOrRmUzEX3D8RSTxwwVDvhMW5ZsyTax0pKzOueXwqX67dyj/O3Y8+nVqlOiTnXAp5cq0jf/3PAt6ZV8SvThzIfr07pDoc51yKeXKtA+/MK+K2N+dx2j7dOfsA7y/AOefJdad9sXYL1zw2hf5d2vC7U79D1LmXc6658+S6E7aVlPLDf31CSalx5zn70TLT7wxwzkW8b4Gd8NsXZjNt6VruOmc/8vwClnMugddcd9CzUwr550dLuOywvowY5H0GOOe+zZPrDpizfD03PPMpQ/M68LPj+qc6HOdcA+TJtZY2bN3OFQ9/QpvsFvzt+/uQke5/Qufcf/M211q66YVZfL56M49eciCd22SnOhznXAPl1a5aWLJqE09/8gXnD+vD0Dx/UMA5VzVPrrXw97c+Iz1NXHZY31SH4pxr4Dy5JqlwzWae/qSQs/bvSee23hzgnKueJ9ck3fXOZ0hw2eG7pToU51wj4Mk1CcvXbeWJSYWcvl9Pdm3XMtXhOOcaAU+uSbh7wmeUmvHDI7zW6pxLjifXGhRt2MYjH3/Oqft0p2eHnFSH45xrJDy51uDedxeyvbSMHx25e6pDcc41Ip5cq7F6UzH//GgJJ+29q3fM4pyrlViTq6QRkuZKWiBpdCXL20t6VtJ0SRMlDUpY9mNJMyTNlHRNwvwOkl6XND98to8r/vveW8iW7aVceZTXWp1ztRNbcpWUDtwBjAQGAmdJGlih2I3AVDMbDJwL3B7WHQRcAgwF9gZOkNQvrDMaeNPM+gFvhuk6t27zdh78YAnHD+rG7p3bxLEL51wTFmfNdSiwwMwWmlkx8BhwcoUyA4kSJGY2B+gjqQswAPjIzDabWQnwDnBqWOdk4MEw/iBwShzB3//BIjZuK/Faq3Nuh8SZXLsDSxOmC8O8RNOA0wAkDQV6Az2AGcBhkjpKygGOB3qGdbqY2TKA8Nm5rgPfsHU7495bxDEDuzCgW9u63rxzrhmIs1esyl4mZRWmxwK3S5oKfApMAUrMbLakm4HXgY1ESbikVjuXLgUuBejVq3YvDXzowyWs31rC1Uf1q7mwc85VIs6aayHf1DYhqpF+mVjAzNab2QVmNoSozTUXWBSW3Wdm+5rZYcBqYH5YbYWkbgDh86vKdm5m95hZvpnl5+bmJh305uIS7ntvEUf0z+U7PXZJej3nnEsUZ3KdBPSTlCcpExgFjE8sIKldWAZwMTDBzNaHZZ3DZy+ipoNHQ7nxwHlh/Dzg+boMumjDNvp0zOEqr7U653ZCbM0CZlYi6UrgVSAdGGdmMyVdHpbfRXTh6iFJpcAs4KKETTwtqSOwHfiRma0J88cCT0i6CPgcOKMu4+7dsRVPXzHMX5HtnNspMqvYDNr05OfnW0FBQarDcM41MZImm1l+Zcv8CS3nnIuBJ1fnnIuBJ1fnnIuBJ1fnnIuBJ1fnnIuBJ1fnnIuBJ1fnnIuBJ1fnnIuBJ1fnnIuBJ1fnnIuBJ1fnnIuBJ1fnnIuBJ1fnnIuBJ1fnnIuBJ1fnnIuBJ1fnnIuBJ1fnnIuBJ1fnnIuBJ1fnnIuBJ1fnnIuBJ1fnnIuBJ1fnnIuBJ1fnnIuBJ1fnnIuBJ1fnnIuBJ1fnnItBrMlV0ghJcyUtkDS6kuXtJT0rabqkiZIGJSy7VtJMSTMkPSopO8wfI+kLSVPDcHycx+CcczsituQqKR24AxgJDATOkjSwQrEbgalmNhg4F7g9rNsduBrIN7NBQDowKmG9P5vZkDC8FNcxOOfcjqoxuUo6QdKOJOGhwAIzW2hmxcBjwMkVygwE3gQwszlAH0ldwrIMoKWkDCAH+HIHYnDOuZRIJmmOAuZLukXSgFpsuzuwNGG6MMxLNA04DUDSUKA30MPMvgD+CHwOLAPWmdlrCetdGZoSxklqX4uYnHOuXtSYXM3sHGAf4DPgfkkfSrpUUpsaVlVlm6swPRZoL2kqcBUwBSgJCfNkIA/YFWgl6Zywzp3AbsAQosR7a6U7j2IskFRQVFRUQ6jOOVe3kvq5b2brgaeJftp3A04FPpF0VTWrFQI9E6Z7UOGnvZmtN7MLzGwIUZtrLrAIOBpYZGZFZrYdeAYYFtZZYWalZlYG/IOo+aGymO8xs3wzy8/NzU3mMJ1zrs4k0+Z6oqRngf8ALYChZjYS2Bu4rppVJwH9JOVJyiRqXhhfYdvtwjKAi4EJIZF/DhwoKUeSgOHA7LBOt4RNnArMSOI4nXOuXmUkUeYMoqvzExJnmtlmSRdWtZKZlUi6EniV6Gr/ODObKenysPwuYADwkKRSYBZwUVj2saSngE+AEqLmgnvCpm+RNISoiWExcFmSx+qcc/VGZhWbQSsUkPKAZWa2NUy3BLqY2eL4w6sb+fn5VlBQkOownHNNjKTJZpZf2bJk2lyfBMoSpkvDPOecc1VIJrlmhPtUAQjjmdWUd865Zi+Z5Fok6aTyCUknAyvjC8k55xq/ZC5oXQ78S9LfiO5dXUp025Rzzrkq1JhczewzotuiWhNdANsQf1jOOde4JVNzRdJ3gb2A7Oi2UzCz/xdjXM4516gl8xDBXcCZRI+niui+194xx+Wcc41aMhe0hpnZucAaM/sNcBDffqzVOedcBckk163hc7OkXYHtRB2qOOecq0Iyba7/ltQO+APR46hG1GGKc865KlSbXEMn2W+a2VrgaUkvANlmtq4+gnPOucaq2maB0K3frQnT2zyxOudczZJpc31N0vdUfg+Wc865GiXT5voToBXRGwK2Et2OZWbWNtbInHOuEUvmCa2aXufinHOughqTq6TDKptfsfNs55xz30imWeBnCePZRO+smgwcFUtEzjnXBCTTLHBi4rSknsAtsUXknHNNQFJvf62gEBhU14E451xTkkyb61+JnsqCKBkPAabFGJNzzjV6ybS5Jr7ZrwR41Mzejyke55xrEpJJrk8BW82sFEBSuqQcM9scb2jOOdd4JdPm+ibQMmG6JfBGPOE451zTkExyzTazjeUTYTwnvpCcc67xSya5bpK0b/mEpP2ALfGF5JxzjV8yba7XAE9K+jJMdyN67Ytzzrkq1FhzNbNJwJ7AFcAPgQFmNjmZjUsaIWmupAWSRleyvL2kZyVNlzRR0qCEZddKmilphqRHJWWH+R0kvS5pfvhsn+zBOudcfUnmBYU/AlqZ2Qwz+xRoLemHSayXDtwBjAQGAmdJGlih2I3AVDMbDJwL3B7W7Q5cDeSb2SAgHRgV1hlN1IF3P6KLbf+VtJ1zLtWSaXO9JLyJAAAzWwNcksR6Q4EFZrbQzIqBx4CTK5QZSJQgMbM5QB9JXcKyDKClpAyiC2jlzRInAw+G8QeBU5KIxTnn6lUyyTUtsaPsUCPNTGK97sDShOnCMC/RNOC0sN2hRK/s7mFmXwB/BD4HlgHrzOy1sE4XM1sGED47V7ZzSZdKKpBUUFRUlES4zjlXd5JJrq8CT0gaLuko4FHg5STWq+zNBVZheizQXtJU4CpgClGn3O2Jaqh5wK5AK0nnJLHPb3Zkdo+Z5ZtZfm5ubm1Wdc65nZbM3QI/By4luqAlogTYLYn1CoGeCdM9+OanPQBmth64ACDUjheF4ThgkZkVhWXPAMOAh4EVkrqZ2TJJ3YCvkojFOefqVTJ3C5QBHwELgXxgODA7iW1PAvpJypOUSXRBanxiAUntwjKAi4EJIeF+DhwoKSck3cR9jgfOC+PnAc8nEYtzztWrKmuukvYgSohnAauAxwHM7MhkNmxmJZKuJGpWSAfGmdlMSZeH5XcBA4CHJJUCs4CLwrKPJT0FfELUWcwU4J6w6bFEzRQXESXhM2p1xM45Vw9kVrEZNCyQyoB3gYvMbEGYt9DM+tZjfHUiPz/fCgoKai7onHO1IGmymeVXtqy6ZoHvAcuBtyT9Q9JwKr9I5ZxzroIqk6uZPWtmZxI9nfU2cC3QRdKdko6tp/icc65RSuaC1iYz+5eZnUB0xX8q/lSUc85Vq1bv0DKz1WZ2t5n5m1+dc64aO/KCwqZvyQewfWuqo3DONWKeXCtaPgPuHwkf3ZHqSJxzjZgn14q6DoI9T4AJf4R1X6Q6GudcI+XJtTLH/Q7KSuGNX6c6EudcI+XJtTLt+8DBP4ZPn4QlH6Y6GudcI+TJtSqHXAtte8DLP4tqsc45VwueXKuSmQPH3gTLP4XJD6Q6GudcI+PJtTp7nQp9DoX/3ASbV6c6GudcI+LJtToSjLwZtq6Dt36f6micc42IJ9eadNkL9r8YCu6L7oF1zrkkeHJNxhE3QHY7ePnnUEUXjc45l8iTazJyOsDwX8KS92DmM6mOxjnXCHhyTda+50HXwfDaL6F4U6qjcc41cJ5ck5WWDsf/AdZ/Ae/emuponHMNnCfX2uh1IOx9Frz3Z5jzYqqjcc41YJ5ca+u7t0K3IfDURVDo7+VyzlXOk2ttZbaC7z8BbbrAI/8Dqz5LdUTOuQbIk+uOaJ0L5zwT3Zb18Pdg08pUR+Sca2A8ue6ojrvB9x+HDcvgkTOheHOqI3LONSCeXHdGz6HwvXvhi8nw9MXee5Zz7mueXHfWgBOj/gfmvgivjPYnuJxzQMzJVdIISXMlLZD0X6/jltRe0rOSpkuaKGlQmN9f0tSEYb2ka8KyMZK+SFh2fJzHkJQDLoODroSJ98AHf011NM65BiAjrg1LSgfuAI4BCoFJksab2ayEYjcCU83sVEl7hvLDzWwuMCRhO18Azyas92cz+2Ncse+QY26KHjB4/ZfQZSDsfnSqI3LOpVCcNdehwAIzW2hmxcBjwMkVygwE3gQwszlAH0ldKpQZDnxmZktijHXnpaXBKXdBx37w4k9h+5ZUR+ScS6E4k2t3YGnCdGGYl2gacBqApKFAb6BHhTKjgEcrzLsyNCWMk9S+7kLeSS2y4YQ/wZrF/oisc81cnMlVlcyreLVnLNBe0lTgKmAKUPL1BqRM4CTgyYR17gR2I2o2WAZUmsUkXSqpQFJBUVHRDh7CDsg7DAafCe/dBkXz6m+/zrkGJc7kWgj0TJjuAXyZWMDM1pvZBWY2BDgXyAUWJRQZCXxiZisS1llhZqVmVgb8g6j54b+Y2T1mlm9m+bm5uXVyQEk79rfRO7he/InfPeBcMxVncp0E9JOUF2qgo4DxiQUktQvLAC4GJpjZ+oQiZ1GhSUBSt4TJU4GG93qA1p3h6DGw+F2Y/niqo3HOpUBsydXMSoArgVeB2cATZjZT0uWSLg/FBgAzJc0hqqX+uHx9STlEdxpU7J36FkmfSpoOHAlcG9cx7JR9z4fu+fDqL2DLmlRH45yrZ7Jm8LM1Pz/fCgpS0IPVsulwz+FRR9sn3lb/+3fOxUrSZDPLr2yZP6EVp26D4YArYPL9sHRSqqNxztUjT65xO/IGaLMrvHAtlJbUXN451yR4co1bVhsYORZWfAoT7051NM65euLJtT4MOAl2Pwb+8ztYV5jqaJxz9cCTa32QopcbWmnUPFBWluqInHMx8+RaXzrkRZ27zH8N3v59qqNxzsUstl6xXCWGXgLLp8OEP0DnATDoe6mOyDkXE6+51icpentszwPhuR/Bl1NTHZFzLiaeXOtbRhac+U/I6QiPfR82fpXqiJxzMfDkmgqtO8NZj0aPxT5+DpRsS3VEzrk65sk1VboNhlP+Dks/hhe89yznmhq/oJVKe50KX82Gd26GLnvBQT9MdUTOuTriNddUO3w07HkCvPYLWPBmqqNxztURT66plpYGp94NuQPgqQtg7dKa13HONXieXBuCrNZw1iNQUgyv/W+qo3HO1QFPrg1F+z5w6E9h1nOw8J1UR+Oc20meXBuSYVdBu97w8s+9e0LnGjlPrg1Ji2w47vdQNBsK7kt1NM65neDJtaHZ87vQ90h463ewaWWqo3HO7SBPrg2NBCNvhuJN8Ob/S3U0zrkd5Mm1IcrtDwdcDp88BF9OSXU0zrkd4Mm1oTr8emjVCV663h+Nda4R8uTaUGXvAkePgcKJMP2JVEfjnKslT64N2d7fh133hdd/Bds2pDoa51wteHJtyNLSondvbVwevb3AOddoeHJt6Hrkw5Cz4cO/w8oFqY7GOZekWJOrpBGS5kpaIGl0JcvbS3pW0nRJEyUNCvP7S5qaMKyXdE1Y1kHS65Lmh8/2cR5DgzD815CRHXXs4m8ucK5RiC25SkoH7gBGAgOBsyQNrFDsRmCqmQ0GzgVuBzCzuWY2xMyGAPsBm4FnwzqjgTfNrB/wZphu2tp0gdPHwcr5cN8xsOqzVEfknKtBnDXXocACM1toZsXAY8DJFcoMJEqQmNkcoI+kLhXKDAc+M7MlYfpk4MEw/iBwSgyxNzx7HAvnvwBb18N9x0Lh5FRH5JyrRpzJtTuQ2DlpYZiXaBpwGoCkoUBvoEeFMqOARxOmu5jZMoDw2bkOY27YeuTDRa9DZit48ASY91qqI3LOVSHO5KpK5lW8G34s0F7SVOAqYArwdXdQkjKBk4Ana71z6VJJBZIKioqKart6w9Vp9yjBdtwdHh0FUx5OdUTOuUrEmVwLgZ4J0z2ALxMLmNl6M7sgtK2eC+QCixKKjAQ+MbMVCfNWSOoGED4rvcJjZveYWb6Z5efm5u70wTQobbrABS9B3mHw/I/gnT/4U1zONTBxJtdJQD9JeaEGOgoYn1hAUruwDOBiYIKZrU8ochbfbhIgbOO8MH4e8HydR94YZLWB7z8Bg8+Et34LL1wLpdtTHZVzLojt7a9mViLpSuBVIB0YZ2YzJV0elt8FDAAeklQKzAIuKl9fUg5wDHBZhU2PBZ6QdBHwOXBGXMfQ4GVkRu/fatMN3r8Nlk+H790HHfJSHZlzzZ6sGfyczM/Pt4KCglSHEa+Zz8L4HwMGJ94Gg76X6oica/IkTTaz/MqW+RNaTcVep8Ll70LunvDUhfD8lVGfsM65lPDk2pS07x1d6Dr0p9FdBPccActnpDoq55olT65NTXoLGP4rOPc52LoO/nEUTPyH303gXD3z5NpU9T0CrvgA+h4OL10Hj/wPrCtMdVTONRueXJuyVp3grMdhxM2w+D2440CYdB+UlaU6MueaPE+uTV1aGhx4eVSL7b4vvPgTePBE7/zFuZh5cm0uOuTBuc/DSX+D5Z/CncPg/duhtKTmdZ1ztebJtTmRYN8fwI8+ht2GR6+PuXd4lGydc3Uqtie0Grrt27dTWFjI1q1bUx1K7LKzs+nRowctWrSIZrTtBqP+BbOeg5d+Ft2ydci1cNjPICMrlaE612Q02+RaWFhImzZt6NOnD1JlHXg1DWbGqlWrKCwsJC8v4bFYKXrwIO9weOWG6B1ds8bDyX+DnkNTF7BzTUSzbRbYunUrHTt2bNKJFUASHTt2rLqGntMBTrsbzn4qeqLrvmPh5dH+dJdzO6nZJlegySfWckkdZ79j4Icfwv4Xwcd3wt8PhM/eij8455qoZp1cU2nVqlUMGTKEIUOG0LVrV7p37/71dHFxcbXrFhQUcPXVV9d9UNlt4bu3wvkvQVoL+OcpUX+xW9bW/b6ca+KabZtrqnXs2JGpU6cCMGbMGFq3bs1111339fKSkhIyMio/Pfn5+eTnV9oRT93oczBc8T68czO8/xeY/0aUdAecEN8+nWtivObagJx//vn85Cc/4cgjj+TnP/85EydOZNiwYeyzzz4MGzaMuXPnAvD2229zwglRohszZgwXXnghRxxxBH379uUvf/lL3QTToiUcPQYueRNa5cLjZ8MT5/mrvZ1Lktdcgd/8eyazvlxfc8FaGLhrW3594l61Xm/evHm88cYbpKens379eiZMmEBGRgZvvPEGN954I08//fR/rTNnzhzeeustNmzYQP/+/bniiiu+ue1qZ+26D1z6VvTAwTs3w6J3YMTY6A0IzaTN2rkd4cm1gTnjjDNIT08HYN26dZx33nnMnz8fSWzfXvlrXL773e+SlZVFVlYWnTt3ZsWKFfToUfElujshvQUcdh0MODHqJ/bZy+DTJ+GE26BdzxpXd6458uQKO1TDjEurVq2+Hv/lL3/JkUceybPPPsvixYs54ogjKl0nK+ubG//T09MpKYnpkdbc/nDhKzDpXnjjN9EdBUeMhsGjoHUTewmkczvJ21wbsHXr1tG9e3cAHnjggdQGUy4tHQ64LLptq+dQeO1/4dY94P7vwsd3w/ova96Gc82AJ9cG7Prrr+eGG27g4IMPprS0NNXhfFv73nDOM3D5e3DodbB5Jbx8PfxpANx7DHzwV1izJNVROpcyzfYFhbNnz2bAgAEpiqj+1cvxFs2D2c9Hj9Eunx7N2/1oOOx66HVAvPt2LgWqe0Ght7m6upO7B+T+LOoAZvUi+PSp6GmvccdCn0Ph8OujT7/LwDUD3izg4tEhDw7/GVzzKRz7O1g5L+qke9wIWPCGv9PLNXmeXF28MlvBsCvhx9Ph+D9G7/F6+HvRixNnPgvFm1MdoXOx8GYBVz9aZMPQS2Df82DaI/Dun+DJ86FFTtRpzICTYI/jIKtNqiN1rk54cnX1KyMT9jsfhpwDS96LLn7N/jfMeh7Ss2D34VGi7T8CWrZPdbTO7bBYk6ukEcDtQDpwr5mNrbC8PTAO2A3YClxoZjPCsnbAvcAgwMKyDyWNAS4BisJmbjSzl+I8DheD9Izo9d99j4Dj/wBLP/4m0c59CdIyYNd9ofcw6HMI9Dwg6rXLuUYituQqKR24AzgGKAQmSRpvZrMSit0ITDWzUyXtGcoPD8tuB14xs9MlZQI5Cev92cz+GFfs9WHVqlUMHx4d6vLly0lPTyc3N3rKaeLEiWRmZla7/ttvv01mZibDhg2LPdbYpaVHSbT3MBjxf/DFJzDnBVjyPnx4B7x/GygNun4Heh8SEu7BXrN1DVqcNdehwAIzWwgg6THgZCAxuQ4E/g/AzOZI6iOpC7AFOAw4PywrBqrv5LSRqanLwZq8/fbbtG7dumkk10QS9NgvGiC64FU4KUq0Sz6Agvvgozuimm2fQ2HgSbDnCdC6c2rjdq6COO8W6A4sTZguDPMSTQNOA5A0FOgN9AD6Ev3sv1/SFEn3SmqVsN6VkqZLGheaFpqEyZMnc/jhh7Pffvtx3HHHsWzZMgD+8pe/MHDgQAYPHsyoUaNYvHgxd911F3/+858ZMmQI7777boojj1FmDvQ9HI68Ec5/AUZ/Dhe8AgddCWuXwAvXwq39/fFb1+DEWXOt7E7xijc3jgVulzQV+BSYApQALYB9gavM7GNJtwOjgV8CdwI3hW3dBNwKXPhfO5cuBS4F6NWrV/WRvjy67l8v3fU7MHJszeUCM+Oqq67i+eefJzc3l8cff5xf/OIXjBs3jrFjx7Jo0SKysrJYu3Yt7dq14/LLL691bbdJyMiC3gdFw9FjYMVMmD0+aq99+fpo6Dak+iaDlu2jTmhy+0On/tBxN3/rratzcSbXQiCxP7oewLeqFWa2HrgAQNGLnhaFIQcoNLOPQ9GniJIrZraifH1J/wBeqGznZnYPcA9Ej7/u/OHEa9u2bcyYMYNjjjkGgNLSUrp16wbA4MGDOfvssznllFM45ZRTUhhlAyNB10HRcOSNsHJ+dNfBwrerecGiwZpF0T225f/XKx069I2SbYe+kF5Ne3erTt8k5ba7+tNmrkpxJtdJQD9JecAXwCjg+4kFwh0Bm0Ob6sXAhJBw10taKqm/mc0lusg1K6zTzcyWhU2cCszY6UhrUcOMi5mx11578eGHH/7XshdffJEJEyYwfvx4brrpJmbOnJmCCBuBTv2ifmcPS6I2X7wZVs2P+kNYOReK5kTj81+Dsqo6yTGwsm8mM9uER373jBJu+7yoLdg1XnuMgLS6aS2N7ZtgZiWSrgReJboVa5yZzZR0eVh+FzAAeEhSKVHyvChhE1cB/wp3Ciwk1HCBWyQNIap2LAYui+sY6lNWVhZFRUV8+OGHHHTQQWzfvp158+YxYMAAli5dypFHHskhhxzCI488wsaNG2nTpg3r19ft2xOalcwc6LZ3NCTLDDYVhUQ8NxpWzo0e5536r/hidfXnlyupq0tRsf43G+4/fanCvLsSxj8E+lWx7lTgv3qbMbMf1G2UDUNaWhpPPfUUV199NevWraOkpIRrrrmGPfbYg3POOYd169ZhZlx77bW0a9eOE088kdNPP53nn3+ev/71rxx66KGpPoSmT4ruSmjdGfIO+/ayLWtg7efeZ0JjV4e/PLzLwWaiuR2vc/Whui4HveMW55yLgSdX55yLgSdX55yLQbNOrs2hvRmaz3E615A02+SanZ3NqlWrmnziMTNWrVpFdnZ2qkNxrllptnc89+jRg8LCQoqKimou3MhlZ2fTo0ePVIfhXLPSbJNrixYtyMvLS3UYzrkmqtk2CzjnXJw8uTrnXAw8uTrnXAyaxeOvkoqAJTUU6wSsrIdwaqMhxgQNMy6PKTkNMSZomHElE1NvM8utbEGzSK7JkFRQ1TPCqdIQY4KGGZfHlJyGGBM0zLh2NiZvFnDOuRh4cnXOuRh4cv3GPakOoBINMSZomHF5TMlpiDFBw4xrp2LyNlfnnIuB11ydcy4GzT65Shohaa6kBZJG1/O+x0n6StKMhHkdJL0uaX74bJ+w7IYQ51xJx8UUU09Jb0maLWmmpB+nOi5J2ZImSpoWYvpNqmNK2E+6pCmSXmhAMS2W9KmkqZIKGkJcktpJekrSnPDdOijF36n+4e9TPqyXdE2dxmRmzXYgenHiZ0BfIBOYBgysx/0fBuwLzEiYdwswOoyPBm4O4wNDfFlAXog7PYaYugH7hvE2wLyw75TFBQhoHcZbAB8DB6b6bxX29RPgEeCFhnD+wr4WA50qzEv19+pB4OIwngm0S3VMCbGlA8uB3nUZUyzBNpYBOAh4NWH6BuCGeo6hD99OrnOBbmG8GzC3stiI3qp7UD3E9zxwTEOJC8gBPgEOSHVMQA/gTeCohOSa8r9TFck1ZXEBbYFFhGs8DSGmCnEcC7xf1zE192aB7sDShOnCMC+VupjZMoDw2TnMr/dYJfUB9iGqKaY0rvDzeyrwFfC6maU8JuA24HqgLGFeqmOC6LXzr0maLOnSBhBXX6AIuD80odwrqVWKY0o0Cng0jNdZTM09uaqSeQ319ol6jVVSa+Bp4BozW19d0Urm1XlcZlZqZkOIaotDJQ1KZUySTgC+MrPJya5Syby4zt/BZrYvMBL4kaTDqilbH3FlEDV/3Wlm+wCbiH5ypzKmaEdSJnAS8GRNRSuZV21MzT25FgI9E6Z7AF+mKJZyKyR1AwifX4X59RarpBZEifVfZvZMQ4kLwMzWAm8DI1Ic08HASZIWA48BR0l6OMUxAWBmX4bPr4BngaEpjqsQKAy/NgCeIkq2Kf9bEf0H9ImZrQjTdRZTc0+uk4B+kvLC/2CjgPEpjmk8cF4YP4+ozbN8/ihJWZLygH7AxLreuSQB9wGzzexPDSEuSbmS2oXxlsDRwJxUxmRmN5hZDzPrQ/S9+Y+ZnZPKmAAktZLUpnycqD1xRirjMrPlwFJJ/cOs4cCsVMaU4Cy+aRIo33fdxBRXI3FjGYDjia6Ifwb8op73/SiwDNhO9D/jRUBHoosk88Nnh4TyvwhxzgVGxhTTIUQ/d6YDU8NwfCrjAgYDU0JMM4Bfhfkp/Vsl7OsIvrmglerz15foqvY0YGb5d7oBxDUEKAjn8DmgfQOIKQdYBeySMK/OYvIntJxzLgbNvVnAOedi4cnVOedi4MnVOedi4MnVOedi4MnVOedi4MnVpYSkjbUoe4SkYTuxr3aSfljN8tIKPST12YF9nCJp4I7G6JoeT66uMTgC2OHkStQDU5XJFdhiZkMShsU7sI9TiHpOSpqkjB3Yj2skPLm6BkPSiZI+Dp17vCGpS6hFXg5cG2qVh4Yntp6WNCkMB4f1xyjqI/dtSQslXR02PRbYLaz/hyRj2U/SO6Hzk1cTHom8JOxzWoghJ9SqTwL+EPaxW4ghP6zTKTwmi6TzJT0p6d9Enau0CjFPCsd9cii3l6I+bKdKmi6pX539oV39iPPJFR98qGoANlYyrz3fvHroYuDWMD4GuC6h3CPAIWG8F9GjuuXlPiDqc7MT0dM3LajQrWMl+y3lm6fRng3rfADkhuVnAuPCeMeE9X4LXBXGHwBOT1j2NpAfxjsBi8P4+URP43UI078Hzgnj7YieFmwF/BU4O8zPBFqm+pz5ULvBf5a4hqQH8HioJWYS9QFamaOBgVE3CAC0LX+eHnjRzLYB2yR9BXRJYr9bLOpxC4DQ49Yg4PWwj3Six5QBBkn6LVEibE3Ur2dtvW5mq8P4sUQdwFwXprOJ/sP4EPiFpB7AM2Y2fwf241LIk6trSP4K/MnMxks6gqgmWpk0oo6KtyTODIlwW8KsUnbsOy5gppkdVMmyB4BTzGyapPOJ2oMrU8I3zW7ZFZZtqrCv75nZ3AplZkv6GPgu8Kqki83sP8kfgks1b3N1DckuwBdh/LyE+RuIXjlT7jXgyvIJSUNq2G7F9WsyF8iVdFDYfgtJe4VlbYBloVvGs6vZx2JgvzB+ejX7ehW4KvRGhqR9wmdfYKGZ/YWoR6bBtYjfNQCeXF2q5EgqTBh+QlRTfVLSu8DKhLL/Bk4tv6AFXA3khws9s4gueFXJzFYB70uakcwFLTMrJkqIN0uaRtQWW363wi+J3szwOlG3h+UeA34WLkrtBvwRuELSB0RtrlW5iaiNd7qiF1XeFOafCcxQ9PaFPYGHaorbNSzeK5ZzzsXAa67OORcDT67OORcDT67OORcDT67OORcDT67OORcDT67OORcDT67OORcDT67OOReD/w96lybr43YQMAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the results with seaborn, set graph size to fit the screen\n",
