Product_Recommendation_Systems.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PK_wOPOGkEbI"
   },
   "source": [
    "# Recommendation Systems\n",
    "\n",
    "**Objective:**\n",
    "\n",
    "Build a recommendation system to recommend products to\n",
    "customers based on the their previous ratings for other\n",
    "products.\n",
    "\n",
    "**Steps followed:**\n",
    "\n",
    "\n",
    "\n",
    "1.   Install the required package\n",
    "2.   Load the required libraries\n",
    "3.   Load the dataset and assign the column names\n",
    "4.   Analyze the dataset\n",
    "5.   Take subset of the dataset\n",
    "6.   Build Popularity based model\n",
    "7.   Build Collaborative filtering model\n",
    "8.   Tune the parameters\n",
    "9.   Compare and conclude the best model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HAchj82rFHif"
   },
   "source": [
    "**Install the requried package for Recommendation system**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 252
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    "colab_type": "code",
    "id": "cYBCOR9adeIQ",
    "outputId": "901df10c-ca5f-4345-be06-21cd4a844441"
   },
   "outputs": [
    {
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     "text": [
      "Collecting scikit-surprise\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f5/da/b5700d96495fb4f092be497f02492768a3d96a3f4fa2ae7dea46d4081cfa/scikit-surprise-1.1.0.tar.gz (6.4MB)\n",
      "\u001b[K     |████████████████████████████████| 6.5MB 99kB/s \n",
      "\u001b[?25hRequirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-surprise) (0.14.0)\n",
      "Requirement already satisfied: numpy>=1.11.2 in /usr/local/lib/python3.6/dist-packages (from scikit-surprise) (1.17.4)\n",
      "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.6/dist-packages (from scikit-surprise) (1.3.2)\n",
      "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from scikit-surprise) (1.12.0)\n",
      "Building wheels for collected packages: scikit-surprise\n",
      "  Building wheel for scikit-surprise (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for scikit-surprise: filename=scikit_surprise-1.1.0-cp36-cp36m-linux_x86_64.whl size=1678230 sha256=76a6ffcf0ee9d672e71176976576d2d6e7a1e1c7abb4c3c9494fd9625fe6cd44\n",
      "  Stored in directory: /root/.cache/pip/wheels/cc/fa/8c/16c93fccce688ae1bde7d979ff102f7bee980d9cfeb8641bcf\n",
      "Successfully built scikit-surprise\n",
      "Installing collected packages: scikit-surprise\n",
      "Successfully installed scikit-surprise-1.1.0\n"
     ]
    }
   ],
   "source": [
    "!pip install scikit-surprise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uRRjOGBSFSqI"
   },
   "source": [
    "**Import all required libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hlsgwittUOir"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from sklearn import metrics\n",
    "from surprise import Reader\n",
    "from surprise import SVD\n",
    "from surprise import Dataset\n",
    "from surprise.model_selection import cross_validate\n",
    "from surprise.model_selection import train_test_split # # Import Surprise package's data splitting function\n",
    "from surprise import accuracy\n",
    "from surprise.model_selection import GridSearchCV\n",
    "from surprise import KNNBasic\n",
    "from surprise import KNNWithMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qjfx1C0GFZfw"
   },
   "source": [
    "**Mount the drive**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 121
    },
    "colab_type": "code",
    "id": "biaqKS2qUCFk",
    "outputId": "6c0ddd51-d411-448e-fdbf-8069b57f46e8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
      "\n",
      "Enter your authorization code:\n",
      "··········\n",
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_4CbOIT4FdG9"
   },
   "source": [
    "**Assign the column names and load the dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 935
    },
    "colab_type": "code",
    "id": "GxlUrVT0URg7",
    "outputId": "0538247b-9286-4575-dae4-364eac18a247"
   },
   "outputs": [
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     },
     "execution_count": 5,
     "metadata": {
      "tags": []
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    }
   ],
   "source": [
