ProductRecommendationSystem_FlipkartGrid.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9d244c81-0a2a-4adc-9f3c-c0f1c0c97d28",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.metrics.pairwise import cosine_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1fcf2004-d70e-46a7-97c6-5e8ae3797d43",
   "metadata": {},
   "outputs": [],
   "source": [
    "testset = pd.read_csv('Recommendation_dataset.csv',header= None)\n",
    "testset.columns = ['user_id', 'product_id', 'ratings', 'timestamp'] \n",
    "testset = testset.drop('timestamp', axis=1)\n",
    "testset_copy = testset.copy(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b2a0e673-7bb6-4ad0-97b5-0fab82c97dcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AKM1MP6P0OYPR</td>\n",
       "      <td>0132793040</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          user_id  product_id  ratings\n",
       "0   AKM1MP6P0OYPR  0132793040      5.0\n",
       "1  A2CX7LUOHB2NDG  0321732944      5.0\n",
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       "3  A2WNBOD3WNDNKT  0439886341      3.0\n",
       "4  A1GI0U4ZRJA8WN  0439886341      1.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "37a4e431-0ad9-4c48-81e2-0e4b08578790",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of rows in testset:  7824482\n",
      "No of columns in testset:  3\n"
     ]
    }
   ],
   "source": [
    "rows, columns = testset.shape[0], testset.shape[1]\n",
    "print(\"No of rows in testset: \", rows) \n",
    "print(\"No of columns in testset: \", columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f219efd8-3de7-4abf-9cae-b73af359acfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id        object\n",
       "product_id     object\n",
       "ratings       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testset.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7a45bbcd-45c9-43df-8799-409f344e6a90",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.824482e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.012337e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.380910e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ratings\n",
       "count  7.824482e+06\n",
       "mean   4.012337e+00\n",
       "std    1.380910e+00\n",
       "min    1.000000e+00\n",
       "25%    3.000000e+00\n",
       "50%    5.000000e+00\n",
       "75%    5.000000e+00\n",
       "max    5.000000e+00"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2be6912a-961f-4330-bf00-5327df7f6374",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id       0\n",
       "product_id    0\n",
       "ratings       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testset.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8d13559e-59cf-4885-aae2-846071a96761",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique users in dataset =  4201696\n",
      "Number of unique items in dataset =  476002\n"
     ]
    }
   ],
   "source": [
    "print('Number of unique users in dataset = ', testset['user_id'].nunique())\n",
    "print('Number of unique items in dataset = ', testset['product_id'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7caea3eb-8533-4c5a-afd0-7c8988c35c2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id\n",
       "A5JLAU2ARJ0BO     520\n",
       "ADLVFFE4VBT8      501\n",
       "A3OXHLG6DIBRW8    498\n",
       "A6FIAB28IS79      431\n",
       "A680RUE1FDO8B     406\n",
       "A1ODOGXEYECQQ8    380\n",
       "A36K2N527TXXJN    314\n",
       "A2AY4YUOX2N1BQ    311\n",
       "AWPODHOB4GFWL     308\n",
       "A25C2M3QF9G7OQ    296\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_rated = testset.groupby('user_id').size().sort_values(ascending=False)[:10]\n",
    "most_rated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "08cd8022-fadd-4038-8e22-eea070c46f79",
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = testset['user_id'].value_counts()\n",
    "testset_final = testset[testset['user_id'].isin(counts[counts >= 50].index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "12200d6c-bd72-4642-88c6-f53c8531c73e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of observations in the final data = 125871\n"
     ]
    }
   ],
   "source": [
    "print('The number of observations in the final data =', len(testset_final))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "633a6298-f3b7-4d11-98e2-0adf46fb8fd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_rating_matrix = testset_final.pivot(index = 'user_id', columns ='product_id', values = 'ratings').fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cfe6e833-48f4-4152-8325-a02fe8ee38ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of final_ratings_matrix:  (1540, 48190)\n"
     ]
    }
   ],
   "source": [
    "print('Shape of final_ratings_matrix: ', final_rating_matrix.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4f64ab98-80fb-4f15-80bb-0432345a5358",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "given_num_of_rating =  125871\n",
      "possible_num_of_rating =  74212600\n",
      "density: 0.17%\n"
     ]
    },
    {
