U2Ucollab (1).ipynb

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    "colab": {
      "name": "U2Ucollab.ipynb",
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      "machine_shape": "hm"
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      "name": "python3",
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        "id": "es9kiG-VqjFB",
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from sklearn.metrics import pairwise_distances\n",
        "from sklearn.model_selection import train_test_split\n",
        "import pickle"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y-NcFY_L2SBl",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "movies = pd.read_csv(\"/content/datanam.csv\",encoding=\"Latin1\")\n",
        "Ratings = pd.read_csv(\"/content/datarat.csv\")\n",
        "Tags = pd.read_csv(\"/content/datatag.csv\",encoding=\"Latin1\")\n",
        "Rating,Test=train_test_split(Ratings,test_size=0.2)\n",
        "\n",
        "Mean = Rating.groupby(by=\"customer_id\",as_index=False)['reviews.rating'].mean()\n",
        "Rating_avg = pd.merge(Rating,Mean,on='customer_id')\n",
        "Rating_avg['adg_rating']=Rating_avg['reviews.rating_x']-Rating_avg['reviews.rating_y']\n",
        "Rating_avg.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>product_id</th>\n",
              "      <th>reviews.rating_x</th>\n",
              "      <th>customer_id</th>\n",
              "      <th>reviews.rating_y</th>\n",
              "      <th>adg_rating</th>\n",
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            "text/plain": [
              "   product_id  reviews.rating_x  customer_id  reviews.rating_y  adg_rating\n",
              "0         927               5.0      4361811          4.166667    0.833333\n",
              "1       11234               5.0      4361811          4.166667    0.833333\n",
              "2         971               5.0      4361811          4.166667    0.833333\n",
              "3        1567               4.0      4361811          4.166667   -0.166667\n",
              "4       14224               5.0      4361811          4.166667    0.833333"
            ]
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        ""
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        "colab": {
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        }
      },
      "source": [
        "Rating_avg.head()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>product_id</th>\n",
              "      <th>reviews.rating_x</th>\n",
              "      <th>customer_id</th>\n",
              "      <th>reviews.rating_y</th>\n",
              "      <th>adg_rating</th>\n",
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              "      <td>4361811</td>\n",
              "      <td>4.166667</td>\n",
              "      <td>-0.166667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>14224</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4361811</td>\n",
              "      <td>4.166667</td>\n",
              "      <td>0.833333</td>\n",
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            "text/plain": [
              "   product_id  reviews.rating_x  customer_id  reviews.rating_y  adg_rating\n",
              "0         927               5.0      4361811          4.166667    0.833333\n",
              "1       11234               5.0      4361811          4.166667    0.833333\n",
              "2         971               5.0      4361811          4.166667    0.833333\n",
              "3        1567               4.0      4361811          4.166667   -0.166667\n",
              "4       14224               5.0      4361811          4.166667    0.833333"
            ]
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        "colab": {
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      },
      "source": [
        "Rating.head()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th>reviews.rating</th>\n",
              "      <th>customer_id</th>\n",
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            "text/plain": [
              "      product_id  reviews.rating  customer_id\n",
              "1157         927             5.0      4361811\n",
              "3452        7182             5.0     14783707\n",
              "5890       14224             5.0      6301648\n",
              "3630       15268             5.0     30646298\n",
              "5744       14224             5.0     47454014"
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      "source": [
        "\n",
        "\n"
      ],
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      "outputs": []
    },
    {
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        "colab": {
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          "height": 255
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      },
      "source": [
        "check = pd.pivot_table(Rating_avg,values='reviews.rating_x',index='customer_id',columns='product_id')\n",