    "plt.figure(figsize=(5, 5))\n",
    "\n",
    "# plot the train data \n",
    "pic = sns.lineplot(num_latent_feats, 1 - np.array(sum_errs_train)/(user_item_train.shape[0] \n",
    "                                                         * user_item_test_predictable.shape[1]), label='Train')\n",
    "# plot the test data \n",
    "pic = sns.lineplot(num_latent_feats, 1 - np.array(sum_errs_test)/(user_item_test_predictable.shape[0] \n",
    "                                                        * user_item_test_predictable.shape[1]), label='Test')\n",
    "# add labels and title\n",
    "pic.set(xlabel ='Latent Features',\n",
    "         ylabel = 'Accuracy', \n",
    "         title ='Train & Test Accuracy vs. Latent Features')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "`6.` Use the cell below to comment on the results you found in the previous question. Given the circumstances of your results, discuss what you might do to determine if the recommendations you make with any of the above recommendation systems are an improvement to how users currently find articles? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your response here.**\n",
    "\n",
    "RESPONSE:\n",
    "The train set and test set are showing completely different patterns. **While the accuracy of the train test increases, the one of the test set decreases with the higher number of latent features**. \n",
    "\n",
    "It appears that there are only 20 matched users in the two data sets for whcih collaborative filtering method can be used. For the rest content based and rank based filtering recommendations can be used. In collaborative filtering, we are using the connections of users and items. In content based techniques, we are using information about the users and items, but not connections (hence the usefulness when we do not have a lot of internal data already available to use).\n",
    "\n",
