Medina_Amazon_Recommendation_System.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pXQzH0nC5JtP"
   },
   "source": [
    "# Project- Recommendation Systems: Amazon product reviews\n",
    "\n",
    "--------------\n",
    "### Context: \n",
    "--------------\n",
    "\n",
    "Online E-commerce websites like Amazon, Flipkart uses different recommendation models to provide personalized suggestions to different users. Amazon currently uses item-to-item collaborative filtering, which scales to massive data sets and produces high-quality recommendations in real-time.\n",
    "\n",
    "----------------\n",
    "### Objective:\n",
    "----------------\n",
    "\n",
    "Build a recommendation system to recommend products to customers based on their previous ratings for other products.\n",
    "\n",
    "--------------\n",
    "### Dataset:\n",
    "--------------\n",
    "\n",
    "The Amazon dataset contains the following attributes:\n",
    "\n",
    "- **userId:** Every user identified with a unique id\n",
    "- **productId:** Every product identified with a unique id\n",
    "- **Rating:** Rating of the corresponding product by the corresponding user\n",
    "- **timestamp:** Time of the rating (ignore this column for this exercise)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZJjsuiDs5JtP"
   },
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "jzu2P-TT5JtP"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NrXYJAv95JtP"
   },
   "source": [
    "### Loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "n5YFrN4nop5k",
    "outputId": "7d0f9cae-3dcd-48f4-8e99-a20a414cfe0c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "JGb-Hk1B5JtP"
   },
   "outputs": [],
   "source": [
    "#Import the data set\n",
    "#df = pd.read_csv('ratings_Electronics.csv', header=None) #There are no headers in the data file\n",
    "df= pd.read_csv('/content/drive/MyDrive/Ratings_Electronics.csv',header=None)\n",
    "\n",
    "df.columns = ['user_id', 'prod_id', 'rating', 'timestamp'] #Adding column names\n",
    "\n",
    "df = df.drop('timestamp', axis=1) #Dropping timestamp\n",
    "\n",
    "df_copy = df.copy(deep=True) #Copying the data to another dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 200
    },
    "id": "aL1JZ00o5JtQ",
    "outputId": "a009d17b-600a-46ca-8bc5-3f4ef5a7d468"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>prod_id</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AKM1MP6P0OYPR</td>\n",
       "      <td>0132793040</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2CX7LUOHB2NDG</td>\n",
       "      <td>0321732944</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2NWSAGRHCP8N5</td>\n",
       "      <td>0439886341</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A2WNBOD3WNDNKT</td>\n",
       "      <td>0439886341</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A1GI0U4ZRJA8WN</td>\n",
       "      <td>0439886341</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          user_id     prod_id  rating\n",
       "0   AKM1MP6P0OYPR  0132793040     5.0\n",
       "1  A2CX7LUOHB2NDG  0321732944     5.0\n",
       "2  A2NWSAGRHCP8N5  0439886341     1.0\n",
       "3  A2WNBOD3WNDNKT  0439886341     3.0\n",
       "4  A1GI0U4ZRJA8WN  0439886341     1.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see few rows of the imported dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cZSTbNLK-re5"
   },
   "source": [
    "#### Shape of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fJ4eQKaY5JtQ",
    "outputId": "c7c5826b-1d1e-45c7-8a43-77d0b39315d2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of rows:  7824482\n",
      "No of columns:  3\n"
     ]
    }
   ],
   "source": [
    "# Check the number of rows and columns\n",
    "rows, columns = df.shape[0], df.shape[1]\n",
    "print(\"No of rows: \", rows) \n",
    "print(\"No of columns: \", columns) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6VZB4lYj-re6"
   },
   "source": [
    "#### Data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SVrgMkye5JtQ",
    "outputId": "d42dcbff-294a-43fb-969a-5486e2c36fea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7824482 entries, 0 to 7824481\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Dtype  \n",
      "---  ------   -----  \n",
      " 0   user_id  object \n",
      " 1   prod_id  object \n",
      " 2   rating   float64\n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 179.1+ MB\n"
     ]
    }
   ],
   "source": [
    "#Check Data types\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1G7o3kqS-re8"
   },
   "source": [
    "#### Checking for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vt-VEjMA5JtQ",
    "outputId": "b41a4298-8ec7-4e57-badc-36c8e1c808ef"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Are there any missing values? False\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "user_id    0\n",
       "prod_id    0\n",
       "rating     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find number of missing values in each column\n",
    "print('Are there any missing values? ' + str(df.isna().any().any()))\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mx3i0tEA-re9"
   },
   "source": [
    "#### Summary Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tYm30MXR5JtR",
    "outputId": "177a1328-1a72-4a18-835d-82b3745badba"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    7.824482e+06\n",
       "mean     4.012337e+00\n",
       "std      1.380910e+00\n",
       "min      1.000000e+00\n",
       "25%      3.000000e+00\n",
       "50%      5.000000e+00\n",
       "75%      5.000000e+00\n",
       "max      5.000000e+00\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary statistics of 'rating' variable\n",
    "df['rating'].describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ywyFrZIf5JtR"
   },
   "source": [
    "#### Checking the rating distribution\n",
    "\n",
    "Check the distribution of ratings and **provide observations** from the plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 380
    },
    "id": "wZU4smh_-re9",
    "outputId": "70e6c592-b637-47ec-93e7-8423c874224d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create the bar plot and provide observations\n",
    "plt.figure(figsize = (12,6))\n",
    "df['rating'].value_counts(1).plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rCISZQ8Osemk"
   },
   "source": [
    "**Jose Medina Observations**\n",
    "\n",
    "> - We observe that the distribution is heavily scewed - with ~55% of ratings falling into the 5.0 category, \n",
    "> - Followed by ~20% in the 4.0 category, and ~13% for the 1.0 category."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HefpLdLJxhXd"
   },
   "source": [
    "#### Checking the number of unique users and items in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NbSom7195JtR",
    "outputId": "fb7ae540-6376-4716-925b-fa75f1e433a3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique USERS in Raw data =  4201696\n",
      "Number of unique ITEMS in Raw data =  476002\n"
     ]
    }
   ],
   "source": [
    "# Number of unique user id and product id in the data\n",
    "print('Number of unique USERS in Raw data = ', df['user_id'].nunique())\n",
    "print('Number of unique ITEMS in Raw data = ', df['prod_id'].nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uKr_9J3U-rfJ"
   },
   "source": [
    "- There are **42,01,696 users and 4,76,002 products** in the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-xubWmas-rfJ"
   },
   "source": [
    "#### Users with most number of ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "n7MX452q5JtR",
    "outputId": "ef39043c-b7a9-4d8f-e055-de3f80601c1c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id\n",