    "\n",
    "colnames = [\"userId\", \"productId\", \"Rating\", \"timestamp\"]\n",
    "df = pd.read_csv(\"/content/drive/My Drive/Colab Notebooks/ratings_Electronics.csv\", names= colnames)\n",
    "df.head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CL4AiPSOFk97"
   },
   "source": [
    "**Analyse the given data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 935
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    "colab_type": "code",
    "id": "F6ZN-wT6VfQV",
    "outputId": "90f24d76-b528-4745-859e-f005ff1ea90c"
   },
   "outputs": [
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       "      <td>5.0</td>\n",
       "      <td>1405036800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824456</th>\n",
       "      <td>A3FOXB6244Y5PC</td>\n",
       "      <td>B00LNUH5X6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1404864000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824457</th>\n",
       "      <td>AELQMX8YAEEYI</td>\n",
       "      <td>B00LOLBBQQ</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1405382400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824458</th>\n",
       "      <td>AG1NNE1V43RWC</td>\n",
       "      <td>B00LOLBBQQ</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1405468800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824459</th>\n",
       "      <td>A2QW4HHHC6XWH4</td>\n",
       "      <td>B00LPQRT34</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1405209600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824460</th>\n",
       "      <td>A2025SOZT6OE6D</td>\n",
       "      <td>B00LS5WBYE</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1405641600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824461</th>\n",
       "      <td>A2DOJYSPCLCQIT</td>\n",
       "      <td>B00LTAUTHE</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1405382400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824462</th>\n",
       "      <td>A1PB6OT0OOPKNQ</td>\n",
       "      <td>B00LXEC8CU</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1405641600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824463</th>\n",
       "      <td>A1MTPZKAYVSES3</td>\n",
       "      <td>BT008G3W52</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1355702400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824464</th>\n",
       "      <td>AS7WVL4JGN1FI</td>\n",
       "      <td>BT008SXQ4C</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1326067200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824465</th>\n",
       "      <td>A3GGII8ILEUI4P</td>\n",
       "      <td>BT008T2BGK</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1320883200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824466</th>\n",
       "      <td>ATK4Q037EM13F</td>\n",
       "      <td>BT008UKTMW</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1356134400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824467</th>\n",
       "      <td>AT10LK8R9MLHT</td>\n",
       "      <td>BT008UKTMW</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1293753600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824468</th>\n",
       "      <td>AKVMJCP7T93X1</td>\n",
       "      <td>BT008UKTMW</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1394928000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824469</th>\n",
       "      <td>A2H4BLBXDNSZ2O</td>\n",
       "      <td>BT008UKTMW</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1316563200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824470</th>\n",
       "      <td>A2DARIL2TWUOLS</td>\n",
       "      <td>BT008UKTMW</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1373328000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824471</th>\n",
       "      <td>A7ETK9SC1HAMP</td>\n",
       "      <td>BT008UKTMW</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1397088000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824472</th>\n",
       "      <td>A5FUA9HEV2O42</td>\n",
       "      <td>BT008UKTMW</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1405987200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824473</th>\n",
       "      <td>A84WRQR47OOKR</td>\n",