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      "text/plain": [
       "product_id      0594451647  0594481813  0970407998  0972683275  1400501466  \\\n",
       "user_id                                                                      \n",
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       "\n",
       "product_id      1400501520  1400501776  1400532620  1400532655  140053271X  \\\n",
       "user_id                                                                      \n",
       "A100UD67AHFODS         0.0         0.0         0.0         0.0         0.0   \n",
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       "\n",
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       "user_id         ...                                                   \n",
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       "\n",
       "product_id      B00LBZ1Z7K  B00LED02VY  B00LGN7Y3G  B00LGQ6HL8  B00LI4ZZO8  \\\n",
       "user_id                                                                      \n",
       "A100UD67AHFODS         0.0         0.0         0.0         0.0         0.0   \n",
       "A100WO06OQR8BQ         0.0         0.0         0.0         0.0         0.0   \n",
       "A105S56ODHGJEK         0.0         0.0         0.0         0.0         0.0   \n",
       "A105TOJ6LTVMBG         0.0         0.0         0.0         0.0         0.0   \n",
       "A10AFVU66A79Y1         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "product_id      B00LKG1MC8  \n",
       "user_id                     \n",
       "A100UD67AHFODS         0.0  \n",
       "A100WO06OQR8BQ         0.0  \n",
       "A105S56ODHGJEK         0.0  \n",
       "A105TOJ6LTVMBG         0.0  \n",
       "A10AFVU66A79Y1         0.0  \n",
       "\n",
       "[5 rows x 48190 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "given_num_of_rating = np.count_nonzero(final_rating_matrix)\n",
    "print('given_num_of_rating = ', given_num_of_rating)\n",
    "possible_num_of_rating = final_rating_matrix.shape[0] * final_rating_matrix.shape[1]\n",
    "print('possible_num_of_rating = ', possible_num_of_rating)\n",
    "density = (given_num_of_rating/possible_num_of_rating)\n",
    "density *= 100\n",
    "print ('density: {:4.2f}%'.format(density))\n",
    "final_rating_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "04d74291-2656-43df-8a27-f1a332e0951b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             ratings\n",
      "product_id          \n",
      "0594451647  5.000000\n",
      "0594481813  3.000000\n",
      "0970407998  2.500000\n",
      "0972683275  4.750000\n",
      "1400501466  3.333333\n"
     ]
    }
   ],
   "source": [
    "average_rating = testset_final.groupby(['product_id']).mean('ratings')\n",
    "print(average_rating.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4c1b3014-6856-4849-bc9e-3fa4d9e157e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Rating Count</th>\n",
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      ],
      "text/plain": [
       "            Average Rating  Rating Count\n",
       "product_id                              \n",
       "0594451647             5.0             1\n",
       "B003RRY9RS             5.0             1\n",
       "B003RR95Q8             5.0             1\n",
       "B003RIPMZU             5.0             1\n",
       "B003RFRNYQ             5.0             2"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_rating = testset_final.groupby(['product_id']).count().ratings\n",
    "final_rating = pd.DataFrame(pd.concat([average_rating,count_rating], axis = 1))\n",
    "final_rating.columns=[\"Average Rating\", \"Rating Count\"]\n",
    "final_rating = final_rating.sort_values(by='Average Rating', ascending=False)\n",
    "final_rating.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "799c40a5-3402-40cc-b991-a4dd4822e107",
   "metadata": {},
   "outputs": [],
   "source": [
    "def top_n_product(final_rating, n, min_interaction):\n",
    "    recommendation = final_rating[final_rating['Rating Count'] >= min_interaction]    \n",
    "    recommendation = recommendation.sort_values(by='Average Rating', ascending=False)\n",
    "    return recommendation.index[:n]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a10175f8-fc99-4e08-accb-8769774798db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['B001TH7GUU', 'B003ES5ZUU', 'B0019EHU8G', 'B006W8U2MU', 'B000QUUFRW']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(top_n_product(final_rating, 5, 50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2cb6de62-7653-4e27-9098-11e83e473919",
   "metadata": {},
   "outputs": [
    {
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       "product_id  0594451647  0594481813  0970407998  0972683275  1400501466  \\\n",
       "user_index                                                               \n",
       "0                  0.0         0.0         0.0         0.0         0.0   \n",
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       "3                  0.0         0.0         0.0         0.0         0.0   \n",