       "A5JLAU2ARJ0BO     520\n",
       "ADLVFFE4VBT8      501\n",
       "A3OXHLG6DIBRW8    498\n",
       "A6FIAB28IS79      431\n",
       "A680RUE1FDO8B     406\n",
       "A1ODOGXEYECQQ8    380\n",
       "A36K2N527TXXJN    314\n",
       "A2AY4YUOX2N1BQ    311\n",
       "AWPODHOB4GFWL     308\n",
       "A25C2M3QF9G7OQ    296\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 10 users based on rating\n",
    "most_rated = df.groupby('user_id').size().sort_values(ascending=False)[:10]\n",
    "most_rated"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4J0hoNE8-rfK"
   },
   "source": [
    "- The highest number of ratings by a user is 520 which is far from the actual number of products present in the data. We can build a recommendation system to recommend products to users which they have not interacted with."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1HIMYcy35JtS"
   },
   "source": [
    "### Data preparation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rfJJ_uhJxhXf"
   },
   "source": [
    "**Let's take a subset of the dataset (by only keeping the users who have given 50 or more ratings) to make the dataset less sparse and easy to work with.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "dA26-qr35JtS"
   },
   "outputs": [],
   "source": [
    "counts = df['user_id'].value_counts()\n",
    "df_final = df[df['user_id'].isin(counts[counts >= 50].index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OSBTrRKJ5JtS",
    "outputId": "2f12e3cb-f39b-480b-e42d-ef2a965f0e03"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of observations in the final data = 125871\n",
      "Number of unique USERS in the final data =  1540\n",
      "Number of unique PRODUCTS in the final data =  48190\n"
     ]
    }
   ],
   "source": [
    "print('The number of observations in the final data =', len(df_final))\n",
    "print('Number of unique USERS in the final data = ', df_final['user_id'].nunique())\n",
    "print('Number of unique PRODUCTS in the final data = ', df_final['prod_id'].nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a7FAL0uYxhXg"
   },
   "source": [
    "- The dataframe **df_final has users who have rated 50 or more items**\n",
    "- **We will use df_final to build recommendation systems**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EH9lz35O5JtS"
   },
   "source": [
    "#### Checking the density of the rating matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 343
    },
    "id": "5tdcMN1Q5JtS",
    "outputId": "ca1963d0-7920-40a5-f34d-0671b1b64ecf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of final_ratings_matrix:  (1540, 48190)\n",
      "given_num_of_ratings =  125871\n",
      "possible_num_of_ratings =  74212600\n",
      "density: 0.17%\n"
     ]
    },
    {
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48190 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "prod_id         0594451647  0594481813  ...  B00LI4ZZO8  B00LKG1MC8\n",
       "user_id                                 ...                        \n",
       "A100UD67AHFODS         0.0         0.0  ...         0.0         0.0\n",
       "A100WO06OQR8BQ         0.0         0.0  ...         0.0         0.0\n",
       "A105S56ODHGJEK         0.0         0.0  ...         0.0         0.0\n",
       "A105TOJ6LTVMBG         0.0         0.0  ...         0.0         0.0\n",
       "A10AFVU66A79Y1         0.0         0.0  ...         0.0         0.0\n",
       "\n",
       "[5 rows x 48190 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating the interaction matrix of products and users based on ratings and replacing NaN value with 0\n",
    "final_ratings_matrix = df_final.pivot(index = 'user_id', columns ='prod_id', values = 'rating').fillna(0)\n",
    "print('Shape of final_ratings_matrix: ', final_ratings_matrix.shape)\n",
    "\n",
    "#Finding the number of non-zero entries in the interaction matrix \n",
    "given_num_of_ratings = np.count_nonzero(final_ratings_matrix)\n",
    "print('given_num_of_ratings = ', given_num_of_ratings)\n",
    "\n",
    "#Finding the possible number of ratings as per the number of users and products\n",
    "possible_num_of_ratings = final_ratings_matrix.shape[0] * final_ratings_matrix.shape[1]\n",
    "print('possible_num_of_ratings = ', possible_num_of_ratings)\n",
    "\n",
    "#Density of ratings\n",
    "density = (given_num_of_ratings/possible_num_of_ratings)\n",
    "density *= 100\n",
    "print ('density: {:4.2f}%'.format(density))\n",
    "\n",
    "final_ratings_matrix.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ad8qPpEn-rfM"
   },
   "source": [
    "- Even with the subset of users and products, the current number of ratings is just **0.17%** of the possible number of ratings. This implies that the data is **highly sparse**.\n",
    "- We will build recommendation systems to recommend products to users with which they have not interacted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A6HgV3qY-rfM"
   },
   "source": [
    "Now that we have explored and preprocessed the data, let's build the first recommendation system"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6xYGrGVy5JtS"
   },
   "source": [
    "### Rank Based Recommendation System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 230
    },
    "id": "yxZTj1UPxhXh",
    "outputId": "40118794-b6f0-4c6e-f000-5ee9018c6b91",
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <th>avg_rating</th>\n",
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      "text/plain": [
       "            avg_rating  rating_count\n",
       "prod_id                             \n",
       "0594451647         5.0             1\n",
       "B003RRY9RS         5.0             1\n",
       "B003RR95Q8         5.0             1\n",
       "B003RIPMZU         5.0             1\n",
       "B003RFRNYQ         5.0             2"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate the average rating for each product \n",
    "average_rating = df_final.groupby('prod_id').mean()['rating']\n",
    "\n",
    "#Calculate the count of ratings for each product\n",
    "count_rating = df_final.groupby('prod_id').count()['rating']\n",
    "\n",
    "#Create a dataframe with calculated average and count of ratings\n",
    "final_rating = pd.DataFrame({'avg_rating':average_rating, 'rating_count':count_rating})\n",
    "\n",
    "#Sort the dataframe by average of ratings\n",
    "final_rating = final_rating.sort_values(by=['avg_rating'], ascending=False)\n",
    "\n",
    "final_rating.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "zKU__5s1xhXi"
   },
   "outputs": [],
   "source": [
    "#defining a function to get the top n products based on highest average rating and minimum interactions\n",
    "def top_n_products(final_rating, n, min_interaction):\n",
    "    \n",
    "    #Finding movies with minimum number of interactions\n",
    "    recommendations = final_rating[final_rating['rating_count'] > min_interaction]\n",
    "    \n",
    "    #Sorting values w.r.t average rating \n",
    "    recommendations = recommendations.sort_values(by='avg_rating', ascending=False)\n",
    "    \n",
    "    return recommendations.index[:n]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F8l6373PxhXi"
   },
   "source": [
    "#### Recommending top 5 products with 50 minimum interactions based on popularity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dBxdLiM_xhXi",
    "outputId": "9512bba6-917c-476b-c068-d6e4f0e12647"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['B001TH7GUU', 'B003ES5ZUU', 'B0019EHU8G', 'B006W8U2MU', 'B000QUUFRW']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(top_n_products(final_rating, 5, 50))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l9_xW_UMxhXj"