        "check.head()"
      ],
      "execution_count": 0,
      "outputs": [
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            ],
            "text/plain": [
              "product_id   12      60      102     162     ...  61723   71521   78129   517162\n",
              "customer_id                                  ...                                \n",
              "97309           NaN     NaN     NaN     NaN  ...     NaN     NaN     NaN     NaN\n",
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              "\n",
              "[5 rows x 47 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vET3nSFe4BYv",
        "colab_type": "code",
        "outputId": "7fe18e46-e7da-4926-bf1e-8224387d111c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "final = pd.pivot_table(Rating_avg,values='adg_rating',index='customer_id',columns='product_id')\n",
        "final.head()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            ],
            "text/plain": [
              "product_id   12      60      102     162     ...  61723   71521   78129   517162\n",
              "customer_id                                  ...                                \n",
              "97309           NaN     NaN     NaN     NaN  ...     NaN     NaN     NaN     NaN\n",
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              "\n",
              "[5 rows x 47 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vWcUp-5tTBi4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jKSFZTOhQIRC",
        "colab_type": "code",
        "outputId": "071c3649-867d-48bc-e1ce-56223f6815f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "final_movie = final.fillna(final.mean(axis=0))\n",
        "\n",
        "# Replacing NaN by user Average\n",
        "final_user = final.apply(lambda row: row.fillna(row.mean()), axis=1)\n",
        "final_user.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th>product_id</th>\n",
              "      <th>12</th>\n",
              "      <th>60</th>\n",
              "      <th>102</th>\n",
              "      <th>162</th>\n",
              "      <th>432</th>\n",
              "      <th>517</th>\n",
              "      <th>581</th>\n",
              "      <th>815</th>\n",
              "      <th>825</th>\n",
              "      <th>927</th>\n",
              "      <th>971</th>\n",
              "      <th>1142</th>\n",
              "      <th>1324</th>\n",
              "      <th>1362</th>\n",
              "      <th>1527</th>\n",
              "      <th>1543</th>\n",
              "      <th>1567</th>\n",
              "      <th>2517</th>\n",
              "      <th>5617</th>\n",
              "      <th>6123</th>\n",
              "      <th>6178</th>\n",
              "      <th>6179</th>\n",
              "      <th>6182</th>\n",
              "      <th>6192</th>\n",
              "      <th>6271</th>\n",
              "      <th>7182</th>\n",
              "      <th>7241</th>\n",
              "      <th>7343</th>\n",
              "      <th>7519</th>\n",
              "      <th>9172</th>\n",
              "      <th>9713</th>\n",
              "      <th>11234</th>\n",
              "      <th>14224</th>\n",
              "      <th>14251</th>\n",
              "      <th>15123</th>\n",
              "      <th>15268</th>\n",
              "      <th>16273</th>\n",
              "      <th>16892</th>\n",
              "      <th>17231</th>\n",
              "      <th>27182</th>\n",
              "      <th>27881</th>\n",
              "      <th>51681</th>\n",
              "      <th>52762</th>\n",
              "      <th>61723</th>\n",
              "      <th>71521</th>\n",
              "      <th>78129</th>\n",
              "      <th>517162</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>customer_id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <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>97309</th>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>4.000000e-01</td>\n",
              "      <td>-6.000000e-01</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>4.000000e-01</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>-6.000000e-01</td>\n",
              "      <td>4.000000e-01</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "      <td>3.552714e-16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110441</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111930</th>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>3.750000e-01</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>-6.250000e-01</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>3.750000e-01</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>3.750000e-01</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>3.750000e-01</td>\n",