       "      <td>BT008UKTMW</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1401753600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824474</th>\n",
       "      <td>A180UGKKYCPDH0</td>\n",
       "      <td>BT008UKTMW</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1369353600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824475</th>\n",
       "      <td>A2R6Q6KJCYSVH7</td>\n",
       "      <td>BT008UKTMW</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1343520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824476</th>\n",
       "      <td>A2IGIIABRZ5LAB</td>\n",
       "      <td>BT008UKTMW</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1356739200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824477</th>\n",
       "      <td>A2YZI3C9MOHC0L</td>\n",
       "      <td>BT008UKTMW</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1396569600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824478</th>\n",
       "      <td>A322MDK0M89RHN</td>\n",
       "      <td>BT008UKTMW</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1313366400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824479</th>\n",
       "      <td>A1MH90R0ADMIK0</td>\n",
       "      <td>BT008UKTMW</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1404172800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824480</th>\n",
       "      <td>A10M2KEFPEQDHN</td>\n",
       "      <td>BT008UKTMW</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1297555200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824481</th>\n",
       "      <td>A2G81TMIOIDEQQ</td>\n",
       "      <td>BT008V9J9U</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1312675200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 userId   productId  Rating   timestamp\n",
       "7824452  A3AENU8Y9HLRAQ  B00LMOH8DU     5.0  1405209600\n",
       "7824453   ABX8TXRB2GSKO  B00LMOH8DU     5.0  1405468800\n",
       "7824454  A1QUWXCBPGCQMW  B00LMOH8DU     5.0  1405728000\n",
       "7824455  A37B1XSZUYNSNK  B00LMOH8DU     5.0  1405036800\n",
       "7824456  A3FOXB6244Y5PC  B00LNUH5X6     5.0  1404864000\n",
       "7824457   AELQMX8YAEEYI  B00LOLBBQQ     5.0  1405382400\n",
       "7824458   AG1NNE1V43RWC  B00LOLBBQQ     1.0  1405468800\n",
       "7824459  A2QW4HHHC6XWH4  B00LPQRT34     4.0  1405209600\n",
       "7824460  A2025SOZT6OE6D  B00LS5WBYE     5.0  1405641600\n",
       "7824461  A2DOJYSPCLCQIT  B00LTAUTHE     5.0  1405382400\n",
       "7824462  A1PB6OT0OOPKNQ  B00LXEC8CU     5.0  1405641600\n",
       "7824463  A1MTPZKAYVSES3  BT008G3W52     5.0  1355702400\n",
       "7824464   AS7WVL4JGN1FI  BT008SXQ4C     1.0  1326067200\n",
       "7824465  A3GGII8ILEUI4P  BT008T2BGK     5.0  1320883200\n",
       "7824466   ATK4Q037EM13F  BT008UKTMW     4.0  1356134400\n",
       "7824467   AT10LK8R9MLHT  BT008UKTMW     3.0  1293753600\n",
       "7824468   AKVMJCP7T93X1  BT008UKTMW     5.0  1394928000\n",
       "7824469  A2H4BLBXDNSZ2O  BT008UKTMW     4.0  1316563200\n",
       "7824470  A2DARIL2TWUOLS  BT008UKTMW     2.0  1373328000\n",
       "7824471   A7ETK9SC1HAMP  BT008UKTMW     5.0  1397088000\n",
       "7824472   A5FUA9HEV2O42  BT008UKTMW     5.0  1405987200\n",
       "7824473   A84WRQR47OOKR  BT008UKTMW     1.0  1401753600\n",
       "7824474  A180UGKKYCPDH0  BT008UKTMW     5.0  1369353600\n",
       "7824475  A2R6Q6KJCYSVH7  BT008UKTMW     3.0  1343520000\n",
       "7824476  A2IGIIABRZ5LAB  BT008UKTMW     5.0  1356739200\n",
       "7824477  A2YZI3C9MOHC0L  BT008UKTMW     5.0  1396569600\n",
       "7824478  A322MDK0M89RHN  BT008UKTMW     5.0  1313366400\n",
       "7824479  A1MH90R0ADMIK0  BT008UKTMW     4.0  1404172800\n",
       "7824480  A10M2KEFPEQDHN  BT008UKTMW     4.0  1297555200\n",
       "7824481  A2G81TMIOIDEQQ  BT008V9J9U     5.0  1312675200"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "4whyCHMGVmN2",
    "outputId": "e2b6498e-4494-4d28-ad0b-30619c3a9cdd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7824482, 4)"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 168
    },
    "colab_type": "code",
    "id": "STJW6vCyVn8y",
    "outputId": "25c31be6-6cfd-490f-c840-09af432aac74"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7824482 entries, 0 to 7824481\n",
      "Data columns (total 4 columns):\n",
      "userId       object\n",
      "productId    object\n",