   },
   "source": [
    "#### Recommending top 5 products with 100 minimum interactions based on popularity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dZgGZCUoxhXj",
    "outputId": "cd0a4a25-1379-4440-b6bc-94b7b567980c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['B003ES5ZUU', 'B000N99BBC', 'B007WTAJTO', 'B002V88HFE', 'B004CLYEDC']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(top_n_products(final_rating, 5, 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BL-m68a15JtT",
    "outputId": "69132b0f-8d3f-4798-f6a0-249e17a3c822"
   },
   "source": [
    "We have recommended the top 5 products by using popularity recommendation system. Now, let's build a recommendation system using collaborative filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PTPxYNE_5JtT"
   },
   "source": [
    "### Collaborative Filtering based Recommendation System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 278
    },
    "id": "AtfdFVux-rfX",
    "outputId": "abe2f2bf-1426-453d-b3a5-a030e944eba8"
   },
   "outputs": [
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       "</div>"
      ],
      "text/plain": [
       "prod_id         0594451647  0594481813  ...  B00LI4ZZO8  B00LKG1MC8\n",
       "user_id                                 ...                        \n",
       "A100UD67AHFODS         0.0         0.0  ...         0.0         0.0\n",
       "A100WO06OQR8BQ         0.0         0.0  ...         0.0         0.0\n",
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       "A10AFVU66A79Y1         0.0         0.0  ...         0.0         0.0\n",
       "\n",
       "[5 rows x 48190 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_ratings_matrix.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2llaY4LxxhXl"
   },
   "source": [
    "**Here, user_id (index) is of the object data type. We will replace the user_id by numbers starting from 0 to 1539 (for all user ids) so that the index is of integer type and represents a user id in the same format**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 278
    },
    "id": "N0nQs0tGxhXl",
    "outputId": "51af6d8a-5163-4dd2-a97d-6630e981a5f7"
   },
   "outputs": [
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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",
       "      <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>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <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",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48190 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "prod_id     0594451647  0594481813  ...  B00LI4ZZO8  B00LKG1MC8\n",
       "user_index                          ...                        \n",
       "0                  0.0         0.0  ...         0.0         0.0\n",
       "1                  0.0         0.0  ...         0.0         0.0\n",
       "2                  0.0         0.0  ...         0.0         0.0\n",
       "3                  0.0         0.0  ...         0.0         0.0\n",
       "4                  0.0         0.0  ...         0.0         0.0\n",
       "\n",
       "[5 rows x 48190 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_ratings_matrix['user_index'] = np.arange(0, final_ratings_matrix.shape[0])\n",
    "final_ratings_matrix.set_index(['user_index'], inplace=True)\n",
    "\n",
    "# Actual ratings given by users\n",
    "final_ratings_matrix.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ft7VMjB2xhXl"
   },
   "source": [
    "Now, let's define a **function to get similar users** for a particular user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "1XkrTk63xhXm"
   },
   "outputs": [],
   "source": [
    "# defining a function to get similar users\n",
    "def similar_users(user_index, interactions_matrix):\n",
    "    similarity = []\n",
    "    for user in range(0, interactions_matrix.shape[0]):\n",
    "        \n",
    "        #finding cosine similarity between the user_id and each user\n",
    "        sim = cosine_similarity([interactions_matrix.loc[user_index]], [interactions_matrix.loc[user]])\n",
    "        \n",
    "        #Appending the user and the corresponding similarity score with user_id as a tuple\n",
    "        similarity.append((user,sim))\n",
    "        \n",
    "    similarity.sort(key=lambda x: x[1], reverse=True)\n",
    "    most_similar_users = [tup[0] for tup in similarity] #Extract the user from each tuple in the sorted list\n",
    "    similarity_score = [tup[1] for tup in similarity]   ##Extracting the similarity score from each tuple in the sorted list\n",
    "   \n",
    "    #Remove the original user and its similarity score and keep only other similar users \n",
    "    most_similar_users.remove(user_index)             \n",
    "    similarity_score.remove(similarity_score[0])\n",
    "       \n",
    "    return most_similar_users, similarity_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7D83LlPCxhXm"
   },
   "source": [
    "#### Finding out top 10 similar users to the user index 3 and their similarity score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9PhhdMdexhXp",
    "outputId": "c3d953c3-c988-4b20-a535-ba818e63bbf3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[320, 12, 793, 261, 156, 1493, 1250, 567, 753, 1360]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similar = similar_users(3, final_ratings_matrix)[0][0:10]\n",
    "similar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Yj_WwDi9xhXp",
    "outputId": "f1800ca6-5948-431d-d707-a7fbc1be82f5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[0.05662371]]),\n",
       " array([[0.05549645]]),\n",
       " array([[0.05098326]]),\n",
       " array([[0.05024185]]),\n",
       " array([[0.05003874]]),\n",
       " array([[0.04930111]]),\n",
       " array([[0.04889354]]),\n",
       " array([[0.04672744]]),\n",
       " array([[0.04637283]]),\n",
       " array([[0.04492668]])]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Print the similarity score\n",
    "similar_users(3, final_ratings_matrix)[1][0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "byKEaRW2xhXq"
   },
   "source": [
    "#### Finding out top 10 similar users to the user index 1521 and their similarity score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kuEJ2upbxhXq",
    "outputId": "2b43fef3-876b-4a32-b076-c64b04524ddb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[991, 1065, 1012, 1402, 1371, 1278, 1518, 692, 785, 161]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similar = similar_users(1521, final_ratings_matrix)[0][0:10]\n",
    "similar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_gAVJ54QxhXr",
    "outputId": "60213adc-f3b2-40d3-b10d-8e29143af697"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[0.10889304]]),\n",
       " array([[0.10538068]]),\n",
       " array([[0.1026758]]),\n",
       " array([[0.09418291]]),\n",
       " array([[0.09149062]]),\n",
       " array([[0.09135361]]),\n",
       " array([[0.09028898]]),\n",
       " array([[0.08735684]]),\n",
       " array([[0.08673386]]),\n",
       " array([[0.08478815]])]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Print the similarity score\n",
    "similar_users(1521, final_ratings_matrix)[1][0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pPb0VF2gxhXs"
   },
   "source": [
    "We have found similar users for a given user. Now, let's create **a function to recommend products** to the user using the ratings given by similar users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "xlU3CoIMxhXs"
   },
   "outputs": [],
   "source": [
    "# defining the recommendations function to get recommendations by using the similar users' preferences\n",