              "      <td>-6.250000e-01</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>3.750000e-01</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "      <td>8.928571e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111932</th>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>1.666667e-01</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>-8.333333e-01</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>1.666667e-01</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>1.666667e-01</td>\n",
              "      <td>1.666667e-01</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>1.666667e-01</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "      <td>2.960595e-16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115345</th>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-7.500000e-01</td>\n",
              "      <td>2.500000e-01</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>1.250000e+00</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>2.500000e-01</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>1.250000e+00</td>\n",
              "      <td>2.500000e-01</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-2.750000e+00</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "      <td>-3.571429e-02</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "product_id         12            60      ...        78129         517162\n",
              "customer_id                              ...                            \n",
              "97309        3.552714e-16  3.552714e-16  ...  3.552714e-16  3.552714e-16\n",
              "110441       0.000000e+00  0.000000e+00  ...  0.000000e+00  0.000000e+00\n",
              "111930       8.928571e-02  8.928571e-02  ...  8.928571e-02  8.928571e-02\n",
              "111932       2.960595e-16  2.960595e-16  ...  2.960595e-16  2.960595e-16\n",
              "115345      -3.571429e-02 -3.571429e-02  ... -3.571429e-02 -3.571429e-02\n",
              "\n",
              "[5 rows x 47 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LlBMsMdHQnDS",
        "colab_type": "code",
        "outputId": "b7a1b868-1d71-42a3-cf17-f8a5b3f93c58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "final_movie.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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",
              "      <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>97309</th>\n",
              "      <td>0.298232</td>\n",
              "      <td>0.350232</td>\n",
              "      <td>0.188107</td>\n",
              "      <td>0.026701</td>\n",
              "      <td>0.221561</td>\n",
              "      <td>0.328351</td>\n",
              "      <td>0.079951</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>0.41958</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>-0.600000</td>\n",
              "      <td>-0.049542</td>\n",
              "      <td>0.209716</td>\n",
              "      <td>-0.620714</td>\n",
              "      <td>0.229823</td>\n",
              "      <td>-0.18617</td>\n",
              "      <td>0.379228</td>\n",
              "      <td>0.157534</td>\n",
              "      <td>0.276042</td>\n",
              "      <td>0.137972</td>\n",
              "      <td>0.077908</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.058447</td>\n",
              "      <td>-0.120068</td>\n",
              "      <td>0.095238</td>\n",
              "      <td>0.101566</td>\n",
              "      <td>0.226935</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.22763</td>\n",
              "      <td>-0.102826</td>\n",
              "      <td>-0.600000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>-0.435026</td>\n",
              "      <td>-0.34127</td>\n",
              "      <td>0.264286</td>\n",
              "      <td>0.443353</td>\n",
              "      <td>0.522619</td>\n",
              "      <td>0.344444</td>\n",
              "      <td>-0.051735</td>\n",
              "      <td>0.357143</td>\n",
              "      <td>-0.330495</td>\n",
              "      <td>0.119038</td>\n",
              "      <td>0.106766</td>\n",
              "      <td>-0.023246</td>\n",
              "      <td>0.044547</td>\n",
              "      <td>0.571429</td>\n",
              "      <td>0.017619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110441</th>\n",
              "      <td>0.298232</td>\n",
              "      <td>0.350232</td>\n",
              "      <td>0.188107</td>\n",
              "      <td>0.026701</td>\n",
              "      <td>0.221561</td>\n",
              "      <td>0.328351</td>\n",
              "      <td>0.079951</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>0.41958</td>\n",
              "      <td>-0.047553</td>\n",
              "      <td>0.272318</td>\n",
              "      <td>-0.049542</td>\n",
              "      <td>0.209716</td>\n",
              "      <td>-0.620714</td>\n",
              "      <td>0.229823</td>\n",
              "      <td>-0.18617</td>\n",