      "Rating       float64\n",
      "timestamp    int64\n",
      "dtypes: float64(1), int64(1), object(2)\n",
      "memory usage: 238.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 284
    },
    "colab_type": "code",
    "id": "-2j0g-ozVpnx",
    "outputId": "97d5bc9f-77f6-4a96-9126-cedb8fa3809d"
   },
   "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>Rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.824482e+06</td>\n",
       "      <td>7.824482e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.012337e+00</td>\n",
       "      <td>1.338178e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.380910e+00</td>\n",
       "      <td>6.900426e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>9.127296e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>1.315354e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>1.361059e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>1.386115e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>1.406074e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Rating     timestamp\n",
       "count  7.824482e+06  7.824482e+06\n",
       "mean   4.012337e+00  1.338178e+09\n",
       "std    1.380910e+00  6.900426e+07\n",
       "min    1.000000e+00  9.127296e+08\n",
       "25%    3.000000e+00  1.315354e+09\n",
       "50%    5.000000e+00  1.361059e+09\n",
       "75%    5.000000e+00  1.386115e+09\n",
       "max    5.000000e+00  1.406074e+09"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "QD5udUOZVrT_",
    "outputId": "d411dda2-1987-4902-d12c-430c18694d36"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 892
    },
    "colab_type": "code",
    "id": "N9ZN3UpFVsx7",
    "outputId": "c38c555f-2a08-4181-fb35-ba6651f3293e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CJwAAAAC6EzoBAAAA0J3QCQAAAIDuhE4AAAAAdCd0AgAAAKA7oRMA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\nAAAA6E7oBAAAAEB3/wfKfz+kPWEa4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize=(20, 15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "colab_type": "code",
    "id": "TjdDS2tlVuO4",
    "outputId": "62cf1d8e-5144-44b3-94c7-e65675babc43"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f59bf3831d0>"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mlKTnupEVQZIXJUm3fEqX5fFR5ZGkVvX21lCSrwKnA8uSPAx8ClgCUFVXAecBH0qyF/gV\ncH5VVV95JEmD9VYEVfXuefZfwdT0UknSCI161pAkacQsAklqnEUgSY2zCCSpcRaBJDXOIpCkxlkE\nktQ4i0CSGmcRSFLjLAJJapxFIEmNswgkqXEWgSQ1ziKQpMZZBJLUOItAkhrXWxEk+UKSR5PcM8v+\nJLksydYkW5K8qq8skqTZ9XlG8EXgzXPsPxM4qXusBa7sMYskaRZ93qrytiSr5xhyLnBdd5/iO5Ic\nn2R5VW3rK5MOvcsvv5ytW7eOOsZh4Zn/DuvWrRtxksPDmjVr+OhHPzrqGFqAUV4jWAE8NG394W7b\nfpKsTTKRZGJycnIo4aQDtXTpUpYuXTrqGNIB6+2M4FCqqvXAeoDx8fEacRxN41980pFvlGcEjwAn\nTltf2W2TJA3RKItgA/C+bvbQqcBOrw9I0vD19tZQkq8CpwPLkjwMfApYAlBVVwEbgbOArcBu4AN9\nZZEkza7PWUPvnmd/AR/u6/UlSQvjJ4slqXEWgSQ1ziKQpMZZBJLUuExdsz1yJJkEfjbqHNIslgGP\njTqENMCLq2ps0I4jrgikw1mSiaoaH3UO6UD41pAkNc4ikKTGWQTSobV+1AGkA+U1AklqnGcEktQ4\ni0CSGmcRSFLjLAJJapxFIEmN+384/EL3LbcX3QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y='Rating',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 280
    },
    "colab_type": "code",
    "id": "EJ539DFEVyXA",
    "outputId": "c263daea-d489-4fe8-f0e9-b1728f7f36bb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f59a048c908>"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m7LPP7jmRNLwuZwS/AUxU1e1V9QDwSeC8w7Y5A/ji4PsvzTAurRhTswJnA1ppujyQuQ6Y\nfg3dbuBZh22zCzgfuBJ4MXB8ksdV1d0d5pI6sWnTJjZt2tR3DGnB+j5ZfDHw3CT/BTwX2AM8ePhG\nSTYnGU8yvm/fvlFnlKQjWpdFsAd4/LTlUwbrfq6qvl9V51fV04BLBut+fPiOqmp7VY1V1djatWs7\njCxJ7emyCL4GPDnJE5P8EvAy4LPTN0iyJslUhrcBOzrMI0maQWdFUFWHgDcCXwBuAz5VVd9IclmS\nFw422wh8K8m3gZMAb8eUpBFLVfWdYUGS7APu6DuHNIs1wF19h5Bm8ISqmvHY+oorAmk5SzJeVWN9\n55AWou+rhiRJPbMIJKlxFoG0tLb3HUBaKM8RSFLjnBFIUuMsAklqnEUgSY2zCCSpcRaBJDXu/wDu\nznQPhXvFnAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y='timestamp',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yJDCsiluFpoz"
   },
   "source": [
    "**Take subset of data by filtering for users who have given more than 50 users to avoid working with large dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 935
    },
    "colab_type": "code",
    "id": "yHbPIqJHWm-z",
    "outputId": "d03ce0d7-83a0-4819-c8b6-591de6471a84"
   },
   "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>userId</th>\n",