    "def recommendations(user_index, num_of_products, interactions_matrix):\n",
    "    \n",
    "    #Saving similar users using the function similar_users defined above\n",
    "    most_similar_users = similar_users(user_index, interactions_matrix)[0]\n",
    "    \n",
    "    #Finding product IDs with which the user_id has interacted\n",
    "    prod_ids = set(list(interactions_matrix.columns[np.where(interactions_matrix.loc[user_index] > 0)]))\n",
    "    recommendations = []\n",
    "    \n",
    "    observed_interactions = prod_ids.copy()\n",
    "    for similar_user in most_similar_users:\n",
    "        if len(recommendations) < num_of_products:\n",
    "            \n",
    "            #Finding 'n' products which have been rated by similar users but not by the user_id\n",
    "            similar_user_prod_ids = set(list(interactions_matrix.columns[np.where(interactions_matrix.loc[similar_user] > 0)]))\n",
    "            recommendations.extend(list(similar_user_prod_ids.difference(observed_interactions)))\n",
    "            observed_interactions = observed_interactions.union(similar_user_prod_ids)\n",
    "        else:\n",
    "            break\n",
    "    \n",
    "    return recommendations[:num_of_products]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FDwdhxgtxhXt"
   },
   "source": [
    "#### Recommend 5 products to user index 3 based on similarity based collaborative filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ft4eij9oxhXt",
    "outputId": "3e0ff2e7-4496-4c40-981d-b24500fbfbad"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['B0025ZH43O', 'B009SUUEO0', 'B000EVSI7M', 'B008PWUS4I', 'B009PK9S90']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommendations(2, 5, final_ratings_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tFlXjpUNxhXt"
   },
   "source": [
    "#### Recommend 5 products to user index 1521 based on similarity based collaborative filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4huRtxwrxhXu",
    "outputId": "37bfae42-2f61-45c9-ca94-3d26f0d2d63d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['B0035JJJQE', 'B007X3VEUW', 'B006GDTTM0', 'B0080EBMM8', 'B007FZN53W']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommendations(1521, 5, final_ratings_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2JK8z6gK-rfd"
   },
   "source": [
    "We have applied two technique to recommend products to users. Now, let's build one more recommendation system using matrix factorization (SVD)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t5_5gqSO5JtT"
   },
   "source": [
    "### Model based Collaborative Filtering: Singular Value Decomposition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "afH6lNy55JtT"
   },
   "source": [
    "**We have seen above that the interaction matrix is highly sparse. SVD is best to apply on a large sparse matrix. Note that for sparse matrices, we can use the sparse.linalg.svds() function to perform the decomposition**\n",
    "\n",
    "Also, we will use **k=50 latent features** to predict rating of products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "q-fhNCQY5JtT"
   },
   "outputs": [],
   "source": [
    "from scipy.sparse.linalg import svds # for sparse matrices\n",
    "\n",
    "# Singular Value Decomposition\n",
    "U, s, Vt = svds(final_ratings_matrix, k = 50) # here k is the number of latent features\n",
    "\n",
    "# Construct diagonal array in SVD\n",
    "sigma = np.diag(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "m8ioK5qoxhXv",
    "outputId": "8f879c3d-a652-4715-cee0-847c6614bfcb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1540, 50)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "U.shape #checking the shape of the U matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JF34FNtYxhXv",
    "outputId": "29530293-e057-41df-e246-9407301b51e6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 50)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigma.shape #checking the shape of the sigma matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d8elBUrMxhXv",
    "outputId": "fec0de23-a255-4dce-d21f-d8a19fa283a7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 48190)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Vt.shape #checking the shape of the Vt matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PC0Ht_jy-rfp"
   },
   "source": [
    "Now, let's regenerate the original matrix using U, Sigma, and Vt matrices. The resulting matrix would be the predicted ratings for all users and products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 247
    },
    "id": "kqFD9mQj5JtT",
    "outputId": "c9fa085d-2c9c-4d6c-8c53-397021c62bb9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>prod_id</th>\n",
       "      <th>0594451647</th>\n",
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       "      <th>787988002X</th>\n",
       "      <th>8862935307</th>\n",
       "      <th>8862936826</th>\n",
       "      <th>9043413585</th>\n",
       "      <th>9573212900</th>\n",
       "      <th>9573212919</th>\n",
       "      <th>9575871979</th>\n",
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       "      <th>9984984362</th>\n",
       "      <th>9985511298</th>\n",
       "      <th>9985511476</th>\n",
       "      <th>9985525264</th>\n",
       "      <th>9985696999</th>\n",
       "      <th>9989360391</th>\n",
       "      <th>B000000O48</th>\n",
       "      <th>B000001OKH</th>\n",
       "      <th>B000001OL6</th>\n",
       "      <th>...</th>\n",
       "      <th>B00KSTODWY</th>\n",
       "      <th>B00KT17TZ4</th>\n",
       "      <th>B00KTIYN7O</th>\n",
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       "      <th>B00KV6FFN0</th>\n",
       "      <th>B00KVLY8E2</th>\n",
       "      <th>B00KVNY2KA</th>\n",
       "      <th>B00KW2W6B2</th>\n",
       "      <th>B00KW3OFG0</th>\n",
       "      <th>B00KWHMR6G</th>\n",
       "      <th>B00KWL62QI</th>\n",
       "      <th>B00KWMNDDM</th>\n",
       "      <th>B00KWPRSJY</th>\n",
       "      <th>B00KWWY1PQ</th>\n",
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       "      <th>B00KXUHX9S</th>\n",
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       "      <th>B00L1NZTSS</th>\n",
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       "      <th>B00L3YHF6O</th>\n",
       "      <th>B00L403O94</th>\n",
       "      <th>B00L43HAY6</th>\n",
       "      <th>B00L5YZCCG</th>\n",
       "      <th>B00L8I6SFY</th>\n",
       "      <th>B00L8QCVL6</th>\n",
       "      <th>B00LA6T0LS</th>\n",
       "      <th>B00LBZ1Z7K</th>\n",
       "      <th>B00LED02VY</th>\n",
       "      <th>B00LGN7Y3G</th>\n",
       "      <th>B00LGQ6HL8</th>\n",
       "      <th>B00LI4ZZO8</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.005086</td>\n",
       "      <td>0.002178</td>\n",
       "      <td>0.003668</td>\n",
       "      <td>0.040843</td>\n",
       "      <td>0.009640</td>\n",
       "      <td>0.006808</td>\n",
       "      <td>0.020659</td>\n",
       "      <td>0.000649</td>\n",
       "      <td>0.020331</td>\n",
       "      <td>0.005633</td>\n",
       "      <td>0.002952</td>\n",
       "      <td>0.008525</td>\n",
       "      <td>0.001923</td>\n",
       "      <td>0.000429</td>\n",
       "      <td>0.002886</td>\n",
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       "      <td>0.020008</td>\n",
       "      <td>0.003876</td>\n",
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       "      <td>0.056064</td>\n",
       "      <td>0.006805</td>\n",
       "      <td>0.004928</td>\n",
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       "      <td>0.003610</td>\n",