              "      <td>0.379228</td>\n",
              "      <td>0.157534</td>\n",
              "      <td>0.276042</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.077908</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.058447</td>\n",
              "      <td>-0.120068</td>\n",
              "      <td>0.095238</td>\n",
              "      <td>0.101566</td>\n",
              "      <td>0.226935</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.22763</td>\n",
              "      <td>-0.102826</td>\n",
              "      <td>0.032879</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.34127</td>\n",
              "      <td>0.264286</td>\n",
              "      <td>0.443353</td>\n",
              "      <td>0.522619</td>\n",
              "      <td>0.344444</td>\n",
              "      <td>-0.051735</td>\n",
              "      <td>0.357143</td>\n",
              "      <td>-0.330495</td>\n",
              "      <td>0.119038</td>\n",
              "      <td>0.106766</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.044547</td>\n",
              "      <td>0.571429</td>\n",
              "      <td>0.017619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111930</th>\n",
              "      <td>0.298232</td>\n",
              "      <td>0.350232</td>\n",
              "      <td>0.188107</td>\n",
              "      <td>0.026701</td>\n",
              "      <td>0.221561</td>\n",
              "      <td>0.328351</td>\n",
              "      <td>0.079951</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>0.41958</td>\n",
              "      <td>-0.047553</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>-0.049542</td>\n",
              "      <td>0.209716</td>\n",
              "      <td>-0.620714</td>\n",
              "      <td>0.229823</td>\n",
              "      <td>-0.18617</td>\n",
              "      <td>0.379228</td>\n",
              "      <td>0.157534</td>\n",
              "      <td>0.276042</td>\n",
              "      <td>-0.625000</td>\n",
              "      <td>0.077908</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.058447</td>\n",
              "      <td>-0.120068</td>\n",
              "      <td>0.095238</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>0.226935</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>0.22763</td>\n",
              "      <td>-0.102826</td>\n",
              "      <td>0.032879</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>-0.625000</td>\n",
              "      <td>-0.34127</td>\n",
              "      <td>0.264286</td>\n",
              "      <td>0.443353</td>\n",
              "      <td>0.522619</td>\n",
              "      <td>0.344444</td>\n",
              "      <td>-0.051735</td>\n",
              "      <td>0.357143</td>\n",
              "      <td>-0.330495</td>\n",
              "      <td>0.119038</td>\n",
              "      <td>0.106766</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>0.044547</td>\n",
              "      <td>0.571429</td>\n",
              "      <td>0.017619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111932</th>\n",
              "      <td>0.298232</td>\n",
              "      <td>0.350232</td>\n",
              "      <td>0.188107</td>\n",
              "      <td>0.026701</td>\n",
              "      <td>0.221561</td>\n",
              "      <td>0.328351</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>0.41958</td>\n",
              "      <td>-0.047553</td>\n",
              "      <td>0.272318</td>\n",
              "      <td>-0.049542</td>\n",
              "      <td>0.209716</td>\n",
              "      <td>-0.620714</td>\n",
              "      <td>0.229823</td>\n",
              "      <td>-0.18617</td>\n",
              "      <td>0.379228</td>\n",
              "      <td>0.157534</td>\n",
              "      <td>0.276042</td>\n",
              "      <td>0.137972</td>\n",
              "      <td>0.077908</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.058447</td>\n",
              "      <td>-0.120068</td>\n",
              "      <td>0.095238</td>\n",
              "      <td>0.101566</td>\n",
              "      <td>0.226935</td>\n",
              "      <td>-0.049029</td>\n",
              "      <td>0.22763</td>\n",
              "      <td>-0.102826</td>\n",
              "      <td>0.032879</td>\n",
              "      <td>0.259761</td>\n",
              "      <td>-0.833333</td>\n",
              "      <td>-0.34127</td>\n",
              "      <td>0.264286</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.522619</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.357143</td>\n",
              "      <td>-0.330495</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.106766</td>\n",
              "      <td>-0.023246</td>\n",
              "      <td>0.044547</td>\n",
              "      <td>0.571429</td>\n",