       "4                  0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "product_id  1400501520  1400501776  1400532620  1400532655  140053271X  ...  \\\n",
       "user_index                                                              ...   \n",
       "0                  0.0         0.0         0.0         0.0         0.0  ...   \n",
       "1                  0.0         0.0         0.0         0.0         0.0  ...   \n",
       "2                  0.0         0.0         0.0         0.0         0.0  ...   \n",
       "3                  0.0         0.0         0.0         0.0         0.0  ...   \n",
       "4                  0.0         0.0         0.0         0.0         0.0  ...   \n",
       "\n",
       "product_id  B00L5YZCCG  B00L8I6SFY  B00L8QCVL6  B00LA6T0LS  B00LBZ1Z7K  \\\n",
       "user_index                                                               \n",
       "0                  0.0         0.0         0.0         0.0         0.0   \n",
       "1                  0.0         0.0         0.0         0.0         0.0   \n",
       "2                  0.0         0.0         0.0         0.0         0.0   \n",
       "3                  0.0         0.0         0.0         0.0         0.0   \n",
       "4                  0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "product_id  B00LED02VY  B00LGN7Y3G  B00LGQ6HL8  B00LI4ZZO8  B00LKG1MC8  \n",
       "user_index                                                              \n",
       "0                  0.0         0.0         0.0         0.0         0.0  \n",
       "1                  0.0         0.0         0.0         0.0         0.0  \n",
       "2                  0.0         0.0         0.0         0.0         0.0  \n",
       "3                  0.0         0.0         0.0         0.0         0.0  \n",
       "4                  0.0         0.0         0.0         0.0         0.0  \n",
       "\n",
       "[5 rows x 48190 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_rating_matrix['user_index'] = np.arange(0, final_rating_matrix.shape[0])\n",
    "final_rating_matrix.set_index(['user_index'], inplace=True)\n",
    "final_rating_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2b5db8c9-04e9-4334-a8be-ad3df150c6bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def similar_users(user_index, interactions_matrix):\n",
    "    similarity = []\n",
    "    for user in range(0, interactions_matrix.shape[0]):\n",
    "        sim = cosine_similarity([interactions_matrix.loc[user_index]], [interactions_matrix.loc[user]])\n",
    "        similarity.append((user, sim))   \n",
    "    similarity.sort(key=lambda x: x[1], reverse=True)\n",
    "    most_similar_users = [Tuple[0] for Tuple in similarity] \n",
    "    similarity_score = [Tuple[1] for Tuple in similarity]    \n",
    "    most_similar_users.remove(user_index)\n",
    "    similarity_score.remove(similarity_score[0])\n",
    "    return most_similar_users, similarity_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7d7b70b9-4a6c-4e7f-8836-da089ebac312",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[320, 12, 793, 261, 156, 1493, 1250, 567, 753, 1360]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similar = similar_users(3, final_rating_matrix)[0][0:10]\n",
    "similar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2b815f86-6e17-4052-ab03-d65de906c879",
   "metadata": {},
   "outputs": [],
   "source": [
    "def recommendations(user_index, num_of_products, interactions_matrix):\n",
    "    most_similar_users = similar_users(user_index, interactions_matrix)[0]\n",
    "    prod_ids = set(list(interactions_matrix.columns[np.where(interactions_matrix.loc[user_index] > 0)]))\n",
    "    recommendations = []\n",
    "    observed_interactions = prod_ids.copy()\n",
    "    for similar_user in most_similar_users:\n",
    "        if len(recommendations) < num_of_products:\n",
    "            similar_user_prod_ids = set(list(interactions_matrix.columns[np.where(interactions_matrix.loc[similar_user] > 0)]))\n",
    "            recommendations.extend(list(similar_user_prod_ids.difference(observed_interactions)))\n",
    "            observed_interactions = observed_interactions.union(similar_user_prod_ids)\n",
    "        else:\n",
    "            break\n",
    "    \n",
    "    return recommendations[:num_of_products]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b4e6b60b-2b21-4556-b14a-c72b944d88ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['B00CL8F98W', 'B003FMVPFY', 'B002VS4V0Y', 'B00HFAEBWG', 'B00BQ4F9ZA']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommendations(5, 5, final_rating_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "379c4714-f5e6-473f-b399-36fc62bc1bbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['B00007E7K1', 'B0020HRCB6', 'B0043D2L70', 'B001SVJUPG', 'B004603DTA']"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommendations(1400, 5, final_rating_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c775a56-6793-4996-a036-06eec28b8f4f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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