       "      <td>0.030306</td>\n",
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       "      <td>0.002063</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.011196</td>\n",
       "      <td>0.000988</td>\n",
       "      <td>0.003445</td>\n",
       "      <td>0.003703</td>\n",
       "      <td>0.004937</td>\n",
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       "      <td>0.001626</td>\n",
       "      <td>0.002329</td>\n",
       "      <td>0.005118</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.020934</td>\n",
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       "      <td>0.080949</td>\n",
       "      <td>0.305623</td>\n",
       "      <td>0.023159</td>\n",
       "      <td>0.051055</td>\n",
       "      <td>0.001275</td>\n",
       "      <td>0.306615</td>\n",
       "      <td>0.032136</td>\n",
       "      <td>0.003623</td>\n",
       "      <td>0.033317</td>\n",
       "      <td>0.211126</td>\n",
       "      <td>0.021486</td>\n",
       "      <td>0.000569</td>\n",
       "      <td>0.012997</td>\n",
       "      <td>0.186545</td>\n",
       "      <td>0.080949</td>\n",
       "      <td>0.112158</td>\n",
       "      <td>0.028931</td>\n",
       "      <td>0.007026</td>\n",
       "      <td>0.379500</td>\n",
       "      <td>0.158210</td>\n",
       "      <td>0.201766</td>\n",
       "      <td>0.089726</td>\n",
       "      <td>0.206036</td>\n",
       "      <td>0.013959</td>\n",
       "      <td>0.063592</td>\n",
       "      <td>0.000238</td>\n",
       "      <td>0.061477</td>\n",
       "      <td>0.001214</td>\n",
       "      <td>0.123433</td>\n",
       "      <td>0.028490</td>\n",
       "      <td>0.016109</td>\n",
       "      <td>0.002855</td>\n",
       "      <td>0.174568</td>\n",
       "      <td>0.011367</td>\n",
       "      <td>0.012997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.002286</td>\n",
       "      <td>0.010898</td>\n",
       "      <td>0.000724</td>\n",
       "      <td>0.130259</td>\n",
       "      <td>0.007506</td>\n",
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       "      <td>0.063711</td>\n",
       "      <td>0.000674</td>\n",
       "      <td>0.016111</td>\n",
       "      <td>0.002433</td>\n",
       "      <td>0.023243</td>\n",
       "      <td>0.004748</td>\n",
       "      <td>0.007898</td>\n",
       "      <td>0.000623</td>\n",
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       "      <td>0.004533</td>\n",
       "      <td>0.001912</td>\n",
       "      <td>0.014662</td>\n",
       "      <td>0.015882</td>\n",
       "      <td>0.003647</td>\n",
       "      <td>0.003395</td>\n",
       "      <td>0.001423</td>\n",
       "      <td>0.001034</td>\n",
       "      <td>0.001213</td>\n",
       "      <td>0.000066</td>\n",
       "      <td>0.007949</td>\n",
       "      <td>0.000794</td>\n",
       "      <td>0.030156</td>\n",
       "      <td>0.008864</td>\n",
       "      <td>...</td>\n",
       "      <td>0.028062</td>\n",
       "      <td>0.069751</td>\n",
       "      <td>0.100250</td>\n",
       "      <td>0.051989</td>\n",
       "      <td>0.000351</td>\n",
       "      <td>0.038586</td>\n",
       "      <td>0.018456</td>\n",
       "      <td>0.012207</td>\n",
       "      <td>0.090763</td>\n",
       "      <td>0.011983</td>\n",
       "      <td>0.000578</td>\n",
       "      <td>0.004127</td>\n",
       "      <td>0.034131</td>\n",
       "      <td>0.065956</td>\n",
       "      <td>0.109507</td>\n",
       "      <td>0.022539</td>\n",
       "      <td>0.000627</td>\n",
       "      <td>0.038570</td>\n",
       "      <td>0.088151</td>\n",
       "      <td>0.038586</td>\n",
       "      <td>0.045624</td>\n",
       "      <td>0.023247</td>\n",
       "      <td>0.008727</td>\n",
       "      <td>0.021204</td>\n",
       "      <td>0.017318</td>\n",
       "      <td>0.015402</td>\n",
       "      <td>0.036499</td>\n",
       "      <td>0.180672</td>\n",
       "      <td>0.027860</td>\n",
       "      <td>0.059609</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.013766</td>\n",
       "      <td>0.001473</td>\n",
       "      <td>0.025588</td>\n",
       "      <td>0.042103</td>\n",
       "      <td>0.004251</td>\n",
       "      <td>0.002177</td>\n",
       "      <td>0.024362</td>\n",
       "      <td>0.014765</td>\n",
       "      <td>0.038570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.001655</td>\n",
       "      <td>0.002675</td>\n",
       "      <td>0.007355</td>\n",
       "      <td>0.007264</td>\n",
       "      <td>0.005152</td>\n",
       "      <td>0.003986</td>\n",
       "      <td>0.003480</td>\n",
       "      <td>0.006961</td>\n",
       "      <td>0.006606</td>\n",
       "      <td>0.002719</td>\n",
       "      <td>0.003830</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>0.052219</td>\n",
       "      <td>0.000099</td>\n",
       "      <td>0.001889</td>\n",
       "      <td>0.001633</td>\n",
       "      <td>0.009597</td>\n",
       "      <td>0.002312</td>\n",
       "      <td>0.013302</td>\n",
       "      <td>0.030823</td>\n",
       "      <td>0.004591</td>\n",
       "      <td>0.005223</td>\n",
       "      <td>0.005087</td>\n",
       "      <td>0.047911</td>\n",
       "      <td>0.011540</td>\n",
       "      <td>0.000369</td>\n",
       "      <td>0.005437</td>\n",
       "      <td>0.001589</td>\n",
       "      <td>0.016260</td>\n",
       "      <td>0.003775</td>\n",
       "      <td>0.001121</td>\n",
       "      <td>0.002047</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.001266</td>\n",
       "      <td>0.001747</td>\n",
       "      <td>0.000410</td>\n",
       "      <td>0.006464</td>\n",
       "      <td>0.000288</td>\n",
       "      <td>0.002617</td>\n",
       "      <td>0.004736</td>\n",
       "      <td>...</td>\n",
       "      <td>0.034186</td>\n",
       "      <td>0.018237</td>\n",
       "      <td>0.006772</td>\n",
       "      <td>0.073118</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>0.043818</td>\n",
       "      <td>0.033330</td>\n",
       "      <td>0.011557</td>\n",
       "      <td>0.075729</td>\n",
       "      <td>0.007235</td>\n",
       "      <td>0.008621</td>\n",
       "      <td>0.112510</td>\n",
       "      <td>0.011972</td>\n",
       "      <td>0.058533</td>\n",
       "      <td>0.043062</td>\n",
       "      <td>0.012472</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>0.005964</td>\n",
       "      <td>0.107058</td>\n",
       "      <td>0.043818</td>\n",
       "      <td>0.032366</td>\n",
       "      <td>0.072784</td>\n",
       "      <td>0.029582</td>\n",
       "      <td>0.125087</td>\n",
       "      <td>0.003514</td>\n",
       "      <td>0.015004</td>\n",
       "      <td>0.025893</td>\n",
       "      <td>0.178518</td>\n",
       "      <td>0.020809</td>\n",
       "      <td>0.008984</td>\n",
       "      <td>0.001708</td>\n",
       "      <td>0.051040</td>\n",
       "      <td>0.000325</td>\n",
       "      <td>0.054867</td>\n",
       "      <td>0.017870</td>\n",
       "      <td>0.004996</td>\n",
       "      <td>0.002426</td>\n",
       "      <td>0.083928</td>\n",
       "      <td>0.112205</td>\n",
       "      <td>0.005964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.001856</td>\n",
       "      <td>0.011019</td>\n",
       "      <td>0.005910</td>\n",
       "      <td>0.014134</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>0.001877</td>\n",
       "      <td>0.005391</td>\n",
       "      <td>0.001709</td>\n",
       "      <td>0.004968</td>\n",
       "      <td>0.001402</td>\n",
       "      <td>0.004506</td>\n",