       "      <th>productId</th>\n",
       "      <th>Rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>A3BY5KCNQZXV5U</td>\n",
       "      <td>0594451647</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1390176000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>AT09WGFUM934H</td>\n",
       "      <td>0594481813</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1377907200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>A32HSNCNPRUMTR</td>\n",
       "      <td>0970407998</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1319673600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>A17HMM1M7T9PJ1</td>\n",
       "      <td>0970407998</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1281744000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>A3CLWR1UUZT6TG</td>\n",
       "      <td>0972683275</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1373587200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>A3TAS1AG6FMBQW</td>\n",
       "      <td>0972683275</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1353456000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>A2Y4H3PXB07WQI</td>\n",
       "      <td>0972683275</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1292457600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138</th>\n",
       "      <td>A25RTRAPQAJBDJ</td>\n",
       "      <td>0972683275</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1329523200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1310</th>\n",
       "      <td>A3LDPF5FMB782Z</td>\n",
       "      <td>1400501466</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1336003200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1322</th>\n",
       "      <td>A1A5KUIIIHFF4U</td>\n",
       "      <td>1400501466</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1332547200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>A2XIOXRRYX0KZY</td>\n",
       "      <td>1400501466</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1371686400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1451</th>\n",
       "      <td>AW3LX47IHPFRL</td>\n",
       "      <td>1400501466</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1339804800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>A1E3OB6QMBKRYZ</td>\n",
       "      <td>1400501466</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1350086400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1485</th>\n",
       "      <td>A19N3S7CBSU6O7</td>\n",
       "      <td>1400501466</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1322697600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1588</th>\n",
       "      <td>A3LDPF5FMB782Z</td>\n",
       "      <td>1400501520</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1362873600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1696</th>\n",
       "      <td>A3LDPF5FMB782Z</td>\n",
       "      <td>1400501776</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1334102400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1777</th>\n",
       "      <td>A18S2VGUH9SCV5</td>\n",
       "      <td>1400501776</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1340755200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>A1K4G5YJDJQI6Q</td>\n",
       "      <td>1400532620</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1306713600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>A1PPS91NLI7KEH</td>\n",
       "      <td>1400532620</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1279843200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2082</th>\n",
       "      <td>A2ZR3YTMEEIIZ4</td>\n",
       "      <td>1400532655</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1300233600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2150</th>\n",
       "      <td>A3CLWR1UUZT6TG</td>\n",
       "      <td>1400532655</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1291420800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2162</th>\n",
       "      <td>A5JLAU2ARJ0BO</td>\n",
       "      <td>1400532655</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1291334400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2228</th>\n",
       "      <td>A1P4XD7IORSEFN</td>\n",