              "      <td>0.017619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115345</th>\n",
              "      <td>0.298232</td>\n",
              "      <td>0.350232</td>\n",
              "      <td>0.188107</td>\n",
              "      <td>0.026701</td>\n",
              "      <td>0.221561</td>\n",
              "      <td>0.328351</td>\n",
              "      <td>0.079951</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>0.41958</td>\n",
              "      <td>-0.750000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>-0.049542</td>\n",
              "      <td>0.209716</td>\n",
              "      <td>-0.620714</td>\n",
              "      <td>0.229823</td>\n",
              "      <td>-0.18617</td>\n",
              "      <td>0.379228</td>\n",
              "      <td>0.157534</td>\n",
              "      <td>0.276042</td>\n",
              "      <td>0.137972</td>\n",
              "      <td>0.077908</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.058447</td>\n",
              "      <td>-0.120068</td>\n",
              "      <td>0.095238</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>0.226935</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.22763</td>\n",
              "      <td>-0.102826</td>\n",
              "      <td>0.032879</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>-0.34127</td>\n",
              "      <td>0.264286</td>\n",
              "      <td>0.443353</td>\n",
              "      <td>0.522619</td>\n",
              "      <td>0.344444</td>\n",
              "      <td>-0.051735</td>\n",
              "      <td>0.357143</td>\n",
              "      <td>-0.330495</td>\n",
              "      <td>0.119038</td>\n",
              "      <td>0.106766</td>\n",
              "      <td>-2.750000</td>\n",
              "      <td>0.044547</td>\n",
              "      <td>0.571429</td>\n",
              "      <td>0.017619</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "product_id     12        60        102     ...    71521     78129     517162\n",
              "customer_id                                ...                              \n",
              "97309        0.298232  0.350232  0.188107  ...  0.044547  0.571429  0.017619\n",
              "110441       0.298232  0.350232  0.188107  ...  0.044547  0.571429  0.017619\n",
              "111930       0.298232  0.350232  0.188107  ...  0.044547  0.571429  0.017619\n",
              "111932       0.298232  0.350232  0.188107  ...  0.044547  0.571429  0.017619\n",
              "115345       0.298232  0.350232  0.188107  ...  0.044547  0.571429  0.017619\n",
              "\n",
              "[5 rows x 47 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v58Ru4nMivzL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "b = cosine_similarity(final_user)\n",
        "np.fill_diagonal(b, 0 )\n",
        "similarity_with_user = pd.DataFrame(b,index=final_user.index)\n",
        "similarity_with_user.columns=final_user.index\n",
        "similarity_with_user.head()\n",
        "filename = 'final_user.pickle'\n",
        "pickle.dump(b, open(filename, 'wb'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0dfSVUe6iwjb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cosine = cosine_similarity(final_movie)\n",
        "np.fill_diagonal(cosine, 0 )\n",
        "similarity_with_movie = pd.DataFrame(cosine,index=final_movie.index)\n",
        "similarity_with_movie.columns=final_user.index\n",
        "similarity_with_movie.head()\n",
        "filename = 'final_movie.pickle'\n",
        "pickle.dump(similarity_with_movie, open(filename, 'wb'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TSTBskmmi1EJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "def find_n_neighbours(df,n):\n",
        "    order = np.argsort(df.values, axis=1)[:, :n]\n",
        "    df = df.apply(lambda x: pd.Series(x.sort_values(ascending=False)\n",
        "           .iloc[:n].index, \n",
        "          index=['top{}'.format(i) for i in range(1, n+1)]), axis=1)\n",
        "    return df"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7VfDM5nKi5F-",
        "colab_type": "code",
        "outputId": "64ee6898-63c4-4287-eddb-159271c8bed5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "sim_user_30_u = find_n_neighbours(similarity_with_user,30)\n",
        "sim_user_30_u.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>top1</th>\n",
              "      <th>top2</th>\n",
              "      <th>top3</th>\n",
              "      <th>top4</th>\n",
              "      <th>top5</th>\n",
              "      <th>top6</th>\n",
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              "      <th>top8</th>\n",
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              "      <th>top11</th>\n",
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              "      <th>top13</th>\n",
              "      <th>top14</th>\n",
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              "      <th>top19</th>\n",