       "      <td>0.005046</td>\n",
       "      <td>0.005222</td>\n",
       "      <td>0.009216</td>\n",
       "      <td>0.002443</td>\n",
       "      <td>0.003019</td>\n",
       "      <td>0.012704</td>\n",
       "      <td>0.007380</td>\n",
       "      <td>0.002409</td>\n",
       "      <td>0.007586</td>\n",
       "      <td>0.008402</td>\n",
       "      <td>0.004391</td>\n",
       "      <td>0.006039</td>\n",
       "      <td>0.033126</td>\n",
       "      <td>0.001186</td>\n",
       "      <td>0.000356</td>\n",
       "      <td>0.010548</td>\n",
       "      <td>0.000258</td>\n",
       "      <td>0.031719</td>\n",
       "      <td>0.000704</td>\n",
       "      <td>0.000331</td>\n",
       "      <td>0.009481</td>\n",
       "      <td>0.000550</td>\n",
       "      <td>0.001440</td>\n",
       "      <td>0.001920</td>\n",
       "      <td>0.000710</td>\n",
       "      <td>0.001126</td>\n",
       "      <td>0.000525</td>\n",
       "      <td>0.001888</td>\n",
       "      <td>0.007548</td>\n",
       "      <td>...</td>\n",
       "      <td>0.032608</td>\n",
       "      <td>0.025964</td>\n",
       "      <td>0.026600</td>\n",
       "      <td>0.038492</td>\n",
       "      <td>0.000487</td>\n",
       "      <td>0.010766</td>\n",
       "      <td>0.020472</td>\n",
       "      <td>0.002667</td>\n",
       "      <td>0.052217</td>\n",
       "      <td>0.003392</td>\n",
       "      <td>0.033634</td>\n",
       "      <td>0.036008</td>\n",
       "      <td>0.004066</td>\n",
       "      <td>0.014100</td>\n",
       "      <td>0.084241</td>\n",
       "      <td>0.015887</td>\n",
       "      <td>0.000318</td>\n",
       "      <td>0.025996</td>\n",
       "      <td>0.041410</td>\n",
       "      <td>0.010766</td>\n",
       "      <td>0.032706</td>\n",
       "      <td>0.033007</td>\n",
       "      <td>0.007304</td>\n",
       "      <td>0.122311</td>\n",
       "      <td>0.041287</td>\n",
       "      <td>0.125360</td>\n",
       "      <td>0.026164</td>\n",
       "      <td>0.126303</td>\n",
       "      <td>0.000990</td>\n",
       "      <td>0.003605</td>\n",
       "      <td>0.000582</td>\n",
       "      <td>0.009326</td>\n",
       "      <td>0.000465</td>\n",
       "      <td>0.048315</td>\n",
       "      <td>0.023302</td>\n",
       "      <td>0.006790</td>\n",
       "      <td>0.003380</td>\n",
       "      <td>0.005460</td>\n",
       "      <td>0.015263</td>\n",
       "      <td>0.025996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.001115</td>\n",
       "      <td>0.002670</td>\n",
       "      <td>0.011018</td>\n",
       "      <td>0.014434</td>\n",
       "      <td>0.010319</td>\n",
       "      <td>0.006002</td>\n",
       "      <td>0.017151</td>\n",
       "      <td>0.003726</td>\n",
       "      <td>0.001404</td>\n",
       "      <td>0.005645</td>\n",
       "      <td>0.019896</td>\n",
       "      <td>0.006029</td>\n",
       "      <td>0.048403</td>\n",
       "      <td>0.002085</td>\n",
       "      <td>0.000791</td>\n",
       "      <td>0.002448</td>\n",
       "      <td>0.013900</td>\n",
       "      <td>0.002457</td>\n",
       "      <td>0.003344</td>\n",
       "      <td>0.016090</td>\n",
       "      <td>0.007060</td>\n",
       "      <td>0.000141</td>\n",
       "      <td>0.001070</td>\n",
       "      <td>0.069563</td>\n",
       "      <td>0.009995</td>\n",
       "      <td>0.000240</td>\n",
       "      <td>0.007394</td>\n",
       "      <td>0.002883</td>\n",
       "      <td>0.011199</td>\n",
       "      <td>0.015970</td>\n",
       "      <td>0.000796</td>\n",
       "      <td>0.000696</td>\n",
       "      <td>0.001133</td>\n",
       "      <td>0.000629</td>\n",
       "      <td>0.000742</td>\n",
       "      <td>0.000680</td>\n",
       "      <td>0.002137</td>\n",
       "      <td>0.000589</td>\n",
       "      <td>0.001698</td>\n",
       "      <td>0.002542</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004272</td>\n",
       "      <td>0.031875</td>\n",
       "      <td>0.042673</td>\n",
       "      <td>0.018370</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>0.006931</td>\n",
       "      <td>0.026694</td>\n",
       "      <td>0.007000</td>\n",
       "      <td>0.008009</td>\n",
       "      <td>0.007756</td>\n",
       "      <td>0.030486</td>\n",
       "      <td>0.039650</td>\n",
       "      <td>0.027703</td>\n",
       "      <td>0.074936</td>\n",
       "      <td>0.089344</td>\n",
       "      <td>0.021118</td>\n",
       "      <td>0.000063</td>\n",
       "      <td>0.012770</td>\n",
       "      <td>0.015358</td>\n",
       "      <td>0.006931</td>\n",
       "      <td>0.019192</td>\n",
       "      <td>0.043584</td>\n",
       "      <td>0.001421</td>\n",
       "      <td>0.052971</td>\n",
       "      <td>0.046688</td>\n",
       "      <td>0.024328</td>\n",
       "      <td>0.015354</td>\n",
       "      <td>0.025700</td>\n",
       "      <td>0.003678</td>\n",
       "      <td>0.007998</td>\n",
       "      <td>0.000207</td>\n",
       "      <td>0.023761</td>\n",
       "      <td>0.000747</td>\n",
       "      <td>0.019347</td>\n",
       "      <td>0.012749</td>\n",
       "      <td>0.001026</td>\n",
       "      <td>0.001364</td>\n",
       "      <td>0.020580</td>\n",
       "      <td>0.011828</td>\n",
       "      <td>0.012770</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48190 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "prod_id  0594451647  0594481813  0970407998  ...  B00LGQ6HL8  B00LI4ZZO8  B00LKG1MC8\n",
       "0          0.005086    0.002178    0.003668  ...    0.174568    0.011367    0.012997\n",
       "1          0.002286    0.010898    0.000724  ...    0.024362    0.014765    0.038570\n",
       "2          0.001655    0.002675    0.007355  ...    0.083928    0.112205    0.005964\n",
       "3          0.001856    0.011019    0.005910  ...    0.005460    0.015263    0.025996\n",
       "4          0.001115    0.002670    0.011018  ...    0.020580    0.011828    0.012770\n",
       "\n",
       "[5 rows x 48190 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_user_predicted_ratings = np.dot(np.dot(U, sigma), Vt) \n",
    "\n",
    "# Predicted ratings\n",
    "preds_df = pd.DataFrame(abs(all_user_predicted_ratings), columns = final_ratings_matrix.columns)\n",
    "preds_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AZQCleRa-rfq"
   },
   "source": [
    "We have the prediction of ratings but we need to create a **function to recommend products** to the users on the basis of predicted ratings for each product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "eRUGWT3s5JtT"
   },
   "outputs": [],
   "source": [
    "# Recommend the items with the highest predicted ratings\n",
    "\n",
    "def recommend_items(user_index, interactions_matrix, preds_df, num_recommendations):\n",
    "    \n",
    "    # Get and sort the user's ratings from the actual and predicted interaction matrix\n",
    "    sorted_user_ratings = interactions_matrix.loc[user_index].sort_values(ascending=False)\n",
    "    sorted_user_predictions = preds_df.loc[user_index].sort_values(ascending=False)\n",
    "\n",
    "    #Creating a dataframe with actual and predicted ratings columns\n",
    "    temp = pd.concat([sorted_user_ratings, sorted_user_predictions], axis=1)\n",
    "    temp.index.name = 'Recommended Products'\n",
    "    temp.columns = ['user_ratings', 'user_predictions']\n",
    "    \n",
    "    #Filtering the dataframe where actual ratings are 0 which implies that the user has not interacted with that product\n",
    "    temp = temp.loc[temp.user_ratings == 0]  \n",
    "    \n",