       "      <td>1400532655</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1324166400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2363</th>\n",
       "      <td>A341HCMGNZCBIT</td>\n",
       "      <td>1400532655</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1302048000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2437</th>\n",
       "      <td>A3HPCRD9RX351S</td>\n",
       "      <td>1400532655</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1332028800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2502</th>\n",
       "      <td>A3LDPF5FMB782Z</td>\n",
       "      <td>140053271X</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1310515200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2741</th>\n",
       "      <td>A20UU20K7M9ZPF</td>\n",
       "      <td>140053271X</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1330732800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3007</th>\n",
       "      <td>A1DQHS7MOVYYYA</td>\n",
       "      <td>1400599997</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1279584000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3050</th>\n",
       "      <td>ALUNVOQRXOZIA</td>\n",
       "      <td>1400599997</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1271548800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3178</th>\n",
       "      <td>A19N3S7CBSU6O7</td>\n",
       "      <td>1400599997</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1262044800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              userId   productId  Rating   timestamp\n",
       "94    A3BY5KCNQZXV5U  0594451647     5.0  1390176000\n",
       "118    AT09WGFUM934H  0594481813     3.0  1377907200\n",
       "177   A32HSNCNPRUMTR  0970407998     1.0  1319673600\n",
       "178   A17HMM1M7T9PJ1  0970407998     4.0  1281744000\n",
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       "1456  A1E3OB6QMBKRYZ  1400501466     1.0  1350086400\n",
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       "1588  A3LDPF5FMB782Z  1400501520     5.0  1362873600\n",
       "1696  A3LDPF5FMB782Z  1400501776     5.0  1334102400\n",
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       "2502  A3LDPF5FMB782Z  140053271X     4.0  1310515200\n",
       "2741  A20UU20K7M9ZPF  140053271X     1.0  1330732800\n",
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       "3178  A19N3S7CBSU6O7  1400599997     5.0  1262044800"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts=df['userId'].value_counts()\n",
    "df1=df[df['userId'].isin(counts[counts>=50].index)]\n",
    "df1.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "VjnyHXyZWpNV",
    "outputId": "497e4326-9db0-46eb-f3c2-d0feefa7c8b6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(125871, 4)"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hf8iqEwBF4vn"
   },
   "source": [
    "# **Popularity based filtering based on Product's ratings**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
    },
    "colab_type": "code",
    "id": "s8YE_Ls2Wsg0",
    "outputId": "354804a8-9e35-4b2c-d8f7-0b0c7c975637"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rating</th>\n",
       "      <th>product_rating_counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>productId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0594451647</th>\n",
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       "      <td>1</td>\n",
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       "      <td>2</td>\n",
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       "      <td>6</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "              Rating  product_rating_counts\n",
       "productId                                  \n",
       "0594451647  5.000000                      1\n",
       "0594481813  3.000000                      1\n",
       "0970407998  2.500000                      2\n",
       "0972683275  4.750000                      4\n",
       "1400501466  3.333333                      6"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products_df = pd.DataFrame(df1.groupby('productId')['Rating'].mean())\n",
    "products_df['product_rating_counts'] = pd.DataFrame(df1.groupby('productId')['Rating'].count())\n",
    "products_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