              "      <th>top20</th>\n",
              "      <th>top21</th>\n",
              "      <th>top22</th>\n",
              "      <th>top23</th>\n",
              "      <th>top24</th>\n",
              "      <th>top25</th>\n",
              "      <th>top26</th>\n",
              "      <th>top27</th>\n",
              "      <th>top28</th>\n",
              "      <th>top29</th>\n",
              "      <th>top30</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>customer_id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <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>97309</th>\n",
              "      <td>50772322</td>\n",
              "      <td>25659526</td>\n",
              "      <td>45286795</td>\n",
              "      <td>32038204</td>\n",
              "      <td>21332512</td>\n",
              "      <td>33582678</td>\n",
              "      <td>10876480</td>\n",
              "      <td>14970541</td>\n",
              "      <td>787281</td>\n",
              "      <td>530848</td>\n",
              "      <td>48148197</td>\n",
              "      <td>16189998</td>\n",
              "      <td>52779789</td>\n",
              "      <td>30841819</td>\n",
              "      <td>10504818</td>\n",
              "      <td>14579833</td>\n",
              "      <td>36816790</td>\n",
              "      <td>27053976</td>\n",
              "      <td>32467074</td>\n",
              "      <td>4361811</td>\n",
              "      <td>17559184</td>\n",
              "      <td>49398124</td>\n",
              "      <td>29182640</td>\n",
              "      <td>30059074</td>\n",
              "      <td>42391754</td>\n",
              "      <td>51746033</td>\n",
              "      <td>14908944</td>\n",
              "      <td>49242514</td>\n",
              "      <td>15176382</td>\n",
              "      <td>32133759</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110441</th>\n",
              "      <td>53076619</td>\n",
              "      <td>14019850</td>\n",
              "      <td>14414189</td>\n",
              "      <td>14400882</td>\n",
              "      <td>14338649</td>\n",
              "      <td>14324509</td>\n",
              "      <td>14315429</td>\n",
              "      <td>14250726</td>\n",
              "      <td>14225036</td>\n",
              "      <td>14173434</td>\n",
              "      <td>14135088</td>\n",
              "      <td>14054766</td>\n",
              "      <td>14045173</td>\n",
              "      <td>14028763</td>\n",
              "      <td>14007674</td>\n",
              "      <td>13425525</td>\n",
              "      <td>13940487</td>\n",
              "      <td>13895839</td>\n",
              "      <td>13894352</td>\n",
              "      <td>13804717</td>\n",
              "      <td>13659988</td>\n",
              "      <td>13647366</td>\n",
              "      <td>13585462</td>\n",
              "      <td>13577585</td>\n",
              "      <td>13534991</td>\n",
              "      <td>13534413</td>\n",
              "      <td>13532681</td>\n",
              "      <td>13529886</td>\n",
              "      <td>14435268</td>\n",
              "      <td>14458914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111930</th>\n",
              "      <td>27278599</td>\n",
              "      <td>18012236</td>\n",
              "      <td>14414189</td>\n",
              "      <td>14544335</td>\n",
              "      <td>1119536</td>\n",
              "      <td>42133558</td>\n",
              "      <td>2260642</td>\n",
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              "      <td>48959028</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 top1      top2      top3  ...     top28     top29     top30\n",
              "customer_id                                ...                              \n",
              "97309        50772322  25659526  45286795  ...  49242514  15176382  32133759\n",
              "110441       53076619  14019850  14414189  ...  13529886  14435268  14458914\n",
              "111930       27278599  18012236  14414189  ...  39406815  39532206  46917633\n",
              "111932        1079519  46827382  29671204  ...    196208  18653653  43773798\n",
              "115345       41659434   1578888   4888142  ...  30959841  21204337  48959028\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ILB4KN2Ci70s",
        "colab_type": "code",
        "outputId": "366886c5-da38-4948-f9e5-a5ea00957612",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "sim_user_30_m = find_n_neighbours(similarity_with_movie,30)\n",
        "sim_user_30_m.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 top1      top2      top3  ...     top28     top29     top30\n",
              "customer_id                                ...                              \n",