    "    #Recommending products with top predicted ratings\n",
    "    temp = temp.sort_values('user_predictions', ascending=False) #Sort the dataframe by user_predictions in descending order\n",
    "    print('\\nBelow are the recommended products for user(user_id = {}):\\n'.format(user_index))\n",
    "    print(temp['user_predictions'].head(num_recommendations))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bp0XlTzDxhXw"
   },
   "source": [
    "**Recommending top 5 products to user id 121**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mrWWbjMh5JtU",
    "outputId": "5425fad2-7f23-49c7-da9b-489f1fc03fcd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Below are the recommended products for user(user_id = 121):\n",
      "\n",
      "Recommended Products\n",
      "B004CLYEDC    2.414390\n",
      "B0088CJT4U    1.521306\n",
      "B009NHWVIA    1.309224\n",
      "B008ULPAT2    1.200111\n",
      "B005I6EU48    1.126866\n",
      "Name: user_predictions, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Enter 'user index' and 'num_recommendations' for the user\n",
    "user_index = 121\n",
    "num_recommendations = 5\n",
    "recommend_items(user_index, final_ratings_matrix, preds_df, num_recommendations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_l0U1j2DxhXx"
   },
   "source": [
    "**Recommending the 5 products to user index 465**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DA3I_PG-xhXx",
    "outputId": "22c725f6-96cb-4bbb-86f6-02a729733635"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Below are the recommended products for user(user_id = 465):\n",
      "\n",
      "Recommended Products\n",
      "B008DWCRQW    1.038076\n",
      "B0079UAT0A    0.809093\n",
      "B008X9ZBVI    0.801342\n",
      "B00829TIA4    0.747477\n",
      "B008X9Z528    0.702148\n",
      "Name: user_predictions, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Enter 'user_index' and 'num_recommendations' for the user \n",
    "user_index = 465\n",
    "num_recommendations = 5\n",
    "recommend_items(user_index, final_ratings_matrix, preds_df, num_recommendations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XGW50Tzn5JtU"
   },
   "source": [
    "### Evaluate the model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5juRY83l5JtU"
   },
   "source": [
    "#### Evaluation of the Model based Collaborative Filtering (SVD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 278
    },
    "id": "1K4aEV7n5JtU",
    "outputId": "e22879be-ed8d-4322-ed39-99672ed3fa0c"
   },
   "outputs": [
    {
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       "      <th>B00KWHMR6G</th>\n",
       "      <th>B00KWL62QI</th>\n",
       "      <th>B00KWMNDDM</th>\n",
       "      <th>B00KWPRSJY</th>\n",
       "      <th>B00KWWY1PQ</th>\n",
       "      <th>B00KXAFYZS</th>\n",
       "      <th>B00KXMN08E</th>\n",
       "      <th>B00KXUHX9S</th>\n",
       "      <th>B00KYA7CT8</th>\n",
       "      <th>B00KYMCJF8</th>\n",
       "      <th>B00L0YFJHY</th>\n",
       "      <th>B00L1I727Y</th>\n",
       "      <th>B00L1I80K2</th>\n",
       "      <th>B00L1NZTSS</th>\n",
       "      <th>B00L21HC7A</th>\n",
       "      <th>B00L2442H0</th>\n",
       "      <th>B00L26YDA4</th>\n",
       "      <th>B00L2P3TRS</th>\n",
       "      <th>B00L3YHF6O</th>\n",
       "      <th>B00L403O94</th>\n",
       "      <th>B00L43HAY6</th>\n",
       "      <th>B00L5YZCCG</th>\n",
       "      <th>B00L8I6SFY</th>\n",
       "      <th>B00L8QCVL6</th>\n",
       "      <th>B00LA6T0LS</th>\n",
       "      <th>B00LBZ1Z7K</th>\n",
       "      <th>B00LED02VY</th>\n",
       "      <th>B00LGN7Y3G</th>\n",
       "      <th>B00LGQ6HL8</th>\n",
       "      <th>B00LI4ZZO8</th>\n",
       "      <th>B00LKG1MC8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_index</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",
       "      <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",
       "      <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",
       "      <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>0</th>\n",
       "      <td>0.0</td>\n",
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       "      <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",
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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>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>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>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>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",
       "    </tr>\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>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>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>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",
       "      <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>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>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>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>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>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>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",
       "      <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>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>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>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>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>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>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",
       "      <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>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>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>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>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>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>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",
       "      <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>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>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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       "      <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>5 rows × 48190 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "prod_id     0594451647  0594481813  ...  B00LI4ZZO8  B00LKG1MC8\n",
       "user_index                          ...                        \n",
       "0                  0.0         0.0  ...         0.0         0.0\n",
       "1                  0.0         0.0  ...         0.0         0.0\n",
       "2                  0.0         0.0  ...         0.0         0.0\n",
       "3                  0.0         0.0  ...         0.0         0.0\n",
       "4                  0.0         0.0  ...         0.0         0.0\n",
       "\n",
       "[5 rows x 48190 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Actual ratings given by the users\n",
    "final_ratings_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3ERqRpsa5JtU",
    "outputId": "b7415bf0-4670-4a01-d922-e2ecdca71699"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "prod_id\n",
       "0594451647    0.003247\n",
       "0594481813    0.001948\n",
       "0970407998    0.003247\n",
       "0972683275    0.012338\n",
       "1400501466    0.012987\n",
       "dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find average actual rating for each item\n",
    "final_ratings_matrix.mean().head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "foZqWixy5JtU",
    "outputId": "5c7eccfb-4629-4fda-dd39-3e8ffac88855"
   },
   "outputs": [