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    "colab_type": "code",
    "id": "r_3mcoyTYUnp",
    "outputId": "4b85335f-1f33-4210-bab3-d1df600540dc"
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "            Rating  product_rating_counts\n",
       "productId                                \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"
      ]
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     "execution_count": 17,
     "metadata": {
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   "source": [
    "products_df.sort_values('Rating', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
    },
    "colab_type": "code",
    "id": "wAqvba1BXFkF",
    "outputId": "e4bb857d-1217-44cb-e440-7c34f2d5a4e4"
   },
   "outputs": [
    {
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       "              Rating  product_rating_counts\n",
       "productId                                  \n",
       "B0088CJT4U  4.218447                    206\n",
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    "products_df.sort_values('product_rating_counts', ascending=False).head()"
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  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Avv3zwrWGIE6"
   },
   "source": [
    "# **Popularity based filtering based on User's ratings**"
   ]
  },
  {
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   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
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    "colab_type": "code",
    "id": "CT8-Eem_Z4ea",
    "outputId": "098d96da-e4c5-4b0c-8da5-d997f970d1be"
   },
   "outputs": [
    {
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      "text/plain": [
       "                  Rating  user_rating_counts\n",
       "userId                                      \n",
       "A100UD67AHFODS  4.508621                 116\n",
       "A100WO06OQR8BQ  3.651515                 132\n",
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     "execution_count": 19,
     "metadata": {
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   "source": [
    "users_df = pd.DataFrame(df1.groupby('userId')['Rating'].mean())\n",
    "users_df['user_rating_counts'] = pd.DataFrame(df1.groupby('userId')['Rating'].count())\n",
    "users_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
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     "height": 225
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    "colab_type": "code",
    "id": "2YH4CN_I47Y5",
    "outputId": "5abed3f8-b556-43cf-c265-29ff3f1b5ddb"
   },
   "outputs": [
    {
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       "                Rating  user_rating_counts\n",
       "userId                                    \n",
       "A23WPWLYT3P1CZ     5.0                  57\n",
       "A2DKQQIZ793AV5     5.0                  75\n",
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    "users_df.sort_values('Rating', ascending=False).head()"
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  {
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   "execution_count": 21,
   "metadata": {
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    "id": "HKYzg98t46iP",
    "outputId": "9b361e6d-c174-4584-dfc7-3ae2fc25bd06"
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   "outputs": [
    {
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       "                  Rating  user_rating_counts\n",
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       "ADLVFFE4VBT8    4.283433                 501\n",
       "A3OXHLG6DIBRW8  4.451807                 498\n",
       "A6FIAB28IS79    4.183295                 431\n",
       "A680RUE1FDO8B   4.406404                 406"
      ]
     },
     "execution_count": 21,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_df.sort_values('user_rating_counts', ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nWbekVA2GR18"
   },
   "source": [
    "# **Collaborative based filtering and get the scores for different models**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 336
    },
    "colab_type": "code",
    "id": "5hJ3fXlhYpsh",
    "outputId": "42037cb7-0e6c-4354-9892-c107fbca4c5a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating RMSE, MAE of algorithm SVD on 5 split(s).\n",