              "97309        45286795  50772322  14908944  ...  41861544  51168531  49242514\n",
              "110441       48424535  31678700  29340349  ...  38195398  50421178  38518519\n",
              "111930        1119536  46917633  42133558  ...  40085728  13895839  48805811\n",
              "111932        1079519  48805811   7822109  ...  44239442  14019850  25328707\n",
              "115345       41659434   4888142  39472629  ...  14783707  30987150   3698606\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bdcDL3najFsC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "def get_user_similar_movies( user1, user2 ):\n",
        "    common_movies = Rating_avg[Rating_avg.customer_id == user1].merge(\n",
        "    Rating_avg[Rating_avg.customer_id == user2],\n",
        "    on = \"product_id\",\n",
        "    how = \"inner\" )\n",
        "    return common_movies.merge( movies, on = 'product_id' )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WN11dPpzjGVi",
        "colab_type": "code",
        "outputId": "15c9c4eb-a5ca-4dfe-b846-d3a683d79d11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        }
      },
      "source": [
        "\n",
        "a = get_user_similar_movies(99730,99730)\n",
        "a = a.loc[ : , ['reviews.rating_x_x','reviews.rating_x_y','name']]\n",
        "\n",
        "a.head()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>reviews.rating_x_x</th>\n",
              "      <th>reviews.rating_x_y</th>\n",
              "      <th>name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [reviews.rating_x_x, reviews.rating_x_y, name]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CBx1V6zGrUWw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kFXKZFuajI1O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def User_item_score(user,item):\n",
        "    a = sim_user_30_m[sim_user_30_m.index==user].values\n",
        "    b = a.squeeze().tolist()\n",
        "    c = final_movie.loc[:,item]\n",
        "    d = c[c.index.isin(b)]\n",
        "    f = d[d.notnull()]\n",
        "    avg_user = Mean.loc[Mean['customer_id'] == user,'reviews.rating'].values[0]\n",
        "    index = f.index.values.squeeze().tolist()\n",
        "    corr = similarity_with_movie.loc[user,index]\n",
        "    fin = pd.concat([f, corr], axis=1)\n",
        "    fin.columns = ['adg_score','correlation']\n",
        "    fin['score']=fin.apply(lambda x:x['adg_score'] * x['correlation'],axis=1)\n",
        "    nume = fin['score'].sum()\n",
        "    deno = fin['correlation'].sum()\n",
        "    final_score = avg_user + (nume/deno)\n",
        "    return final_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QKDFTWGNl5cY",
        "colab_type": "code",
        "outputId": "bfa9c370-b7ca-40c8-f0c7-d2e4568a1949",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "score = User_item_score(45880701,14224)\n",
        "print(\"score (u,i) is\",score)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "score (u,i) is 3.101815541770479\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "COeNPwlbykiV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "Rating_avg = Rating_avg.astype({\"product_id\": str})\n",
        "Movie_user = Rating_avg.groupby(by = 'customer_id')['product_id'].apply(lambda x:','.join(x))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VRJ3RUeq0xF_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def User_item_score1(user):\n",
        "    Movie_seen_by_user = check.columns[check[check.index==user].notna().any()].tolist()\n",
        "    a = sim_user_30_m[sim_user_30_m.index==user].values\n",
        "    b = a.squeeze().tolist()\n",
        "    d = Movie_user[Movie_user.index.isin(b)]\n",
        "    l = ','.join(d.values)\n",
        "    Movie_seen_by_similar_users = l.split(',')\n",
        "    Movies_under_consideration = list(set(Movie_seen_by_similar_users)-set(list(map(str, Movie_seen_by_user))))\n",
        "    Movies_under_consideration = list(map(int, Movies_under_consideration))\n",
        "    score = []\n",
        "    for item in Movies_under_consideration:\n",
        "        c = final_movie.loc[:,item]\n",
        "        d = c[c.index.isin(b)]\n",
        "        f = d[d.notnull()]\n",
        "        avg_user = Mean.loc[Mean['customer_id'] == user,'reviews.rating'].values[0]\n",
        "        index = f.index.values.squeeze().tolist()\n",
        "        corr = similarity_with_movie.loc[user,index]\n",
        "        fin = pd.concat([f, corr], axis=1)\n",
        "        fin.columns = ['adg_score','correlation']\n",
        "        fin['score']=fin.apply(lambda x:x['adg_score'] * x['correlation'],axis=1)\n",
        "        nume = fin['score'].sum()\n",
        "        deno = fin['correlation'].sum()\n",
        "        final_score = avg_user + (nume/deno)\n",
        "        score.append(final_score)\n",
        "    data = pd.DataFrame({'product_id':Movies_under_consideration,'score':score})\n",