    "For just 20 users we can confirm that the number of users is not enough to make conclusive results about the model. Furheremore, we should be careful with the accuracy metric (number of correct predictions/number of rows in data). As many authors claim, counting only on accuracy is very often misleading. \n",
    "\n",
    "\n",
    "\n",
    "As for validating the test, we can use A/B test experiment to get more isnights and vlaidate the methods in use. We can separate two groups of users (A and B). One half will use our recommendation engine and the other half will use a random recommendation. The below steps can help:\n",
    "1. Plan the experiment (we should divide users by cookie, as we can split visitors on their initial visit and it's fairly reliable for tracking). The test can be run for 21 days.\n",
    "2. Decide on metrics (ex. invariant metric can be the number of cookies at the home page)\n",
    "3. Decide on sizing - we should take into consideration the number of unique visitors per day.\n",
    "4. Analize data\n",
    "5. Draw conclusions\n",
    "\n"
   ]
  },
  {
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   "source": [
    "### <a class=\"anchor\" id=\"resources\">Part VII : Resources </a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "During my work I used several sources to complete the exercises like Udacity Lessons, Scikitlearn, Github, Pandas, and more. I list a few links below that might help other students.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Machine Learning Overfitting - https://elitedatascience.com/overfitting-in-machine-learning\n",
    "- T function in python https://www.geeksforgeeks.org/pandas-dataframe-t-function-in-python/\n",
    "- Seaborn https://seaborn.pydata.org/generated/seaborn.lineplot.html\n",
    "- Seaborn labels https://www.geeksforgeeks.org/how-to-set-axes-labels-limits-in-a-seaborn-plot/\n",
    "- duplicate values https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop_duplicates.html\n",
    "- Pandas https://pandas.pydata.org/\n",
    "- Numpy https://numpy.org/"
   ]
  },
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   "source": []
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