      "\n",
      "                  Fold 1  Fold 2  Fold 3  Fold 4  Fold 5  Mean    Std     \n",
      "RMSE (testset)    0.9842  0.9897  0.9850  0.9721  0.9858  0.9834  0.0059  \n",
      "MAE (testset)     0.7265  0.7303  0.7289  0.7222  0.7285  0.7273  0.0028  \n",
      "Fit time          6.32    6.30    6.29    6.32    6.26    6.30    0.02    \n",
      "Test time         0.21    0.29    0.29    0.29    0.21    0.26    0.04    \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'fit_time': (6.320570230484009,\n",
       "  6.300092458724976,\n",
       "  6.292995452880859,\n",
       "  6.3234639167785645,\n",
       "  6.258005619049072),\n",
       " 'test_mae': array([0.7265179 , 0.73031273, 0.72889027, 0.72216649, 0.72847171]),\n",
       " 'test_rmse': array([0.98422141, 0.98974344, 0.98498668, 0.97209063, 0.98575215]),\n",
       " 'test_time': (0.21454262733459473,\n",
       "  0.2912914752960205,\n",
       "  0.292050838470459,\n",
       "  0.29150867462158203,\n",
       "  0.2101912498474121)}"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reader = Reader(rating_scale=(0.5,5))\n",
    "\n",
    "data = Dataset.load_from_df(df1[['userId', 'productId', 'Rating']], reader)\n",
    "\n",
    "# Use the famous SVD algorithm.\n",
    "svd = SVD()\n",
    "\n",
    "# Run 5-fold cross-validation and print results.\n",
    "\n",
    "cross_validate(svd, data, measures=['RMSE', 'MAE'], cv=5, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67
    },
    "colab_type": "code",
    "id": "lKEooXM4ZZee",
    "outputId": "2c244c12-abd1-44da-f2e1-16630978b655"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.9851\n",
      "MAE:  0.7308\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7307667331193174"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split the data into 70% / 30%\n",
    "trainset, testset = train_test_split(data, test_size=.30)\n",
    "\n",
    "# Run SVD algorithm\n",
    "svd=SVD()\n",
    "\n",
    "#Build the model\n",
    "svd.fit(trainset)\n",
    "predictions=svd.test(testset)\n",
    "\n",
    "# Compute errors\n",
    "\n",
    "accuracy.rmse(predictions)\n",
    "accuracy.mae(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "5b9Cr-G4614r",
    "outputId": "0c3471ee-6887-4420-8f3b-36fc851c5bae"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user: 150        item: 10000      r_ui = 4.00   est = 4.26   {'was_impossible': False}\n"
     ]
    }
   ],
   "source": [
    "#Define userid and productid in the string format as required\n",
    "\n",
    "uid=str(150)\n",
    "pid=str(10000)\n",
    "\n",
    "# Get predictions\n",
    "\n",
    "pred=svd.predict(uid, pid, r_ui=4, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "mAIfrUxlCvUy",
    "outputId": "f1482fca-d2ee-4250-f2c1-1a6accd24852"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'rmse': 0.9890760019452024, 'mae': 0.7470791113063835}\n",
      "{'rmse': {'n_epochs': 10, 'lr_all': 0.005, 'reg_all': 0.4}, 'mae': {'n_epochs': 10, 'lr_all': 0.005, 'reg_all': 0.4}}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "param_grid = {\n",
    "    \"n_epochs\": [5, 10],\n",
    "    \"lr_all\": [0.002, 0.005],\n",
    "    \"reg_all\": [0.4, 0.6]\n",
    "}\n",
    "gs = GridSearchCV(SVD, param_grid, measures=[\"rmse\", \"mae\"], cv=3)\n",
    "\n",
    "gs.fit(data)\n",
    "\n",
    "print(gs.best_score)\n",
    "print(gs.best_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 958
    },
    "colab_type": "code",
    "id": "GjVH1HEA8oyC",
    "outputId": "1c8ba114-83dd-4436-b8e2-97dbdfe48920"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "{'rmse': 1.0527855030203785, 'mae': 0.7995881565020767}\n",
      "{'rmse': {'min_k': 4, 'k': 20}, 'mae': {'min_k': 2, 'k': 10}}\n"
     ]
    }
   ],
   "source": [
    "# GridSearch with KNNbasic\n",
    "\n",
    "param_grid = {'min_k':[2,3,4], 'k':[5,10,20] }\n",
    "gs = GridSearchCV(KNNBasic, param_grid, measures=[\"rmse\", \"mae\"], cv=3)\n",
    "\n",
    "gs.fit(data)\n",
    "\n",
    "print(gs.best_score)\n",
    "print(gs.best_params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "14QDlbAPIrJU"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OKd8VzppJCPG"
   },
   "outputs": [],
   "source": []
  }
 ],
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