        "    top_5_recommendation = data.sort_values(by='score',ascending=False).head(10)\n",
        "    Movie_Name = top_5_recommendation.merge(movies, how='inner', on='product_id')\n",
        "    Movie_Names = Movie_Name.name.values.tolist()\n",
        "    return Movie_Names"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AKLOPJJa1iPL",
        "colab_type": "code",
        "outputId": "fcfe7de1-e93f-4507-be90-a268149957e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "user = int(input(\"Enter the user id to whom you want to recommend : \"))\n",
        "predicted_movies = User_item_score1(user)\n",
        "print(\" \")\n",
        "print(\"The Recommendations for User Id : 110441\")\n",
        "print(\"   \")\n",
        "for i in predicted_movies:\n",
        "    print(i)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Enter the user id to whom you want to recommend : 45880701\n",
            " \n",
            "The Recommendations for User Id : 110441\n",
            "   \n",
            "Microsoft Surface Pro 4 Type Cover with Fingerprint ID\n",
            "Midland Weather Alert Radio, 1.0 CT\n",
            "Sony SRSXB30/BLK XB30 Portable Wireless Speaker with Bluetooth\n",
            "Sony LBT-GPX555 Mini-System with Bluetooth and NFC\n",
            "Boytone - 2500W 2.1-Ch. Home Theater System - Black Diamond\n",
            "JBL - 6\" x 8\" 2-Way Coaxial Car Speakers with Polypropylene Cones (Pair) - Black\n",
            "Sony SRSHG1/BLK Hi-Res Wireless Speaker- Charcoal black\n",
            "Verizon MiFi 6620L Jetpack 4G LTE Mobile Hotspot (Verizon Wireless)\n",
            "CRX-322 CD Receiver\n",
            "Slingbox M2\n",
            "iHome Rechargeable Splash Proof Stereo Bluetooth Speaker - Black (IBT33BC)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nQZ7uKBB2ipf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "filename = 'item,score.sav'\n",
        "pickle.dump(User_item_score, open(filename, 'wb'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1WGU1yWr3xlO",
        "colab_type": "code",
        "outputId": "a87d61c3-d845-48e0-8b70-4a90d4988faf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "loaded_model = pickle.load(open(filename, 'rb'))\n",
        "result = loaded_model(45880701, 14224)\n",
        "print(result)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3.101815541770479\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_-E1cwzi4XR5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "filename = 'user.csv'\n",
        "pickle.dump(b, open(filename, 'wb'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dJ_e5S6f4n0E",
        "colab_type": "code",
        "outputId": "85225046-eaa4-4b1e-8ae8-9efba8771470",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "loaded_model = pickle.load(open(filename, 'rb'))\n",
        "result = loaded_model(45880701)\n",
        "print(result)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-85-3aa106d34f01>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mloaded_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloaded_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m45880701\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: 'DataFrame' object is not callable"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ieDsQvLi5b_r",
        "colab_type": "code",
        "outputId": "3a79ff2c-96df-4e18-ee08-6e8c785d96d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "from math import sqrt\n",
        "\n",
        "rms = sqrt(mean_squared_error(Test,final_movie))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-86-2134bb66a600>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmath\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msqrt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mrms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTest\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfinal_movie\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mmean_squared_error\u001b[0;34m(y_true, y_pred, sample_weight, multioutput, squared)\u001b[0m\n\u001b[1;32m    250\u001b[0m     \"\"\"\n\u001b[1;32m    251\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0;32m--> 252\u001b[0;31m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[1;32m    253\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m     output_errors = np.average((y_true - y_pred) ** 2, axis=0,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m     \"\"\"\n\u001b[0;32m---> 84\u001b[0;31m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     85\u001b[0m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m    210\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m         raise ValueError(\"Found input variables with inconsistent numbers of\"\n\u001b[0;32m--> 212\u001b[0;31m                          \" samples: %r\" % [int(l) for l in lengths])\n\u001b[0m\u001b[1;32m    213\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [1414, 938]"
          ]
        }
      ]
    }
  ]
}