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       "      <td>0.007264</td>\n",
       "      <td>0.005152</td>\n",
       "      <td>0.003986</td>\n",
       "      <td>0.003480</td>\n",
       "      <td>0.006961</td>\n",
       "      <td>0.006606</td>\n",
       "      <td>0.002719</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001708</td>\n",
       "      <td>0.051040</td>\n",
       "      <td>0.000325</td>\n",
       "      <td>0.054867</td>\n",
       "      <td>0.017870</td>\n",
       "      <td>0.004996</td>\n",
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       "      <td>0.083928</td>\n",
       "      <td>0.112205</td>\n",
       "      <td>0.005964</td>\n",
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       "      <td>0.011019</td>\n",
       "      <td>0.005910</td>\n",
       "      <td>0.014134</td>\n",
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       "      <td>0.004968</td>\n",
       "      <td>0.001402</td>\n",
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       "      <td>0.000582</td>\n",
       "      <td>0.009326</td>\n",
       "      <td>0.000465</td>\n",
       "      <td>0.048315</td>\n",
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       "      <td>0.005460</td>\n",
       "      <td>0.015263</td>\n",
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       "      <td>0.012770</td>\n",
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       "prod_id  0594451647  0594481813  0970407998  0972683275  1400501466  \\\n",
       "0          0.005086    0.002178    0.003668    0.040843    0.009640   \n",
       "1          0.002286    0.010898    0.000724    0.130259    0.007506   \n",
       "2          0.001655    0.002675    0.007355    0.007264    0.005152   \n",
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       "4          0.001115    0.002670    0.011018    0.014434    0.010319   \n",
       "\n",
       "prod_id  1400501520  1400501776  1400532620  1400532655  140053271X  ...  \\\n",
       "0          0.006808    0.020659    0.000649    0.020331    0.005633  ...   \n",
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       "2          0.003986    0.003480    0.006961    0.006606    0.002719  ...   \n",
       "3          0.001877    0.005391    0.001709    0.004968    0.001402  ...   \n",
       "4          0.006002    0.017151    0.003726    0.001404    0.005645  ...   \n",
       "\n",
       "prod_id  B00L5YZCCG  B00L8I6SFY  B00L8QCVL6  B00LA6T0LS  B00LBZ1Z7K  \\\n",
       "0          0.000238    0.061477    0.001214    0.123433    0.028490   \n",
       "1          0.000038    0.013766    0.001473    0.025588    0.042103   \n",
       "2          0.001708    0.051040    0.000325    0.054867    0.017870   \n",
       "3          0.000582    0.009326    0.000465    0.048315    0.023302   \n",
       "4          0.000207    0.023761    0.000747    0.019347    0.012749   \n",
       "\n",
       "prod_id  B00LED02VY  B00LGN7Y3G  B00LGQ6HL8  B00LI4ZZO8  B00LKG1MC8  \n",
       "0          0.016109    0.002855    0.174568    0.011367    0.012997  \n",
       "1          0.004251    0.002177    0.024362    0.014765    0.038570  \n",
       "2          0.004996    0.002426    0.083928    0.112205    0.005964  \n",
       "3          0.006790    0.003380    0.005460    0.015263    0.025996  \n",
       "4          0.001026    0.001364    0.020580    0.011828    0.012770  \n",
       "\n",
       "[5 rows x 48190 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicted ratings \n",
    "preds_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FfxUk7uA5JtU",
    "outputId": "b1110da5-6064-45fa-e3a9-4d5268ff110f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "prod_id\n",
       "0594451647    0.003360\n",
       "0594481813    0.005729\n",
       "0970407998    0.008566\n",
       "0972683275    0.035330\n",
       "1400501466    0.006966\n",
       "dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find average predicted rating for each item\n",
    "preds_df.mean().head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 230
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    "id": "gxBreF6x5JtU",
    "outputId": "0e2db2e7-97a7-4906-ae2a-e7642c6b014d"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Avg_actual_ratings</th>\n",
       "      <th>Avg_predicted_ratings</th>\n",
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      "text/plain": [
       "            Avg_actual_ratings  Avg_predicted_ratings\n",
       "prod_id                                              \n",
       "0594451647            0.003247               0.003360\n",
       "0594481813            0.001948               0.005729\n",
       "0970407998            0.003247               0.008566\n",
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      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create a dataframe containing average actual ratings and avearge predicted ratings for each product\n",
    "rmse_df = pd.concat([final_ratings_matrix.mean(), preds_df.mean()], axis=1)\n",
    "\n",
    "rmse_df.columns = ['Avg_actual_ratings', 'Avg_predicted_ratings']\n",
    "\n",
    "rmse_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NLrgGD2K5JtU",
    "outputId": "287b3bf7-b33c-412c-c2e2-19d78825d25f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "RMSE SVD Model = 0.013679389779858015 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Calculate and print RMSE using the mean_square_error function\n",
    "RMSE = mean_squared_error(rmse_df['Avg_actual_ratings'], rmse_df['Avg_predicted_ratings'], squared=False)\n",
    "print('\\nRMSE SVD Model = {} \\n'.format(RMSE))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZDopC20i-rf4"
   },
   "source": [
    "### Recommendation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PUtKv8vR5JtU"
   },
   "source": [
    "#### Recommend top 10 products to the user id 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rISMTcWg5JtU",
    "outputId": "fedc8b3f-e831-45bf-e2d9-ac3e05ebce34"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Below are the recommended products for user(user_id = 100):\n",
      "\n",
      "Recommended Products\n",
      "B000N99BBC    1.624746\n",
      "B001CHHVCU    1.132730\n",
      "B000JMJWV2    1.047888\n",
      "B002SZEOLG    0.955049\n",
      "B001TH7GSW    0.879705\n",
      "B009SYZ8OC    0.854430\n",
      "B00DIFIM36    0.816153\n",
      "B00BGGDVOO    0.803755\n",
      "B004CLYEDC    0.748799\n",
      "B0015DYMVO    0.748797\n",
      "Name: user_predictions, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Enter 'user_index' and 'num_recommendations' for the user #\n",
    "user_index = 100\n",
    "num_recommendations = 10\n",
    "recommend_items(user_index, final_ratings_matrix, preds_df, num_recommendations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f3M5tHUF5JtU"
   },
   "source": [
    "### Conclusion "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K9IPL6ue5JtU"
   },
   "source": [
    "**Conclusion: _______________**\n",
    "\n",
    "Jose Medina Observations\n",
    "\n",
    "> In this project we developed three approaches to recommendation systems\n",
    "  >   - Rank-based system using averaging\n",
    "  >   - similarity based collaborative filtering\n",
    "  >   - model based collaborating filtering using Singular Value Decomposition\n",
    "\n",
    "We demonstrated the ability to recommend products with respect to each approach.\n",
    "\n",
    "We also observed that the RMSE for Model Based Collaborative Filtering is 0.0137\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0W_drPIkb4nV"
   },
   "outputs": [],
   "source": []
  }
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